Sofya
Server Details
Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.7/5 across 4 of 4 tools scored.
Each tool has a clearly distinct purpose: search for web searching, fetch for raw page content, extract for specific information from a page, and research for comprehensive synthesis. No overlap.
All tool names are single-word imperative verbs (extract, fetch, research, search), forming a consistent and predictable pattern.
With 4 tools, the server is well-scoped for its purpose of web information retrieval. Each tool covers a distinct need without excess or deficiency.
The tool set covers the full spectrum of web information access: searching, fetching raw content, extracting structured data, and synthesizing research reports. No obvious gaps for a read-only information retrieval server.
Available Tools
4 toolsextractAInspect
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.
Returns: content (the extracted text), url, credits_used, credits_remaining, usage (token counts).
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")
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| prompt | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the credit cost (5 credits) and the return fields (content, url, credits_used, credits_remaining, usage). No annotations are provided, so the description carries the burden. It does not cover failure modes or rate limits, but the cost and output structure are transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, using a few sentences to cover purpose, use case, cost, return, and args. It is well-structured and front-loaded with the main purpose, with zero unnecessary content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description adequately explains the return fields. It covers the two required parameters and the credit cost. However, it could be improved by mentioning error handling or timeouts, but overall it is relatively complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no descriptions and 0% coverage. The description fully compensates by explaining each parameter: 'url: The URL to extract from' and 'prompt: What information to extract' with concrete examples. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches a webpage and extracts specific information using AI. It distinguishes itself from sibling tools like 'fetch' by specifying it's for structured data (pricing, specs, contact info) rather than raw content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use this tool: 'when you need structured data from a page rather than the raw content.' It gives examples, but does not explicitly state when not to use or provide alternatives beyond the implicit differentiation from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetchAInspect
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)
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | ||
| include_raw_html | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden. It discloses cost (1 credit per URL), maximum URLs (10), that failed URLs are not charged, the optional raw HTML inclusion, its behavior on non-HTML content, and the exact return structure. This is comprehensive for a fetch operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with separate paragraphs for when to use, optional behavior, and output. Every sentence adds value, though it could be slightly more concise (e.g., merging some sentences). It front-loads the core purpose and then provides details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description provides a detailed return structure including fields (title, url, content, raw_html, etc.) and metadata (credits_used, credits_remaining). It covers input validation, cost, error handling, and optional behavior. For a simple tool with two parameters, this is complete and leaves no ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0%, but the description thoroughly explains both parameters: 'urls' (list, max 10) and 'include_raw_html' (optional, default false, and its effect). It adds meaning beyond the schema's type and title fields, making the parameters fully understandable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it fetches URLs and returns clean markdown content. It distinguishes from sibling tools like 'search' by emphasizing it provides complete text rather than snippets, and mentions support for PDF/DOCX. The verb 'fetch' and resource 'URLs' are specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool (reading articles, documentation, etc.) and implicitly contrasts with search. It also provides practical details like max URLs, cost, and error handling that help the agent decide. However, it does not explicitly mention when not to use it or name alternatives like 'extract' or 'research' for different tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
researchAInspect
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)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| topic | No | general | |
| freshness | No | ||
| max_sources | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the inner workings (decompose, parallel search, synthesis with citations) and return types. Without annotations, it covers key behavioral aspects. Lacks mention of error handling or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose, when to use, returns, args. Slightly verbose but efficient; each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all necessary aspects: purpose, usage, parameters, and return format (including fields like sources, sub_queries). No output schema, so description compensates fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds meaning beyond the input schema: clarifies query as 'research question or topic', explains topic options (general/news), freshness as recency filter, and max_sources range (5-30). Schema has 0% description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Perform comprehensive research on a topic' and explains the process of decomposing queries, searching multiple sources, and synthesizing a report. It distinguishes from sibling tools by explicitly contrasting with 'search' for simple lookups.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: 'Best for open-ended or comparative questions' and directs to 'use search instead' for factual lookups. Also mentions cost (25 credits) as a consideration.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspect
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"
| 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 Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that search returns extracted page content (not just snippets), explains credit costs for different search depths and include_answer, and lists all returned fields. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with a clear two-sentence introduction followed by detailed parameter explanations. However, it is lengthy due to the number of parameters and examples. Front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 params, no output schema, no annotations), the description fully covers purpose, usage guidelines, parameter details, return fields, and credit costs. An agent can correctly invoke this tool without additional information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explains every parameter in detail: query, search_depth, max_results, include_answer, include_domains, exclude_domains, topic, freshness. This adds significant meaning beyond the schema's title and default values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search the web for current information on any topic.' It uses a specific verb (search) and resource (web), and differentiates from sibling tools like 'research' for synthesis, and implicitly from 'extract' and 'fetch'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use: 'Best for factual lookups, specific questions, or when you need a list of sources.' Also provides when-not: 'For open-ended questions that need synthesis across many sources, use the research tool instead.' Includes guidance for news queries with topic='news'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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