webclaw

by 0xMassi

Overview Schema Related Servers Score Discussions

Rust

Hybrid

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but there is some potential overlap between 'extract' (structured data via LLM) and 'scrape' (content extraction in various formats), which could cause confusion. Other tools like 'brand', 'diff', 'map', and 'summarize' are clearly specialized, helping to minimize misselection.
Naming Consistency5/5
All tool names follow a consistent pattern of single, descriptive verbs (e.g., batch, brand, crawl, diff, extract, map, research, scrape, search, summarize). There are no mixed conventions or deviations, making the set predictable and easy to navigate.
Tool Count5/5
With 10 tools, the count is well-scoped for a web scraping and research server. Each tool appears to serve a specific function in the domain, from basic scraping to advanced research, without feeling excessive or insufficient for the intended purpose.
Completeness4/5
The toolset covers a broad range of web-related operations, including extraction, crawling, searching, and analysis. Minor gaps exist, such as the lack of explicit update or delete operations for stored data, but these are not critical for the server's primary focus on content retrieval and processing.
Average 3.6/5 across 10 of 10 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 25 of 26 community issues answered or closed in the last 6 months
- 105 commits in the last 12 weeks
- Last stable release on August 16, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under AGPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
View related servers

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full behavioral disclosure. It successfully mentions 'concurrently' indicating parallel execution, but lacks critical safety information for a network tool: error handling behavior (what happens if one URL fails?), timeout behavior, rate limiting, or whether results preserve input ordering.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely efficient at 6 words. Front-loaded with action verb ('Extract') and scope ('multiple URLs concurrently'). Zero redundancy or filler content. Every word earns its place without requiring editing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 100% input schema coverage, the description need not elaborate on parameters. However, lacking both annotations and output schema, the description should disclose error behavior or return structure for this concurrent network operation. It is minimal but adequate given the straightforward nature of the operation and good schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (concurrency, format, urls all documented), establishing baseline 3. The description implies the 'urls' parameter through 'multiple URLs' but adds no semantic detail beyond what the schema already provides for parameters (no syntax examples, no clarification of 'llm' format value, no default explanations).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Extract' and resource 'content from multiple URLs', and includes 'concurrently' which hints at the batch nature. However, it does not explicitly distinguish this from the sibling 'extract' tool (likely single-URL) or 'crawl' (which may also handle multiple URLs), leaving some ambiguity about when to choose this specific tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'extract', 'scrape', or 'crawl'. It does not mention prerequisites, rate limiting considerations, or appropriate use cases for concurrent processing versus sequential operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the algorithm (breadth-first) and constraints (depth/page limits), but omits critical operational details: that it makes external HTTP requests, potential rate-limiting concerns, concurrency behavior implications, and what data structure it returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the operation ('Crawl a website') and logically flows from seed URL through traversal method to constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, network I/O, potentially long-running) and the absence of annotations or output schema, the description is minimally adequate but omits important context. It fails to describe return values, error conditions, or external side effects that an agent would need to know.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing a baseline of 3. The description mentions 'configurable depth and page limit' and 'seed URL', which reference parameters, but adds no additional semantic context, syntax guidance, or usage patterns beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description provides a clear specific verb ('Crawl') and resource ('website'), and explains the core mechanism ('following links breadth-first'). However, it does not explicitly differentiate from sibling tools like 'scrape' or 'map' that might overlap in functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use 'crawl' versus siblings such as 'scrape', 'extract', or 'map'. There are no exclusion criteria or stated prerequisites for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It successfully discloses the WEBCLAW_API_KEY requirement and mentions 'structured results', but lacks safety profile disclosure (read-only vs destructive), rate limits, or specific result format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences with no waste. Front-loaded with action ('Search the web'), followed by output description and auth requirement. Every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a simple 2-parameter tool with no output schema, covering purpose, auth, and return type. However, gaps remain in distinguishing from functionally similar siblings and detailing what 'structured results' specifically means.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with clear descriptions for both 'query' and 'num_results'. Description adds minimal parameter-specific semantics beyond the schema, but baseline 3 is appropriate given comprehensive schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb ('Search') and resource ('the web') with output format specified ('structured results'). However, given siblings like 'crawl', 'scrape', and 'research', it could better differentiate this web search capability from those related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no guidance on when to use this tool versus siblings like 'research', 'crawl', or 'scrape', nor does it mention prerequisites beyond the API key requirement. No 'when-not-to-use' guidance present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses async execution model (starts job, polls) and authentication requirement, which is valuable given no annotations. However, missing critical behavioral details: no description of return format (text? JSON? report?), no mention of rate limits, cost implications, or error states for the cloud API operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste: purpose front-loaded, prerequisites stated, execution model explained. No redundancy with structured fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for basic invocation but gaps remain: no output schema exists yet description doesn't specify return format (critical for a research tool), and lacks guidance on expected duration or result structure given the async polling pattern described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage, establishing baseline 3. Description reinforces parameters by mentioning 'deep research' (deep), 'topic' (topic), and 'question' (query), but adds no syntax details, examples, or constraints beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb+resource ('Run a deep research investigation') and distinguishes from siblings like 'search' by emphasizing 'deep' investigation and async execution ('polls until complete'). However, could more explicitly contrast with 'search' or 'summarize' siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides prerequisite ('Requires WEBCLAW_API_KEY') and implies usage context through 'deep' and async behavior, but lacks explicit when-to-use guidance versus alternatives like 'search' for quick lookups or 'scrape' for specific extraction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It adds valuable behavioral context about the automatic fallback to webclaw cloud API when bot protection is detected, which is critical for dependency understanding. However, it omits other important behavioral details like rate limits, authentication requirements, or what happens when extraction partially fails.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficiently structured sentences with zero waste. The first front-loads the core purpose with specific deliverables, while the second adds critical operational context (fallback behavior). Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with full schema coverage and no output schema, the description adequately covers the primary unusual behavior (external API fallback). It could be improved by briefly mentioning the expected return structure or noting that this is a read-only operation, but the essential functional and behavioral context is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 100% schema coverage (baseline 3), the description adds meaningful semantic context by specifying extraction occurs from 'HTML and CSS', implying the URL should point to a styled webpage rather than arbitrary resources. This adds clarity beyond the schema's basic 'URL to extract brand identity from'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific extraction target (colors, fonts, logo, favicon) using the verb 'Extract', making the scope well-defined. However, it lacks explicit differentiation from the sibling 'extract' tool, which could cause confusion about when to choose 'brand' over general extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implicit guidance by specifying the domain (brand identity extraction) and mentions automatic fallback behavior when bot protection is detected. However, it lacks explicit 'when to use' guidance contrasting with siblings like 'extract', 'scrape', or 'crawl'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure and successfully reveals the automatic fallback to 'webclaw cloud API when bot protection is detected.' However, it omits other critical behavioral traits such as whether the operation is read-only, idempotent, or what error conditions might occur beyond bot protection.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of exactly two sentences with zero wasted words. The first sentence front-loads the core functionality, while the second provides relevant operational context regarding the fallback mechanism, maintaining tight focus throughout.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (comparison logic with fallback) and the absence of an output schema, the description adequately covers the input requirements and operational model. However, it would benefit from explicitly stating the prerequisite relationship to the 'extract' sibling and describing the expected diff output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage for both parameters ('url' and 'previous_snapshot'), establishing a baseline of 3. The description references these parameters in context ('current content of a URL,' 'previous extraction snapshot') but does not add additional semantic details beyond what the schema already provides, such as validation rules or the expected format of the ExtractionResult.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Compare') and clearly identifies both resources involved ('current content of a URL' vs 'previous extraction snapshot'), along with the output ('showing what changed'). It effectively distinguishes this from siblings like 'extract' or 'scrape' by specifying the comparison paradigm.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The mention of 'previous extraction snapshot' implicitly signals that this tool should be used after an extraction has been performed, providing context for the prerequisite workflow. However, it lacks explicit guidance on when to choose this over siblings like 'crawl' or 'scrape' for monitoring changes, or what conditions make the fallback behavior trigger.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It successfully discloses the automatic fallback to 'webclaw cloud API when bot protection is detected,' which is substantive behavioral context. However, it omits rate limits, auth requirements, and output format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tightly constructed sentences: purpose declaration, input guidance, and fallback behavior. Every sentence earns its place with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 9 sibling tools sharing similar domains ('scrape', 'crawl', 'summarize'), the description lacks explicit differentiation to aid tool selection. While the core functionality is covered, the absence of output schema disclosure and sibling comparisons leaves gaps for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing a baseline of 3. The description adds value by clarifying the relationship between 'schema' and 'prompt' parameters ('either... or'), indicating they are alternative specification methods—a semantic constraint not explicit in the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb ('Extract') and resource ('structured data from a web page') are clear, plus method ('using an LLM'). However, it does not explicitly differentiate from siblings like 'scrape' (raw HTML) or 'crawl' (multiple pages), only implying the distinction via the LLM mention.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage by stating input options ('Provide either a JSON schema or a natural language prompt'), but lacks explicit when-to-use guidance versus alternatives like 'scrape' or 'summarize', and does not state prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It adds valuable behavioral context by specifying it checks both 'robots.txt' and 'sitemap.xml'. However, missing critical behavioral details: whether it follows sitemap index files, rate limiting, output format, or error handling when sitemaps are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely efficient at 9 words. Front-loaded with the action 'Discover URLs' and immediately specifies the source mechanism. Every word earns its place with zero redundancy or boilerplate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Appropriately complete for a single-parameter discovery tool. The description covers the core function and specific data sources. Minor gap: does not describe the return value (list of URLs), though this is somewhat implied by 'Discover URLs'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage with the 'url' parameter well-documented as 'Base URL to discover sitemaps from'. The description does not add additional semantics about the parameter (e.g., protocol requirements, trailing slashes), warranting the baseline score for high-coverage schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Discover' with clear resource 'URLs' and distinct mechanism 'sitemaps (robots.txt + sitemap.xml)'. This effectively distinguishes the tool from siblings like 'crawl' or 'scrape' by specifying it extracts URLs from sitemap files rather than page content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage through the specific mechanism mentioned (sitemaps), suggesting use when architectural URL discovery is needed. However, lacks explicit guidance on when to prefer this over 'crawl' or 'search' siblings, or prerequisites like requiring valid sitemap files.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full disclosure burden. Adds valuable operational context about automatic fallback to webclaw cloud API for bot protection and JS rendering, but omits rate limits, authentication requirements, timeout behavior, and error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. First sentence establishes core functionality and output options; second provides critical implementation detail about fallback behavior. Well-structured and appropriately front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a 6-parameter tool with complete schema coverage. Description compensates for missing output schema by detailing return format options, though could enhance with error behavior or rate limit documentation given the web scraping domain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage (baseline 3). Description adds semantic value by clarifying that 'llm' format means 'LLM-optimized text' and elaborating on format intentions beyond the schema's terse descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear specific verb 'scrape' with scope 'single URL' that explicitly distinguishes from sibling tools like 'batch' and 'crawl'. Lists output formats (markdown, LLM-optimized text, plain text, JSON) to clarify extraction capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly differentiates from batch/crawl via 'single URL' phrasing, but lacks explicit when-to-use guidance versus siblings like 'extract' or 'map'. No mention of prerequisites such as URL accessibility or when to prefer local vs cloud fallback.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It effectively discloses two key behaviors: (1) it uses an LLM (not just extraction), and (2) it 'automatically falls back to the webclaw cloud API when bot protection is detected' - crucial resilience behavior. Missing: output format, error handling on failure, or rate limit warnings.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, both essential. First sentence front-loads the core purpose (LLM summarization). Second sentence provides critical operational detail (fallback mechanism). Zero waste, appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no output schema, the description adequately covers core function and resilience behavior. Minor gap: no mention of return value format (string vs object) or what happens if the URL is unreachable even after fallback.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage ('URL to fetch and summarize', 'Number of sentences...'), establishing a baseline of 3. Description reinforces the 'web page' concept aligning with the url parameter, but adds no syntax details, format constraints, or semantic clarification beyond the schema definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool 'Summarize[s] the content of a web page using an LLM' - providing specific verb (summarize), resource (web page content), and method (LLM). This distinguishes it from siblings like 'scrape' (raw extraction), 'extract' (structured data), and 'crawl' (multi-page), which don't imply LLM-based condensation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage by mentioning LLM-based summarization, but provides no explicit guidance on when to choose this over siblings like 'scrape' or 'extract'. The fallback behavior hints at resilience but doesn't state prerequisites or exclusions.
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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