A
licenseNot graded
qualityC
maintenanceHelps AI agents search the public web and fetch content with anti-bot measures, returning clean markdown outputs suitable for citation.
2
Apache 2.0
Sofya
by co.sofya
Server Details
Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
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- Status
- Healthy
- Uptime
- 99.9% over 60 days
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-03-26
- URL
- Repository
- sofya-co/sofya-mcp
- GitHub Stars
- 2
- Server Listing
- sofya-mcp
TDQS
A4.7/5.0
Scored across 4 tools
Disambiguation4/5
Search, research, fetch, and extract each have a distinct primary role, and the descriptions explicitly call out when to use one over another. The only mild ambiguity is between fetch and extract, since both operate on URLs and return page-derived content, but their different outputs (full markdown vs. AI-extracted structured data) are clearly explained.
Naming Consistency5/5
All four tool names are single lowercase verbs that match their function directly: search, research, fetch, and extract. The pattern is completely consistent and predictable, with no mixed conventions or vague filler names.
Tool Count5/5
Four tools is a well-scoped size for a web research server. Each tool covers a distinct operation—discovery, deep synthesis, targeted fetching, and structured extraction—so none feels redundant or missing.
Completeness5/5
The tool set covers the full research lifecycle: search for sources, fetch specific URLs, extract structured data from a page, and synthesize an overall report. It includes useful parameters like freshness, domain filtering, and raw HTML access, leaving no obvious dead ends for typical web research workflows.
Available Tools
4 toolsextractInspect
Fetch a webpage and extract specific information using AI. Use this when you need one specific answer from a page (e.g. a price, a spec, contact info) rather than its full content. The answer comes back as text, not JSON. 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")
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| prompt | Yes |
fetchInspect
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. May be null for non-HTML content (PDF, DOCX, etc.) and for some sites. 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)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | ||
| include_raw_html | No |
researchInspect
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)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| topic | No | general | |
| freshness | No | ||
| max_sources | No |
searchInspect
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 (the query actually run), answer (if requested), results (array of {title, url, content, description, fetched, published_date, sublinks, table}), 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, as bare hostnames like "github.com" (max 10) exclude_domains: Exclude results from these domains, as bare hostnames (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"
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| topic | No | general | |
| freshness | No | ||
| max_results | No | ||
| search_depth | No | basic | |
| include_answer | No | ||
| exclude_domains | No | ||
| include_domains | No |
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- Changed
research1 field changed- added
Input schema / properties / topic / enumAdded value: +[ + "general", + "news" +]
- Changed
search2 fields changed- added
Input schema / properties / search_depth / enumAdded value: +[ + "snippets", + "basic" +] - added
Input schema / properties / topic / enumAdded value: +[ + "general", + "news" +]
4 tool updates
- First observed
extract - First observed
fetch - First observed
research - First observed
search
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