scanner
AI-agent readiness of business websites: scans, city indexes, assistants' answers, direct links.
사용해야 할까요
품질 및 안전성
발견 사항 (13)
- HIGH
- MEDIUMfind_direct_places에서
- MEDIUMwhat_assistants_say에서
- MEDIUMassistant_recommendations에서
- MEDIUMassistant_destinations에서
- MEDIUMcompare_cities에서
- MEDIUMcountry_summary에서
- MEDIUMrankings에서
- MEDIUMsearch_places에서
- MEDIUMdirect_record에서
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"scanner": {
"url": "https://directindex.org/mcp"
}
}
}원격 엔드포인트
https://directindex.org/mcpstreamable-http할 수 있는 일
도구 목록
도구 (18)
⚪scan_website(url, context, llm_model, conversation_id)
Scan a business website and return its Agent-Direct Score (0 to 100), grade, findings and fixes. Reuses a scan under 24 hours old; otherwise runs one, which takes 10 to 40 seconds.
입력 스키마
{
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Website address, for example yourbusiness.com"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"url",
"context",
"llm_model"
]
}🟢get_site_report(domain, context, llm_model, conversation_id)
Return the latest Agent-Direct report for a domain that has already been scanned, including findings, fixes and agent-discoverability checks.
입력 스키마
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Registered domain, for example belcanto.pt"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"domain",
"context",
"llm_model"
]
}🟢city_leaderboard(city, limit, context, llm_model, conversation_id)
Businesses in a city index ranked by Agent-Direct Score, with grade, menu format, direct channel and whether AI crawlers are blocked.
입력 스키마
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City slug, for example lisbon"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 500,
"default": 50
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"city",
"context",
"llm_model"
]
}🟢find_direct_places(city, query, want, limit, lat, ...)
Places in a city that take a booking or an order on their own website, best Agent-Direct Scores first, with the direct booking or ordering link for each (the owner's corrected link where the business is claimed), the vendor behind it, verified_at when a browser reached the step before confirming, the vendor's terms for agents when noted, the hours, and follow links (https://directindex.org/go/{domain}/book, /order, /menu) that count. Use it to recommend places a person can book or order from directly, without a marketplace. Optional query: a cuisine, a dish or a few words, for example pizza, sushi, vegan, francesinha. Optional filters: lat, lon and radius_m for places near a point; open_now for places open at this moment in the city's zone (unknown hours stay in, marked null); verified_only for links a browser verified; vendor for one booking or ordering vendor (slug as on https://directindex.org/vendors). The same lists exist as pages at https://directindex.org/city/{slug}/direct, /direct/book, /direct/order and /direct/{want}; the full record of one place is available by domain.
입력 스키마
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City slug, for example lisbon; see list_cities"
},
"query": {
"type": "string",
"description": "What the person wants: a cuisine, a dish or a few words. Empty for any."
},
"want": {
"type": "string",
"enum": [
"book",
"order",
"any"
],
"default": "any",
"description": "book: a table; order: food to collect or deliver; any: either"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 25,
"default": 10
},
"lat": {
"type": "number",
"description": "Latitude of the person; with lon, only places within radius_m"
},
"lon": {
"type": "number",
"description": "Longitude of the person"
},
"radius_m": {
"type": "integer",
"minimum": 50,
"maximum": 50000,
"default": 1000,
"description": "Metres around lat, lon"
},
"open_now": {
"type": "boolean",
"default": false,
"description": "Only places open right now by their OpenStreetMap hours, in the city's zone; places with unreadable hours stay in with open_now null"
},
"verified_only": {
"type": "boolean",
"default": false,
"description": "Only links a browser verified: an agent reached the step before confirming"
},
"vendor": {
"type": "string",
"description": "Only places on this booking or ordering vendor, by slug: zenchef, sevenrooms, toast ..."
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"city",
"context",
"llm_model"
]
}🟢list_cities(context, llm_model, conversation_id)
List the city indexes that exist, with counts and status.
입력 스키마
{
"type": "object",
"properties": {
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"context",
"llm_model"
]
}🟢order_deep_report(domain, email, lang, context, llm_model, ...)
Order the paid deep report (5 EUR, one-off) for a business in a city index: ChatGPT, Claude and Gemini are asked ten questions about it the way people ask, every answer is kept in full with where it sent people, plus the nearest similar places, the fix plan with the kit to paste, what an agent reads on the site, a page and a PDF. Returns the order token and a Stripe checkout link for the person to pay (nothing is charged until they do); the report is written in about fifteen minutes after payment and also emailed, and its status and text can be read by the token.
입력 스키마
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "The business's registered domain, for example belcanto.pt; it must be in a city index (see city_leaderboard)"
},
"email": {
"type": "string",
"description": "Where the report and the receipt go"
},
"lang": {
"type": "string",
"description": "Language of the delivery mail: en, pt, es, fr, de, nl, it, da or cs",
"default": "en"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"domain",
"email",
"context",
"llm_model"
]
}🟢get_deep_report(order, context, llm_model, conversation_id)
Status of a deep report order by its token, and the finished report itself as Markdown once it is done (every answer in full, the table of where answers sent people, the nearest similar places, the fix plan and the kit).
입력 스키마
{
"type": "object",
"properties": {
"order": {
"type": "string",
"description": "The 32-character order token from order_deep_report"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"order",
"context",
"llm_model"
]
}🟢what_assistants_say(domain, context, llm_model, conversation_id)
What ChatGPT, Claude and Gemini answer about one business: the latest verdict per question (official website, where to book, where to order: own site, vendor, marketplace, directory, no link), the answers quoted, the questions people ask for which the business was named, and one sentence on why an assistant sent people elsewhere. For businesses in a city index that have been asked about; see the report at https://directindex.org/r/{domain}.
입력 스키마
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Registered domain, for example belcanto.pt"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"domain",
"context",
"llm_model"
]
}🟢assistant_recommendations(city, intent, context, llm_model, conversation_id)
The places ChatGPT, Claude and Gemini name in a city when asked the way people ask ('best pizza delivered right now', 'book me a table for Saturday'): per question, the places named, which assistant named each, whether with a link to the place's own site, and the direct booking or ordering link where the place has one. Pages: https://directindex.org/city/{slug}/recommended and /recommended/{question}.
입력 스키마
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City slug, for example lisbon; see list_cities"
},
"intent": {
"type": "string",
"description": "A question key to narrow to one question, for example pizza-delivery, romantic, fado; empty for all"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"city",
"context",
"llm_model"
]
}🟢assistant_destinations(city, limit, context, llm_model, conversation_id)
Where the assistants send people in a city: the league table of destinations (the businesses' own sites, each marketplace and directory by name, press and guides, other sites) with the share of answers that link each one, per assistant and overall, and the headline shares (direct, marketplace, the top marketplace). Pages: https://directindex.org/city/{slug}/assistants and https://directindex.org/assistants for every city.
입력 스키마
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City slug, for example lisbon; 'all' for every city together"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 200,
"default": 25,
"description": "How many destinations"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"city",
"context",
"llm_model"
]
}🟢compare_cities(a, b, kind, context, llm_model, ...)
Two city indexes side by side: business websites scanned, the share that take a booking or an order on their own site, the share that turn an AI crawler away, the share scoring A or B, grades, menus, channels, builders, vendors, the assistants' direct share where probed, and the month-by-month numbers. Takes index slugs or city names (lisbon, Porto, berlin-cafes-bars). Page: https://directindex.org/compare/{a}/{b}.
입력 스키마
{
"type": "object",
"properties": {
"a": {
"type": "string",
"description": "First city or index slug, for example lisbon"
},
"b": {
"type": "string",
"description": "Second city or index slug, for example porto"
},
"kind": {
"type": "string",
"enum": [
"restaurants",
"cafes",
"salons",
"clinics",
"tours"
],
"default": "restaurants",
"description": "Which kind of index when a city name is given"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"a",
"b",
"context",
"llm_model"
]
}🟢country_summary(code, context, llm_model, conversation_id)
A country's indexes added together (ISO code, for example PT or DE): sites scanned, direct share, crawler-blocked share, A-or-B share, the cities with their numbers, the kinds, the vendors common in the country. Without a code: every country with an index. Pages: https://directindex.org/country/{code} and /countries.
입력 스키마
{
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Two-letter country code, for example PT; empty for the list of countries"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"context",
"llm_model"
]
}🟢rankings(kind, context, llm_model, conversation_id)
Every city index of a kind ranked on three measures with its sample size and date: direct share (a booking or ordering path on the business's own domain), crawler-open share (not turned away at the edge) and A-or-B share. Indexes with fewer than 100 scanned sites are listed unranked. Page: https://directindex.org/rankings?kind=.
입력 스키마
{
"type": "object",
"properties": {
"kind": {
"type": "string",
"enum": [
"restaurants",
"cafes",
"salons",
"clinics",
"tours",
"all"
],
"default": "restaurants",
"description": "restaurants, cafes, salons, clinics, tours, or all (whole cities, every kind added)"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"context",
"llm_model"
]
}🟢search_places(q, city, kind, grade, builder, ...)
Find businesses in the city indexes by name or domain, filtered by city, kind, grade, builder, booking or ordering vendor and finding (for example menu_pdf, edge_blocks, no_structured, booking_marketplace). Fifty results a page, best Agent-Direct Scores first, each with its report link. Free, no contacts. Page: https://directindex.org/search.
입력 스키마
{
"type": "object",
"properties": {
"q": {
"type": "string",
"description": "A name, part of a name, or a domain"
},
"city": {
"type": "string",
"description": "City or index slug, for example berlin or berlin-cafes-bars"
},
"kind": {
"type": "string",
"enum": [
"restaurants",
"cafes",
"salons",
"clinics",
"tours"
]
},
"grade": {
"type": "string",
"enum": [
"A",
"B",
"C",
"D",
"F"
]
},
"builder": {
"type": "string",
"description": "Builder slug, for example wordpress, wix, squarespace"
},
"vendor": {
"type": "string",
"description": "Vendor slug, for example thefork, zenchef, toast"
},
"finding": {
"type": "string",
"description": "Finding id, for example menu_pdf, edge_blocks, robots_blocks, no_structured, no_text_nojs, no_channel, booking_marketplace, ordering_marketplace, no_llms_txt"
},
"page": {
"type": "integer",
"minimum": 1,
"default": 1
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"context",
"llm_model"
]
}🟢direct_record(domain, context, llm_model, conversation_id)
The direct record of a business: the booking, ordering and menu links an agent should use instead of a marketplace, where each leads (the business's own site or a named vendor), whether and when a browser verified it, the hours (OpenStreetMap form and schema.org, with open_now), phone, address, the assistants' latest verdicts, how often agents read and followed it this month, the score, and the source and date of every fact. Corrected by the owner where the business is claimed. Also GET https://directindex.org/api/direct/{domain}.
입력 스키마
{
"type": "object",
"properties": {
"domain": {
"type": "string",
"description": "Registered domain, for example belcanto.pt"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"domain",
"context",
"llm_model"
]
}🟢vendor_report(slug, context, llm_model, conversation_id)
A booking or ordering vendor across the index: software on the vendor's domain or a marketplace, booking or ordering, how many sites use it, their average score and A or B share, how the browser action test went on them, the cities, example sites, and the vendor's terms for agents when a note exists. Vendor slugs as on https://directindex.org/vendors (zenchef, sevenrooms, thefork, toast ...).
입력 스키마
{
"type": "object",
"properties": {
"slug": {
"type": "string",
"description": "Vendor slug, for example zenchef"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"slug",
"context",
"llm_model"
]
}🟢city_changes(city, days, context, llm_model, conversation_id)
What changed in a city index over the last days: findings fixed or new, grades up or down, AI crawlers turned away or let in, direct booking or ordering channels and vendors gained or lost, and assistants that changed their answer about a business. Each change links the business's report. Also a page (https://directindex.org/city/{slug}/changes), an Atom feed (/changes.xml) and JSON (/api/city/{slug}/changes?days=30).
입력 스키마
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City slug, for example lisbon; see list_cities"
},
"days": {
"type": "integer",
"minimum": 1,
"maximum": 365,
"default": 30
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"city",
"context",
"llm_model"
]
}🟢vendor_terms(vendor, context, llm_model, conversation_id)
Whether a booking or ordering vendor's terms let an AI agent book or order through it: allowed, partners only, banned, or not stated; with the vendor's own page and the date the terms were read. Without a vendor: every researched vendor. Use it before sending a person to book or order through a vendor on an agent's behalf.
입력 스키마
{
"type": "object",
"properties": {
"vendor": {
"type": "string",
"description": "Vendor slug as it appears in reports, for example thefork, opentable, square"
},
"context": {
"type": "string",
"description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
},
"llm_model": {
"type": "string",
"description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
},
"conversation_id": {
"type": "string",
"description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
}
},
"required": [
"context",
"llm_model"
]
}커뮤니티
증거