Sofya
Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
我該用這個嗎
品質與安全性
根據工具定義與協定合規性的自動化分析。
上下文成本
這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。
安裝
一鍵安裝
將以下內容加入你的 `claude_desktop_config.json` 檔案:
{
"mcpServers": {
"sofya": {
"url": "https://sofya.co/mcp"
}
}
}遠端端點
https://sofya.co/mcpstreamable-http它能做什麼
工具清單
工具(4)
🟡search(query, search_depth, max_results, include_answer, include_domains, ...)
Search the web for current information on any topic. Returns extracted page content, not just snippets. Best for factual lookups, specific questions, or when you need a list of sources. For open-ended questions that need synthesis across many sources, use the research tool instead. For news queries (current events, breaking news, politics, world events), set topic="news" to search news sources specifically. This returns recent articles with publication dates. Set include_answer=true to get an AI-synthesized answer alongside results (adds 10 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 12 credits, much cheaper than a full 50-credit research call. Returns: query, answer (if requested), results (array of {title, url, content, description, fetched, published_date}), search_depth, topic, elapsed_ms, credits_used, credits_remaining, altered_query, relaxed_query (set when the query matched nothing and was retried once with its site: operator, else its quotes, removed - the results answer that looser query). Args: query: The search query search_depth: "basic" (default) for extracted page content (2 credits), "snippets" for SERP snippets only without page fetching (1 credit) max_results: Number of results (default 10, max 20) include_answer: Generate an AI answer that synthesizes the search results (adds 10 credits) include_domains: Only include results from these domains (max 10) exclude_domains: Exclude results from these domains (max 10) topic: "general" for web search, "news" for news articles. use "news" for current events, breaking news, politics, or any time-sensitive query freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD"
輸入結構描述
{
"type": "object",
"properties": {
"query": {
"type": "string",
"title": "Query"
},
"search_depth": {
"type": "string",
"default": "basic",
"title": "Search Depth"
},
"max_results": {
"type": "integer",
"default": 10,
"title": "Max Results"
},
"include_answer": {
"type": "boolean",
"default": false,
"title": "Include Answer"
},
"include_domains": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"default": null,
"title": "Include Domains"
},
"exclude_domains": {
"anyOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "null"
}
],
"default": null,
"title": "Exclude Domains"
},
"topic": {
"type": "string",
"default": "general",
"title": "Topic"
},
"freshness": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Freshness"
}
},
"required": [
"query"
]
}🟡fetch(urls, include_raw_html)
Fetch one or more URLs and return their content as clean markdown. Use this to read articles, documentation, blog posts, or any page where you need the complete text, not just a snippet from search. Also supports PDF, DOCX, and other document formats. Costs 2 credits per URL. Max 10 URLs per request. Failed URLs are not charged. Set include_raw_html=true to also get the raw HTML source in each result. Useful for inspecting embedded URLs, data attributes, iframes, or script tags that are stripped during markdown conversion. Returns null for non-HTML content (PDF, DOCX, etc.). Same cost. Returns: results (array of {title, url, content, raw_html, published_time, success, error}), credits_used, credits_remaining. Args: urls: List of URLs to fetch (max 10) include_raw_html: Include raw HTML source in each result (default false)
輸入結構描述
{
"type": "object",
"properties": {
"urls": {
"type": "array",
"items": {
"type": "string"
},
"maxItems": 10,
"title": "Urls"
},
"include_raw_html": {
"type": "boolean",
"title": "Include Raw Html",
"default": false
}
},
"required": [
"urls"
]
}🟢extract(url, prompt)
Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 10 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")
輸入結構描述
{
"type": "object",
"properties": {
"url": {
"type": "string",
"title": "Url"
},
"prompt": {
"type": "string",
"title": "Prompt"
}
},
"required": [
"url",
"prompt"
]
}🟢research(query, topic, freshness, max_sources)
Perform comprehensive research on a topic. Decomposes your query into sub-queries, searches and reads multiple sources in parallel, then synthesizes a structured report with citations. Best for open-ended or comparative questions that need coverage from many angles. For simple factual lookups, use search instead (optionally with include_answer=true for cheap synthesis). Costs 50 credits. Returns: query, report (structured markdown with citations), sources (array of {title, url, fetched}), sub_queries (the decomposed queries), credits_used, credits_remaining, usage (token counts). Args: query: The research question or topic topic: "general" (default) or "news" (prioritize recent news articles) freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD" max_sources: Maximum number of sources to use, 5-30 (default 20)
輸入結構描述
{
"type": "object",
"properties": {
"query": {
"type": "string",
"title": "Query"
},
"topic": {
"type": "string",
"default": "general",
"title": "Topic"
},
"freshness": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Freshness"
},
"max_sources": {
"type": "integer",
"default": 20,
"title": "Max Sources"
}
},
"required": [
"query"
]
}社群
證據