Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
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Average 4.8/5 across 4 of 4 tools scored.
Each tool serves a clearly distinct purpose: search for web queries, fetch for raw page content, extract for structured data extraction, and research for comprehensive synthesis. Descriptions are detailed enough to prevent confusion.
All tool names are single lowercase verbs (extract, fetch, research, search), following a consistent and predictable pattern. No mixing of styles or non-standard conventions.
With 4 tools, the set covers the core needs of web information retrieval and research without being bloated. Each tool earns its place, and the count is ideal for the domain.
The tool surface provides a complete workflow: search to find sources, fetch to retrieve full content, extract to pull specific data, and research to synthesize multiple sources. No obvious gaps for typical agent tasks.
extractAInspectFetch 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")
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| prompt | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses credit cost, behavior on empty pages (low_content flag), and advises agents to check usage.low_content. Also documents return fields including edge cases.
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 separate sections for guidance, behavior, returns, and parameter details. Every sentence is informative and no waste.
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?
Despite no output schema, the description lists all return fields and usage metrics. Covers edge cases (empty pages) and advises on handling. Complete for a 2-parameter tool.
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?
With 0% schema description coverage, the description fully compensates by explaining the 'url' and 'prompt' parameters, including what to prompt for (e.g., 'list all pricing tiers'). Adds meaning beyond the bare 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?
Clearly states it fetches a webpage and extracts structured data using AI. Provides specific use case examples (pricing, specs, contact info) and distinguishes from raw content retrieval, which contrasts with the sibling '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?
Specifies when to use: for structured data instead of raw content. However, it does not explicitly list when not to use or directly name alternatives like 'fetch', 'research', or 'search'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetchAInspectFetch 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, discloses cost per URL, max 10, failure policy, optional raw HTML, and supports PDF/DOCX. Missing behavior on redirects or authentication but sufficient.
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?
Efficient, well-organized: purpose first, then usage rules, options, and return structure. No wasted words.
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?
Despite no output schema, specifies return fields (title, url, content, etc.), input details, costs, and limitations. Fully adequate for the tool's simplicity.
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 coverage 0%, but description explains urls (max 10), include_raw_html (default false, meaning, and behavior for non-HTML). Adds value beyond bare 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?
States 'Fetch one or more URLs and return their content as clean markdown' – specific verb+resource+output. Contrasts with search and other siblings, clearly differentiating.
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?
Explicitly says when to use (read articles, docs) and contrasts with search. Mentions limits and credit cost. Could be improved by stating when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
researchAInspectPerform 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: decomposes into sub-queries, searches and reads multiple sources in parallel, synthesizes a structured report with citations. Also details the return structure including credits consumed.
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 with no wasted words, front-loading the main purpose then providing essential details in a structured manner (best use, cost, returns, args). Every sentence earns its place.
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 fully explains the return structure (query, report, sources array with fields, sub_queries, credits, usage counts). It covers cost, alternative tools, parameter details, and behavior, leaving no gaps for an AI agent.
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 description explains each parameter (query, topic, freshness, max_sources) with their purpose and defaults, adding significant meaning beyond the schema which lacks descriptions. The schema coverage being 0% makes this contribution critical.
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 performs comprehensive research, decomposes queries, searches multiple sources, and synthesizes a report. It distinguishes from sibling tool 'search' for simple lookups, making the purpose specific and unambiguous.
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?
Explicitly says 'Best for open-ended or comparative questions' and directs users to 'use search instead' for simple facts. Also mentions cost of 25 credits, providing clear guidance on when to use this tool vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchAInspectSearch 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It reveals that the tool returns extracted page content (not just snippets) and clearly explains credit costs per search_depth and include_answer. However, it does not mention rate limits or authentication, but these are less critical for a search tool.
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 and front-loaded core purpose. While slightly long, every sentence adds value, and the organization aids readability. A minor reduction for potential conciseness.
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 8 parameters, no output schema, no annotations, and sibling tools, the description is highly complete. It describes return format, credits, altered_query, and provides all necessary context for an agent to use the tool correctly.
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 description thoroughly explains all 8 parameters: query, search_depth, max_results, include_answer, include_domains, exclude_domains, topic, freshness. It provides defaults, usage advice, and credit costs, adding significant value 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?
Description clearly states 'Search the web for current information on any topic' and distinguishes from siblings (research tool) by specifying when to use each. It contrasts with extract, fetch, and research by saying it's best for factual lookups and specific questions.
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 when-to-use (factual lookups, specific questions, list of sources) and when-not-to-use (open-ended questions needing synthesis, use research tool). Also offers specific advice for news queries (set topic='news') and cost optimization (include_answer).
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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