Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
Inferido de los transportes que declara este listado (streamable-http). Que un cliente no aparezca aquí no significa que se haya descartado: simplemente Forge no puede confirmarlo.
La verificación confirma la identidad del publicador (la propiedad del repo), no la seguridad del código. El análisis de seguridad cubre los CVE conocidos y los scripts de instalación sospechosos.
Leído de un handshake MCP real initialize → tools/list contra el endpoint declarado. No se invocó ninguna herramienta: tools/list es la llamada de introspección de solo lectura que el protocolo define para esto. Refleja lo que el servidor anunciaba en ese momento; un endpoint alojado no está fijado a ninguna versión y puede cambiar sin avisar.
https://sofya.co/mcp4 herramientas · 2019 mssearchSearch 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 5 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 8 credits, much cheaper than a full 25-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.
Args:
query: The search query
search_depth: "basic" (default) for extracted page content (3 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 5 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"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 5 credits). This is the sweet spot for most agent tasks, e.g. basic + include_answer = 8 credits, much cheaper than a full 25-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. Args: query: The search query search_depth: "basic" (default) for extracted page content (3 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 5 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"
No se publicó ningún esquema de entrada para esta herramienta.
fetchFetch 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 1 credit 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)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 1 credit 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)
No se publicó ningún esquema de entrada para esta herramienta.
extractFetch 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 5 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")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 5 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")
No se publicó ningún esquema de entrada para esta herramienta.
researchPerform 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 25 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)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 25 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)
No se publicó ningún esquema de entrada para esta herramienta.
4 de 4 herramientas publicaron una descripción.
Los nombres y descripciones de las herramientas los escribe el publicador y se muestran literalmente como texto inerte. Son las cadenas que un cliente MCP pasa al modelo, así que Forge las analiza en busca de patrones de inyección de prompts — cualquier hallazgo aparece junto al análisis de seguridad de arriba. «Privilegiada» es una coincidencia de palabra clave en el nombre de la herramienta, no una auditoría de lo que hace: un nombre inofensivo puede hacer cualquier cosa.
Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
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