Fenlight
Google search results, forum threads, local and shopping data, keyword ideas, AI answer tracking.
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (2)
- HIGHTool poisoning patterns detected
- INFOTool description contains placeholder or incomplete textin find_discussion_candidates
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"fenlight": {
"url": "https://fenlight.waggle.ae/mcp"
}
}
}Remote-Endpunkte
https://fenlight.waggle.ae/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (8)
🟢google_serp(query, location, gl, hl, depth, ...)
Execute a full Google SERP search query with strict Google provenance (depth 10 to 100). Returns verified Google organic results with classification tagging (forums, video, ecommerce) and the results page's features (AI Overview, PAA, Knowledge Graph). The local pack and shopping are add-ons: set local_pack and/or shopping to true to include them. A result searched in the last 24 hours is served from the cache at a lower price; set fresh to true to search live.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"location": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Location"
},
"gl": {
"default": "us",
"title": "Gl",
"type": "string"
},
"hl": {
"default": "en",
"title": "Hl",
"type": "string"
},
"depth": {
"default": 10,
"title": "Depth",
"type": "integer"
},
"local_pack": {
"default": false,
"title": "Local Pack",
"type": "boolean"
},
"shopping": {
"default": false,
"title": "Shopping",
"type": "boolean"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"query"
],
"title": "google_serpArguments"
}🟢find_discussion_candidates(query, location, gl, hl, depth, ...)
Search Google and filter organic results (positions 1-100) specifically for community discussions and forum threads (Reddit, Quora, HackerNews, StackExchange, niche forums). Ideal for sentiment analysis, user discussions, and customer problem discovery.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"location": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Location"
},
"gl": {
"default": "us",
"title": "Gl",
"type": "string"
},
"hl": {
"default": "en",
"title": "Hl",
"type": "string"
},
"depth": {
"default": 30,
"title": "Depth",
"type": "integer"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"query"
],
"title": "find_discussion_candidatesArguments"
}🟢extract_local_places(query, location, gl, hl, fresh)
Extract Google Local Pack (3-pack) and Google Places listings for local queries. Returns place_id/cid, title, rating, review count, address, latitude/longitude, category, phone, and website.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"location": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Location"
},
"gl": {
"default": "us",
"title": "Gl",
"type": "string"
},
"hl": {
"default": "en",
"title": "Hl",
"type": "string"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"query"
],
"title": "extract_local_placesArguments"
}🟢extract_shopping_results(query, location, gl, hl, fresh)
Extract Google Shopping carousel listings for e-commerce and product queries. Returns product title, price, currency, merchant, rating, reviews, image URL, link, and product ID.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"location": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Location"
},
"gl": {
"default": "us",
"title": "Gl",
"type": "string"
},
"hl": {
"default": "en",
"title": "Hl",
"type": "string"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"query"
],
"title": "extract_shopping_resultsArguments"
}🟢geo_citation_radar(query, brand, domain, location, gl, ...)
Run GEO Citation & Injection Radar for a topic query and brand. Calculates citation probability score (0-100), identifies high-impact Reddit/Quora threads with injection playbooks, extracts listicle PR outreach targets with Vera contacts payload, discovers AEO question gaps from PAA, and produces an LLM-ready markdown injection brief.
Eingabe-Schema
{
"type": "object",
"properties": {
"query": {
"title": "Query",
"type": "string"
},
"brand": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Brand"
},
"domain": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Domain"
},
"location": {
"default": "Dubai",
"title": "Location",
"type": "string"
},
"gl": {
"default": "ae",
"title": "Gl",
"type": "string"
},
"hl": {
"default": "en",
"title": "Hl",
"type": "string"
},
"depth": {
"default": 30,
"title": "Depth",
"type": "integer"
},
"engine": {
"default": "google",
"title": "Engine",
"type": "string"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"query"
],
"title": "geo_citation_radarArguments"
}🟢company_serp_footprint(company, domain, location, gl, hl, ...)
Audit a company's SERP digital footprint, brand reputation, owned domains, and entity profiles. Separates owned web properties, social/entity profiles (Wikipedia, LinkedIn, Instagram, Crunchbase), Google Maps locations, third-party mentions, and customer brand questions (PAA).
Eingabe-Schema
{
"type": "object",
"properties": {
"company": {
"title": "Company",
"type": "string"
},
"domain": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Domain"
},
"location": {
"default": "Dubai",
"title": "Location",
"type": "string"
},
"gl": {
"default": "ae",
"title": "Gl",
"type": "string"
},
"hl": {
"default": "en",
"title": "Hl",
"type": "string"
},
"depth": {
"default": 20,
"title": "Depth",
"type": "integer"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"company"
],
"title": "company_serp_footprintArguments"
}🟢keyword_ideas(seed, gl, hl, location, location_code, ...)
Keyword ideas for a seed keyword from Google autocomplete (the seed plus question words or the alphabet), People Also Ask and related searches, cleaned and de-duplicated. Each idea carries its sources and an intent label (question, comparison, commercial, local, informational). Works in Arabic. Ideas only: no search volumes. Charged for the results page (3 credits live, 1 from your cache) plus 3 credits for each autocomplete call that returns suggestions, so at most 6 credits for expand 'none', 30 for 'questions' (the default), 84 for 'alphabet' (90 for an Arabic seed) and 93 for 'all'; it stops early once `limit` ideas are found, which is 6. Set fresh to true to search the results page live; autocomplete is always live.
Eingabe-Schema
{
"type": "object",
"properties": {
"seed": {
"title": "Seed",
"type": "string"
},
"gl": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Gl"
},
"hl": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Hl"
},
"location": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Location"
},
"location_code": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"title": "Location Code"
},
"limit": {
"default": 50,
"title": "Limit",
"type": "integer"
},
"expand": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Expand"
},
"fresh": {
"default": false,
"title": "Fresh",
"type": "boolean"
}
},
"required": [
"seed"
],
"title": "keyword_ideasArguments"
}🟢ai_answers(prompt, brand, brand_domain, competitors, country, ...)
Put a buyer's question to an AI assistant that searches the live web, and see whether a brand and its competitors are named in the answer, in what rank, with the citations and cited domains behind it. The assistant is ChatGPT unless `engine` names claude, perplexity or gemini; an assistant the server has no key for answers not_configured and costs nothing. `samples` (1 to 5, default 1) asks the same question more than once, for a mention rate. Returns metrics per assistant, not the raw answers. Charged per call to the assistant at the price list in credits (see llms.txt), the same as POST /v1/ai/answers: the worst case is held first, only the calls made are charged, and a call where the assistant failed is charged nothing.
Eingabe-Schema
{
"type": "object",
"properties": {
"prompt": {
"description": "The question to put to the assistant, as a buyer would ask it",
"title": "Prompt",
"type": "string"
},
"brand": {
"description": "Brand name to look for in the answer",
"title": "Brand",
"type": "string"
},
"brand_domain": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "The brand's domain, for example roasters.ae",
"title": "Brand Domain"
},
"competitors": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Competitor names or domains to track",
"title": "Competitors"
},
"country": {
"default": "ae",
"description": "Two-letter ISO country code of the market",
"title": "Country",
"type": "string"
},
"language": {
"default": "en",
"description": "Language tag of the market, for example en or ar",
"title": "Language",
"type": "string"
},
"samples": {
"default": 1,
"description": "Times the question is asked of the assistant",
"maximum": 5,
"minimum": 1,
"title": "Samples",
"type": "integer"
},
"engine": {
"default": "chatgpt",
"description": "The assistant to ask",
"enum": [
"chatgpt",
"claude",
"perplexity",
"gemini"
],
"title": "Engine",
"type": "string"
}
},
"required": [
"prompt",
"brand"
],
"title": "ai_answersArguments"
}Community
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