ScrapingDog-MCP

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
Each tool targets a specific platform and function (e.g., google_search vs google_images), making distinctions clear. Minor overlap exists between universal_search and google_search, but descriptions resolve ambiguity.
Naming Consistency4/5
Most tools follow a consistent 'service_function' pattern (e.g., amazon_product, google_images). A few tools like 'scrape' and 'account' lack a service prefix, causing slight inconsistency.
Tool Count1/5
With 77 tools, the server far exceeds the typical well-scoped range (3-15). This overwhelming number forces agents to sift through many options, increasing selection complexity and potential errors.
Completeness4/5
Coverage is broad, including search, product details, reviews, profiles, and posts for most major platforms. Some niche endpoints (patents, scholarships) are included, though a few services (e.g., Indeed, Yelp) have only one tool.
Average 4.1/5 across 77 of 77 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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.
Add a glama.json file to provide metadata about your server.
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.
Add related servers to improve discoverability.

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?
Description discloses key behaviors: returns suggestions with relevance scores, based on country and language, and notes no pagination. However, it lacks explicit mention of read-only nature, rate limits, or error handling. Since no annotations are present, this is acceptable but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but includes filler ('Credits: Not specified in documentation') that adds no value. Could be tightened by removing that line.
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?
Despite lacking an output schema, the description specifies the return format with fields. It covers the core functionality well, though it omits details like field descriptions ('type' unclarified) and possible empty results.
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 covers all parameters with descriptions, and the description reinforces their purpose (query, country, language). No additional semantics beyond schema.
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 it returns Google Search autocomplete suggestions for a query, including relevance scores. It differentiates from generic search tools but does not explicitly distinguish from sibling autocomplete tools like google_trends_autocomplete.
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?
No guidance on when to use this tool versus alternatives like google_search or other autocomplete tools. No prerequisites or exclusions mentioned.
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 describes the output structure (JSON with suggestions array) and explains the Freebase-style mid usable in google_trends. However, it lacks details on rate limits, authentication, or 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description includes useful output info and credit note, but the credit statement is extraneous for tool invocation. Could be more concise without losing essential guidance.
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?
Given 2 params, 100% schema coverage, no output schema, and no annotations, the description is reasonably complete. It explains output structure and the use of mid, satisfying basic needs for a lightweight autocomplete tool.
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%. Description does not add meaning beyond the schema; it mentions query and language but provides no additional constraints, examples, or formatting details.
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?
Description clearly states it returns Google Trends autocomplete suggestions for a search query, including topics/entities with categorization and a Trends link. Distinguishes from siblings like google_trends and google_autocomplete by focusing on autocomplete for Trends data.
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?
No explicit guidance on when to use this tool vs alternatives. Mentions the mid can be used in google_trends endpoint, implying it is a precursor, but does not specify when to choose this over google_autocomplete or other similar tools.
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 the description carries the full burden. It discloses credit cost and notes that documentation does not name specific engines, which is transparent. However, it does not discuss rate limits, reliability, or handling of duplicates across engines. The return structure is given, adding some transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured: it states the core capability, then adds credit info and return format. It is front-loaded with the most important information. Could be slightly shorter if credit info were moved, but overall cohesive.
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?
For a tool with no output schema and moderate complexity (multiple engines, credit cost), the description provides a decent overview. It explains the return structure and credit usage. However, it lacks details on real-time vs cached results, how credits are consumed per query, and whether advanced operators are supported.
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 description coverage is 100%, but the description adds value by explaining query behavior ('just like a standard search') and providing examples for country and language parameters. It also emphasizes geographic targeting and language customization, which goes beyond the schema's generic descriptions.
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 it scrapes results from various search engines, handling proxy and parsing. This distinguishes it from sibling tools targeting specific engines (e.g., google_search). However, it could be more explicit that it aggregates multiple engines in one request.
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?
No guidance on when to use this tool versus individual search tools. Lacks context about trade-offs or prerequisites. The description only says it supports geographic targeting, but does not help the agent decide between universal and specific search tools.
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, the description carries the burden. It effectively discloses the cost (5 API credits), pagination behavior (next_page_token), and the return format. This goes beyond the schema by adding behavioral context. It does not contradict any annotations (none provided). However, it could mention error conditions or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is mostly concise with two sentences and a return type example. However, it includes a redundant phrase '[Credits: 5 API credits per request]' that repeats the earlier statement, slightly reducing efficiency. Still, it is well-structured and front-loaded.
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 has 10 parameters, no output schema, and no annotations, the description provides adequate context: cost, pagination, and return structure. It lacks explanation of error handling, rate limits, or parameter interactions. It is complete enough for basic use but has gaps for a 10-parameter tool.
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%, so the baseline is 3. The description adds minimal extra meaning beyond the schema; it clarifies that query should be like 'jobs in london' and that next_page_token comes from a prior response. Most parameters remain as described in the schema, so no significant additional semantics.
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 it retrieves Google Jobs search results with specific fields (job titles, company names, etc.), distinguishing it from general search tools like google_search. However, it does not explicitly differentiate from other job-specific tools in the sibling list (e.g., linkedin_jobs_search, indeed_scraper), so it loses a point for lack of explicit differentiation.
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 guidance on pagination using next_page_token and mentions the cost (5 credits), which helps in usage. However, it does not specify when to use this tool versus alternatives, nor does it mention prerequisites or limitations. The guidance is implied but not explicit.
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, the description carries the burden. It discloses credit cost and mutual exclusivity but lacks details on authentication, rate limits, error handling, or pagination beyond the 'page' parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a clear first sentence stating purpose, followed by credits and notes. The structure is good, though the credits line is redundant.
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?
Given 9 parameters and no output schema, the description provides a reasonable return structure and constraints. However, it misses potential error scenarios and pagination behavior beyond the 'page' parameter.
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%, so the schema already documents parameters. The description adds critical mutual exclusivity note and return format, which goes beyond the schema.
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 it retrieves Google Local business listings with specific fields like ratings, reviews, addresses, etc. It distinguishes from siblings like google_search (web search) but does not explicitly differentiate from similar tools like google_maps_search.
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 mentions cost (5 credits) and the mutual exclusivity of 'location' and 'uule', providing some constraints. However, it does not provide when to use this tool versus alternatives like google_maps_search.
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 are provided, so the description carries the burden. It discloses the return structure and credit cost, but does not explicitly state that the tool is read-only, nor does it mention potential errors, rate limits, or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose, though the return structure is presented as a dense block. Overall, it avoids redundancy and is efficiently structured.
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?
Given the tool's moderate complexity, the description covers the key aspects: purpose, parameters, credit cost, and return format. No output schema exists, but the inlined return structure compensates. It is sufficiently complete for agent usage.
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% with good descriptions, and the description adds value by explaining the two identification approaches and providing an example URL, making parameter usage clearer.
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 it extracts detailed data for any TikTok post, listing specific fields like play counts, likes, comments, etc. However, it does not explicitly differentiate from sibling tools like tiktok_profile or scrape_post.
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 explains the two parameter approaches (url or username+post_id) and mentions credit cost, but does not provide guidance on when to use this tool over alternatives or when not to use it.
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 credit cost (4 credits) and pagination scheme (start offset in increments of 10, next URL). However, lacks details on potential restrictions, rate limiting, or authentication requirements. Without annotations, more behavioral context would be beneficial.
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?
Description is three sentences, front-loaded with purpose, and contains no unnecessary words. Efficient and clear.
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?
Despite no output schema, the description adequately explains the return structure (filters, ads, organic_results, pagination). Covers pagination details, credit cost, and domain targeting. Missing some nuance on parameter interdependencies but generally complete for a scraper tool.
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%—every parameter is described in the schema. The tool description adds marginal semantics like pagination explanation and domain targeting, but overall adds limited value beyond the schema.
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?
Clearly states the verb (extract), resource (business listings from Yelp), and key parameter categories (keyword, location, filters, sorting, pagination). Distinguishes from sibling scraper tools by targeting Yelp specifically.
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?
Does not provide when-to-use or when-not-to-use guidance. With many sibling scraper tools (e.g., google_search, amazon_search), explicit guidance on selecting this tool for Yelp-specific scraping is missing.
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, description carries full burden. Discloses no pagination and returns all active offers, also provides return structure. However, lacks disclosure of whether tool is read-only, auth needs, rate limits, or other behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is relatively concise, front-loading purpose. Includes helpful notes on credits and pagination with return structure example. A few extra details could be trimmed but overall efficient.
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?
Given absence of output schema, description compensates by providing detailed return structure. Covers key aspects like no pagination and credit note. Adequate for a single-resource tool with 4 parameters.
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%, so baseline 3. Description does not add much beyond schema for parameters; it mentions 'for a given ASIN' but schema already describes asin. No additional semantic enrichment for parameters.
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 uses specific verb 'Retrieves detailed offer data for a given ASIN' combined with resource 'ASIN' and includes key aspects like pricing, availability, seller details, delivery. Clearly distinguishes from sibling tools like amazon_product (product details) and amazon_reviews (reviews).
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 when needing offer data for a specific ASIN, mentions no pagination and returns all active offers, but does not explicitly state when to use vs alternatives or 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full disclosure burden. It transparently notes that response field names are inconsistent between sample and code examples, and captures both in 'response_summary'. Also clarifies that credits are not applicable. This reveals important behavioral quirks beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose and not front-loaded. It starts with purpose but then dives into detailed notes about documentation inconsistencies, which could be summarized more briefly. Every sentence does not earn its place; extraneous details clutter the message.
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 no output schema, the description adequately details return fields (though inconsistently). For a simple status-check tool with zero parameters, it covers essential aspects, but the lengthy digression on documentation conflicts may confuse rather than clarify.
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?
No parameters exist, so the baseline is 4. The description does not need to add parameter details. It instead provides useful information about the response structure, adding value beyond the empty schema.
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 clearly states the tool monitors Scrapingdog account usage, specifically remaining API credits and active concurrent connections. This verb+resource combination is unique among sibling tools (primarily scrapers), leaving no ambiguity about its purpose.
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?
No explicit guidance on when to use this tool vs alternatives. However, among siblings with different scraping functions, the context strongly implies it's for account status checks. Still, lacking when-not or alternative recommendations.
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, the description carries full burden. It describes return format and pagination but does not explicitly state read-only nature or any side effects. The documentation note is about a copy-paste artifact, not behavioral.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is detailed and well-structured, front-loading the core purpose and then adding notes. It is slightly lengthy but each part adds information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description thoroughly explains the return format and pagination tokens. It covers query, filters, region, pagination, and HTML retrieval, making it self-contained for a search tool.
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?
All 5 parameters are documented in schema (100% coverage). The description adds extra context: custom date range format, region documentation reference, and clarification on the html param, providing value beyond the schema.
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 clearly states it retrieves organic search results from DuckDuckGo and lists key features (region codes, date filters, pagination), differentiating it from sibling search tools like Google or Bing.
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?
No explicit guidance on when to use DuckDuckGo vs alternatives (e.g., Google, Bing). Mentions credit cost differences but does not provide decision criteria for tool 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 are provided. The description adds credit cost, pagination behavior, and return format, but does not disclose rate limits, authentication requirements, or any destructive actions. It covers basic behavioral aspects but lacks depth.
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 three sentences long with no wasted words. The first sentence states the core purpose, followed by credits and a pagination note, and finally the return format. It is efficiently structured and 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?
Given the lack of output schema, the description provides a sample return structure and explains pagination. It covers essential operational details for a search tool, though it could include error scenarios or result limits.
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%, so the baseline is 3. The description adds minimal extra meaning: notes that 'page' is 0-indexed and provides an example of the return structure. However, it does not elaborate on parameter interdependencies or usage recommendations.
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 clearly states the tool retrieves Google video search results with specific features like geographic localization and language preferences. It uses a specific verb ('Retrieves') and resource ('Google video search results'), distinguishing it from siblings like google_images or google_search.
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 mentions pagination and credits cost but does not explicitly state when to use this tool over alternatives. No guidance on when not to use it or which sibling tools are better for other search types.
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 are provided, so the description carries full burden. It mentions credits cost and output format (parsed JSON by default, or HTML if html=true). However, it lacks details on error handling, rate limits, or any destructive behavior (though scraping is presumably read-only).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but verbose, especially the extensive return structure example. It is front-loaded with purpose, but the length could be reduced without losing clarity. Several sentences could be condensed.
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?
Given no output schema, the description provides a thorough breakdown of the return object fields. Parameters are fully covered. However, it missing information about pagination or how to retrieve additional results, which would be expected for a search tool.
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?
Input schema has 100% coverage, but the description adds meaning by specifying that the url must be a full pre-built Myntra search URL with rawQuery and filters, and explains the html parameter. This provides context beyond the schema 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?
The description clearly states it scrapes Myntra search result pages by passing a search URL, and lists extracted data (product IDs, names, brands, prices, etc.). It distinguishes from sibling tools like myntra_product by focusing on search results.
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 explains how to use it (pass a full pre-built Myntra search URL) but does not explicitly state when to use this tool vs alternative shopping search tools or provide exclusion criteria. The context is implied by the platform-specific name.
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 are provided, so the description bears the full burden. It discloses the credit cost and return fields but lacks warnings about rate limits, authentication needs, or potential data staleness. This is adequate but not comprehensive.
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 clear structure: purpose, credit note, and return fields. No fluff, front-loaded with key verb 'scrapes'. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter scraping tool, the description covers purpose, param format with example, credit cost, and all return fields. No output schema exists, but the description compensates with a comprehensive field list. Complete for its complexity.
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%, but the description adds value by clarifying that profileId accepts a plain handle without '@' and gives an example ('elonmusk'). It also later lists the return structure, aiding understanding.
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 it scrapes comprehensive profile data for X users and lists specific data fields (follower counts, engagement metrics, etc.). However, it does not explicitly differentiate from sibling tools like 'account' or 'scrape'.
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 implies usage for fetching profile data and includes credit cost, but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'account' or 'scrape_post'). No when-not-to-use conditions are mentioned.
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 burden. It discloses the credit cost, mutual exclusivity of mkt and cc, and the return format. However, it omits details like rate limits, auth requirements, or error handling. The added context is valuable but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise with two sentences plus a notes section. It front-loads the purpose and adds essential details. Slight repetition of credit cost could be removed, but overall efficient.
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?
Given 9 parameters and no output schema, the description covers the return format and key parameter interactions. It lacks details on pagination behavior or error cases, but for a search tool it provides sufficient context for an agent to use it correctly.
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%, baseline 3. The description adds meaning beyond the schema by explaining that lat/lon set a geographic starting point, mkt and cc are mutually exclusive, and first adjusts starting position with a default. This improves usability beyond the raw schema.
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 clearly states it retrieves organic search results from Bing with customizable parameters, using a specific verb and resource. It implicitly distinguishes from sibling tools like Bing Shopping and other search engines by explicitly naming Bing search.
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 explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or compare to related sibling tools like google_search, duckduckgo_search, etc.
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, the description carries full burden. It discloses the output format (parsed JSON or HTML), mentions credit cost, and describes the return structure in detail. However, it omits potential error states or rate limiting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and includes important notes. It is moderately concise; the return schema section is lengthy but useful. Minor redundancy (credits noted twice). Still efficient overall.
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?
Given no output schema, the description provides a detailed return structure. Parameters are fully described. It covers credits and URL structure. Missing guidance on error handling or edge cases, but adequate for typical use.
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%, so baseline is 3. The description adds value by explaining the URL structure (product identity embedded) but largely restates schema descriptions for the html parameter. Infomation adds minimal extra meaning beyond the schema.
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 clearly states the tool scrapes an eBay product listing page by URL to retrieve specific fields. It uses a specific verb ('scrape') and resource ('eBay product listing page'), and is easily distinguishable from siblings like ebay_search.
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 implies usage for scraping known product pages but does not explicitly state when to use this vs alternatives (e.g., ebay_search). It provides a note about URL structure but no when-to-use or when-not-to-use guidance.
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, description carries full burden. It mentions credit consumption and notes about URL identity, but does not disclose rate limits, error handling, or behavior for invalid URLs. Some transparency but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is relatively concise with organized sections (credits, notes, returns). Could be slightly more concise, but this is effective.
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?
Given no annotations or output schema, description provides a detailed return structure and credit info. Lacks error behavior and prerequisites, but is largely complete for a scraping tool.
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 description coverage is 100% (both parameters described). Description adds value by explaining credit cost and return format, enhancing understanding beyond the schema. Baseline 3, plus extra context.
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 explicitly states it scrapes Flipkart product pages via URL and lists the data retrieved (title, brand, pricing, etc.). It clearly distinguishes from sibling tools like flipkart_search (which searches) and other product scrapers.
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 provides credit cost and notes about URL structure, but does not explicitly state when to use this tool versus alternatives (e.g., flipkart_search for search, or amazon_product for Amazon). No exclusion criteria or context for selection.
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, the description provides the return structure and input format, disclosing behavioral traits. However, it lacks information on error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and is mostly concise, though the credits line is superfluous and could be removed for better conciseness.
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?
Given the tool's complexity and lack of output schema, the description adequately covers input format and return structure, but could be improved by noting error behavior or response format limitations.
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%, so baseline is 3. The description adds value by reinforcing the query format and defining the return structure, but does not significantly enhance parameter understanding beyond the schema.
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 clearly states the tool retrieves Google Finance market data including stock price, price movement, and related market/news instruments. This distinguishes it from sibling tools like google_search which are more general.
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 implicitly indicates usage for financial data retrieval and specifies the query format requirement, but does not explicitly contrast with alternatives or state 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost (5 API credits), parameter type nuance (string but boolean-like), and returns structure. With no annotations, this is valuable behavioral context. Lacks mention of idempotency or rate limits, but sufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise and front-loaded with the main action and credits. Could be more structured (e.g., bullet points), but no wasted sentences.
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?
Given 7 parameters with 100% schema coverage and no output schema, the description provides meaningful extra context (credits, type warning, return format) to support agent decision-making. Complete enough for typical use.
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%, but description adds clarity on boolean-like parameter values and the URL format for the required parameter. This goes beyond the schema descriptions, aiding correct usage.
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 it performs reverse image search via Google Lens, supporting product, visual, and exact matches. It differentiates itself from sibling tools like google_search and google_images by specifying the Lens endpoint and result types.
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?
No guidance on when to use this tool versus alternatives, such as when to choose Lens over generic image or web search. No explicit when-not-to-use scenarios mentioned.
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 exist, so the description carries full burden. It discloses API credit cost (5 credits per request) and the return structure. However, it does not mention error handling, rate limits, or what happens if patent_id is invalid. The credit cost is helpful but incomplete for full behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is efficient at 5-6 sentences, front-loading the core purpose and key fields. Uses notes for critical format clarification and cost info. Could be slightly tighter but is well-structured and avoids verbosity.
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?
Given the tool has three parameters, no output schema, and no annotations, the description covers what the tool does, what it returns, and the crucial format constraint. It does not explain the html or language parameters beyond the schema, but those are standard. Overall adequate for an agent to use correctly.
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%, with parameter descriptions already clarifying the patent_id format. The description's format note is redundant with the schema. The description adds minimal extra meaning beyond listing return fields, which is not directly parameter semantics. Baseline score of 3 is appropriate.
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 clearly states the tool retrieves detailed information about a specific patent, listing key fields like title, PDF link, inventors, dates, and prior-art keywords. It is specific and distinguishes from sibling tools like google_patents which is a search endpoint.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on the required patent_id format, including an example and warning about the format returned by the sibling google_patents search. Implies this tool should be used after obtaining a patent ID from search results. Could be more explicit about when to use vs. other patent-related tools.
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, the description fully discloses the async behavior: immediate session ID response followed by asynchronous POST to webhook. It notes the undocumented webhook payload shape, which is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly verbose including a redundant 'Credits: Not specified' line and a returns section that repeats the notes. Could be tightened without losing clarity.
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?
For a simple 3-param tool with no output schema, the description covers core behavior and return value. However, it lacks information on error handling, authentication requirements, or potential failure modes, leaving some gaps.
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%, so baseline is 3. The description does not add further meaning to the parameters beyond what the schema already provides.
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 clearly states it receives scraped data at a configured webhook endpoint asynchronously, using a session ID pattern. It explicitly distinguishes from polling and is unique among sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It advises using this tool for async workflows and database pipelines, explicitly contrasting with polling. However, it does not specify when not to use or compare to synchronous scraping tools.
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 are provided, so the description carries the full burden. It discloses credit costs, endpoint verification status (and the actual working endpoint), pagination token usage, and the shared endpoint behavior. However, it does not mention rate limits, authentication, or 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is slightly long but well-structured, starting with the core purpose, then credits, endpoint notes, pagination, verification, and return format. Every sentence adds value, though it could be tightened.
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?
Given the complexity (multiple result types, pagination) and no output schema, the description provides a detailed return structure and explains pagination and endpoint behavior. It covers most key aspects but omits error handling and rate limits.
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%, so the baseline is 3. The description adds context for sp (pagination and filter examples) and search_query (identifying the tool), but country and language are already well-documented in the schema. The description adds marginal value beyond the schema.
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 clearly states it scrapes YouTube search results for any query and lists the types of data returned (videos, channels, shorts, etc.). It distinguishes itself from sibling tools like youtube_video or youtube_channel by focusing on search.
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 mentions that the presence of search_query selects Search behavior and explains pagination, but it does not provide explicit guidance on when to use this tool vs alternatives like youtube_video or youtube_channel. No exclusions or when-not-to-use scenarios are given.
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 full burden. It discloses the return format and pagination uniqueness, but does not mention whether the tool is read-only, requires authentication, or handles errors. This is adequate but not fully transparent 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused paragraph with the purpose upfront, followed by a critical pagination note and a clear return structure. No unnecessary words, earning 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?
The description provides a return structure example, compensating for the missing output schema. It covers the essential behavioral note on pagination. However, it lacks explicit mention of idempotency or error scenarios, which are minor gaps for a simple search tool.
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% with all 11 parameters described, so the description adds no additional semantic value beyond the schema. The mention of pagination offset is a behavioral note, not parameter-specific. Baseline 3 is appropriate.
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 clearly states it retrieves Google Shorts search results, specifying the type of content (short videos) and listing returned fields (thumbnails, GIF previews, account names, dates). This distinguishes it from siblings like google_videos or google_search which cover general video or web search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly notes that pagination uses offset-based 'start' unlike other endpoints using 'page', providing clear context for correct usage. However, it does not explicitly state when to use this tool versus alternatives, though the purpose is self-explanatory for short video queries.
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, the description covers credit costs (50-100), premium cost increase, webhook delay, and expected fields (with uncertainty). However, it does not explicitly state that the operation is read-only, nor does it disclose potential rate limits or error conditions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and then details each parameter. It includes a note about uncertain return fields, which slightly reduces conciseness but is honest. Overall well-structured.
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 lack of output schema, the description attempts to describe return fields but admits uncertainty. It covers parameters well but omits error handling, rate limits, and pagination details. Adequate but with gaps.
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%, and the description adds valuable context: id is a slug, type='profile' differentiates modes, premium impacts cost, webhook trades timeliness for success. This enriches 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?
The description clearly states the tool scrapes LinkedIn person profiles using a profile ID and returns full profile data including experience, education, and about sections. This specific verb+resource combination distinguishes it from sibling tools like linkedin_company_profile.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on using the profile ID slug, required type='profile', and options like premium and webhook with their trade-offs. It does not explicitly state when not to use this tool, but the distinction from company/profile mode is clear.
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 notes credits cost and missing official documentation, but does not disclose rate limits, authentication requirements, or potential side effects. Adequate but not thorough.
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?
Description is efficiently structured: one short paragraph with key information (purpose, credits, parameter hint, expected return fields). No superfluous sentences.
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 no output schema, the description adequately covers what it does, parameter source, and expected return structure. It honestly notes documentation gaps, which aids realistic agent expectations.
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 description already covers the 'id' parameter well (100% coverage). The tool description adds practical guidance on extracting the numeric ID from the share URL, providing extra value beyond the schema.
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 explicitly states it scrapes LinkedIn posts by ID, returning content and engagement data. This clearly distinguishes it from sibling tools targeting other LinkedIn data like profiles or jobs.
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 implies usage for scraping post data and provides credit cost and ID extraction instructions, but lacks explicit guidance on when to prefer this tool over alternatives or when not to use it.
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?
Discloses return type (binary image data), credit cost (5 credits per request), and notes that no country/proxy/session parameters are documented. It also mentions the wait_until parameter controls load timing. This adds value beyond the schema and annotations (which 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main action but includes extraneous details about documentation structure (e.g., 'only one of the fetched pages that ships a full, structured API Parameters reference table') that may not aid an AI agent. Could be more concise.
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?
Given 7 parameters, no output schema, and no annotations, the description adequately covers purpose, parameters, output type, and credit cost. It lacks error handling details but sufficiently describes the return format for a screenshot tool.
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%, so baseline is 3. The description adds credit cost and documentation notes but does not add significant new meaning beyond what the schema already provides for each parameter.
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 starts with 'Captures a screenshot of any webpage' which clearly states the action (captures) and resource (screenshot of webpage). It lists controllable aspects and distinguishes from the sibling 'scrape' by noting it's a distinct endpoint, providing clear differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives context by contrasting with /scrape endpoint, implying this tool is for image capture rather than HTML scraping. However, it does not explicitly state when to use this tool over other siblings or provide when-not conditions.
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 burden. It discloses credit cost per request, notes on missing localization documentation, and describes the return structure. However, it does not cover rate limits, authentication, or 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?
The description is concise and front-loaded with the purpose. It includes essential notes on credits, localization, pagination, and return structure without unnecessary words. Every sentence provides value.
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?
Given the tool is simple with one parameter and no output schema, the description adequately covers purpose, return structure, and usage constraints. Minor omissions like error handling prevent a perfect score.
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 only one parameter (query) that includes an example. The main description adds no additional parameter semantics beyond what the schema already provides. Baseline 3 applies.
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 clearly states the tool retrieves Walmart autocomplete search suggestions for any query, specifying it includes suggested search terms and category navigation data. This distinguishes it from sibling tools like google_autocomplete or walmart_search.
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 context on credits, lack of localization parameters, and no pagination, but does not explicitly state when to use this tool versus alternatives like walmart_search or other autocomplete tools. Usage guidance is implied rather than direct.
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, the description carries full burden. It discloses credit usage, return format, and limitations on pagination and localization. Does not mention rate limits or error handling, but adds useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is structured with a clear purpose statement, followed by notes and return format. It is somewhat long but organized, with front-loaded key information. Some minor redundancy could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description details the full return structure and notes important limitations. For a single-parameter tool, it provides comprehensive context, making it self-contained for an agent.
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%, so baseline is 3. The description adds minimal extra context beyond the schema's description of the 'url' parameter, mainly giving an example URL. Does not significantly enhance semantics.
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?
Clearly states it scrapes Walmart product reviews and returns ratings distribution, individual reviews, and top positive/negative feedback. Distinguishes from siblings like walmart_product and walmart_search by specifying the input is a reviews page URL.
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 some usage context (no pagination parameters, credits cost) but does not explicitly state when to use this tool versus alternatives like walmart_product. Lacks direct guidance on 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses cost (5 API credits), how to extract tweetId from URL, and lists all return fields. While it doesn't mention failure modes or rate limits, it provides sufficient behavioral context for a read-only extraction tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is moderately sized with front-loaded purpose. Each sentence adds value: metrics list, credit cost, input note, return fields. Could be slightly trimmed, but overall efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, description lists all return fields comprehensively. It covers input format, cost, output structure, and includes a usage note. For a single-parameter tool, this is complete and self-contained.
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% with both schema and description explaining tweetId. Description adds redundant but clarifying example. Baseline 3 is appropriate as schema already does the heavy lifting and description adds marginal value.
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 clearly states the tool extracts detailed data for any X (Twitter) post, listing specific engagement metrics, content, and author info. The verb 'extracts' and resource 'X post' are specific and distinct from siblings like x_profile or search tools.
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?
No explicit guidance on when to use this tool versus alternatives. The description does not mention when not to use it or suggest sibling tools. Usage is implied by the clear purpose, but lacking explicit context for decision-making.
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, the description carries the transparency burden. It reveals the credit cost, mutual exclusivity constraint, and return structure (JSON snippet). It does not mention idempotency or side effects, but as a read tool, this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence plus a note on credits and mutual exclusivity, plus a return snippet. It is fairly concise, though the return snippet could be more structured. No fluff.
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?
Given the lack of output schema, the description provides a return structure example. It covers credit usage and mutual exclusivity. It omits rate limits and error handling, but for a search tool, this is reasonably complete.
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%, so parameters are documented there. The description adds minor value: notes mutual exclusivity (not in schema) and efirst offset behavior. Most parameter meaning is already in schema, so baseline 3 is appropriate.
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 clearly states 'Retrieves shopping results from Bing', specifying the resource and action. It lists features like market targeting, pagination, and filters, distinguishing it from sibling shopping tools (e.g., google_shopping, walmart_search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions mutual exclusivity of mkt/cc, pagination via efirst, and filters, providing clear usage context. However, it lacks explicit guidance on when to choose this tool over alternatives like google_shopping or ebay_search.
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?
The description details the return format including specific fields (shopping_results, inline_images, text_blocks, references, local_results) and notes the credit cost per request. It also explains the mutual exclusivity of geo-targeting parameters. However, it does not mention authentication requirements or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence followed by structured notes on parameter constraints and return format. It is front-loaded with the core purpose and credits. The return format details are extensive but necessary for a tool returning structured data. Could be slightly more concise.
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?
Given the lack of output schema and annotations, the description provides a thorough explanation of the return structure and key parameter interactions. It covers credit costs and mutual exclusivity. However, it does not explain what 'AI Mode' entails or how it differs from other Google search tools, which would enhance completeness.
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?
The schema has 100% parameter description coverage, so the baseline is 3. The description adds value by explaining the mutual exclusivity of uule and location, and that country further localizes the search alongside these parameters. This additional context improves understanding beyond the schema.
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 clearly states that the tool searches Google with AI Mode enabled and returns structured results with reference sources and text blocks. It distinctly identifies the action and resource, differentiating from siblings like 'google_search' which likely returns standard search results.
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 mentions credit cost and mutual exclusivity of parameters (uule and location), but does not provide explicit guidance on when to use this tool versus alternatives (e.g., standard Google Search or AI Overview). The context of when to prefer AI Mode over other Google tools is implied but not stated.
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 are provided, so the description carries the full burden. It discloses the cost (10 credits), return structure, and important parameter interactions (mutual exclusivity of date parameters, chips from suggested_searches). It does not cover rate limits or authentication, but given the complexity, it provides substantial behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative and front-loaded with purpose, but it contains a slight redundancy (credit mentioned twice). It is structured with clear notes, but could be more concise. Overall, it earns its place without being overly verbose.
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?
Given the complexity (23 parameters, no output schema, no annotations), the description covers key behavioral aspects: cost, return format, and parameter constraints. It does not detail every edge case, but it provides sufficient contextual completeness for an agent to use the tool effectively.
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%, so the schema already documents all parameters. The description adds meaningful semantic guidance beyond the schema, such as the chips discovery mechanism and the constraints on time filtering parameters. This extra context helps an agent use the parameters correctly, elevating it above the baseline of 3.
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 clearly states the tool retrieves Google Images search results including specific fields like titles, thumbnails, source links, and dimensions. It distinguishes from sibling tools such as google_search, google_videos, and google_lens, which handle different media or search types. The verb 'retrieves' is 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.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions cost (10 API credits per request) and provides notes on time filtering and chips usage. However, it does not explicitly contrast with siblings or state when to use this tool vs. alternatives like google_search or google_lens. While some guidance is implied, it lacks explicit when-to-use/when-not-to-use direction.
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 must carry burden. Discloses return structure and pagination behavior but does not mention rate limits, authentication, or side effects (though read-only assumed).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two substantive sentences plus return format. Front-loaded with purpose. Extraneous credit note but overall concise.
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?
Completeness is high given 5 parameters and pagination complexity. Covers pagination, parameter usage, return structure. No output schema, so return format is helpful.
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% with descriptions for all 5 parameters. Description adds context: explains sori and stores collaboration, and source of page_token. Also gives return format.
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 it scrapes Google's immersive product popup view for a specific product, returning brand info, price range, and per-store listings. Distinguishes from sibling google_shopping by focusing on popup view.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit pagination guidance: enable `stores=true` and increment `sori` by cumulative sellers. Notes that page_token comes from google_shopping. Lacks explicit alternatives or 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description does well by disclosing credits usage, pagination behavior, relative timestamps, and that no id/token concepts are used. It could mention rate limits or error handling, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise, combining key information in a few sentences and notes. It could be better structured (e.g., bullet points for notes), but it is efficient and front-loaded with the primary purpose.
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?
Given 12 parameters, no output schema, and no annotations, the description adequately covers the tool's purpose, behavior, and return structure (inline). It does not cover error scenarios or environment requirements, but it is sufficient for basic invocation.
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%, so the schema already documents all parameters. The description adds some context (e.g., pagination note, v2 reference) but does not significantly enhance parameter meaning beyond what is in the schema.
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 clearly states it retrieves news search results from Google News with specific fields (headlines, snippets, etc.). It distinguishes itself from google_news_v2 by mentioning relative timestamps vs absolute dates, and from other search tools by specifying the 'classic search-style scrape' nature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context on pagination (0-indexed), credits, and a clear alternative (google_news_v2) for absolute dates. However, it lacks explicit guidance on when to use this tool over other search tools like google_search or google_news_v2 beyond the date distinction.
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, the description carries the full burden. It discloses credit cost, multi-query constraints, region parameter dependencies, and a sample return format for TIMESERIES. However, it omits details like error handling, rate limits, and full structure for GEO_MAP data types, which would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at a few sentences, front-loading the core purpose. It includes inline notes and return format, but the structure is dense and could be improved with bullet points for better readability. No wasted sentences.
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?
Given 9 parameters and no output schema, the description covers key behavioral constraints and a sample output for TIMESERIES. It omits full output structures for GEO_MAP types and does not differentiate from many siblings, but is adequate for a read tool with good parameter descriptions.
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%, baseline 3. The description adds value by clarifying relationships between data_type and query limits, and region parameter usage with GEO_MAP types. It also provides practical examples for date formats and topic IDs, going beyond the schema 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?
The description clearly states the tool retrieves Google Trends search interest data, listing three specific data types (interest over time, comparative regional breakdown, interest by region) and the capacity for up to 5 queries. It distinguishes from siblings like google_trends_autocomplete and google_trends_trending_now by focusing on historical trends data.
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 does not explicitly compare this tool to alternatives or state when to use it over siblings. It implies usage through data type descriptions but lacks direct guidance on tool selection, such as 'for autocomplete suggestions use google_trends_autocomplete'.
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, the description provides useful behavioral context: notes the geo parameter's default behavior making it effectively optional, and describes the return format (JSON with trending_searches array and specific fields). This adds value beyond the schema, though it lacks explicit safety or side-effect information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (a few sentences) and front-loaded with the core purpose. It includes necessary details like credits and return format without unnecessary fluff. The structure is clear and efficient.
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 simple read tool with three parameters, the description covers purpose, parameters, and output format adequately. Missing elements like error handling or use case examples are not critical given the tool's simplicity. The absence of an output schema is compensated by the return structure description.
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% with each parameter described. The description adds nuance about geo's practical optionality due to a default, and hints at hours' predefined values. This enriches the schema information, justifying a score above the baseline of 3.
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 clearly states 'Retrieves currently trending searches on Google', specifying a concrete action and resource. It distinguishes itself from siblings like 'google_trends' (historical trends) and 'google_trends_autocomplete' by focusing on real-time trending data.
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 implies usage for current trends with location/time/language filters, but does not explicitly state when to use this tool versus alternatives like 'google_trends' or 'google_trends_autocomplete'. No exclusions or when-not guidance is provided.
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 exist, so description carries full weight. Mentions credit cost (5 per request) and lists all return fields comprehensively. No hidden behaviors like auth requirements or rate limits are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is 4 sentences with clear structure: purpose, credit note, formatting note, return data. Could be slightly trimmed, but every sentence adds value.
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?
No output schema exists, so the detailed list of return fields is essential and provided. Lacks error handling or additional limitations, but given the tool's simplicity (single parameter, no pagination), it is sufficiently complete.
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% with one parameter already described well. Description adds value by clarifying 'username is the TikTok handle without the @ symbol', which is a helpful nuance beyond the schema's examples.
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 verb 'scrapes' with specific resource 'profile data for any TikTok user' and lists key data types (follower counts, engagement metrics, etc.). Distinguishes from siblings like tiktok_post and tiktok_ads by focusing on profile-level data.
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?
States the tool is for 'any TikTok user' and notes credit cost and username format, but does not explicitly contrast with alternatives or provide when-not-to-use guidance. Siblings include other TikTok tools, but no direct comparison.
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 are provided, so the description must stand alone. It mentions credits cost (2 per request) and the need for a full URL, but does not disclose whether the tool is read-only, rate limits, or authentication requirements. The return object is detailed, partially compensating.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but includes a lengthy sample return object inline. While front-loaded with the purpose, the return format details could be condensed. Every sentence adds value, but the structure could be tighter.
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?
Given no output schema, the description provides a comprehensive sample return object. However, it lacks information on error handling, result limits, or pagination. For a single-parameter tool, it covers the essential usage context well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'url' has a schema description that is minimal. The tool description adds critical context: the URL must be a full Zillow search-results URL built via Zillow's own UI, with examples. This significantly enhances understanding beyond the schema.
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 clearly states the action 'extract', resource 'real estate listings from any Zillow search page', and output 'structured property data'. It distinguishes itself from sibling tools by specifying Zillow as the source, which no other sibling covers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on constructing the input URL via Zillow's own UI and filters, and gives examples like for_sale, for_rent. It implies the tool is for Zillow listings but doesn't discuss when to avoid it or alternatives.
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, the description takes responsibility for behavioral disclosure. It mentions credit cost (5 API credits per request), pagination, concurrency recommendation, and return format. Missing details on error handling or input validation, but overall adequate.
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 efficient: 4-5 sentences, front-loaded with purpose, followed by important notes. No redundant or irrelevant information.
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?
Given 9 parameters and no output schema, the description provides a return format example, which adds completeness. It covers key aspects like pagination, concurrency, and input alternatives but lacks details on potential errors or limits.
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%, so the description adds minimal value beyond what is in the schema. It groups parameters (asin+domain+page) and notes the url shortcut, but does not elaborate on each parameter's meaning. Baseline score is appropriate.
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 clearly states it scrapes customer reviews from Amazon product pages, with explicit filtering options. It distinguishes itself from sibling tools that target other platforms (e.g., google_maps_reviews, walmart_reviews) by specifying Amazon.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage guidance: pagination via page parameter (starts at 1), single concurrency recommendation, and alternative ways to provide input (asin+domain+page vs url). However, it does not explicitly differentiate from other review scraping tools or state 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that no browser automation is required, the credit cost, and details of the return format (structured content blocks). However, it does not mention potential rate limits, error handling, or any required authentication.
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 concise with no wasted words. It is front-loaded with the core action, then provides key details (credits, return format) in a clear, well-structured manner.
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?
Given the tool has no output schema, the description adequately explains the return value. It covers credits and behavioral notes. It is mostly complete for a simple 2-parameter tool, though it could mention error scenarios or timeouts.
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%, so the baseline is 3. The description adds value by explaining the return format and that the prompt is the input, providing context beyond the schema descriptions. It clarifies the structure of the response, aiding interpretation.
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?
Clearly states the tool sends a prompt to ChatGPT and returns a structured JSON conversation. The verb 'sends' and resource 'ChatGPT' are specific, and the description distinguishes from sibling tools that cover other platforms.
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 implies use when you want a ChatGPT conversation, but does not explicitly state when to use this tool vs alternatives, nor does it mention when not to use it. The sibling context helps, but the description itself lacks guidance.
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 are provided, so the description carries the full burden. It discloses the cost (5 API credits per request), the two-step flow (multi-step process), mutual exclusions, and parameter interactions (e.g., booking_token ignores dates/filters). It also describes the return format. This gives good insight into the tool's behavior, though it could mention error handling or rate limits beyond credits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but somewhat verbose and contains redundancy (credits mentioned twice: 'Costs 5 API credits per request. [Credits: 5 API credits per request]'). The structure is front-loaded with purpose but then mixes parameter notes and return format. Could be more concise by removing repetition and organizing flow better.
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?
Given the tool's complexity (29 parameters, multi-step flow), the description covers the essential two-step process, mutual exclusions, and a sample return format. It lacks detailed explanation of multi-city JSON structure and full enumeration of type values (only mentions round trip constraints). Still, it provides substantial context, and no output schema is present, so the return format description helps.
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?
The input schema has 100% description coverage, so each parameter is already documented. The description adds value by explaining interactions (e.g., return_date required only for type=1, mutual exclusions) and the multi-step flow involving departure_token and booking_token. This context helps understand parameter usage beyond the schema.
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 clearly states the tool retrieves Google Flights results for one-way, round-trip, and multi-city searches, with sorting/filtering and booking token support. It specifies the resource (Google Flights) and action (retrieves), and distinguishes it from sibling tools like google_hotels or general google_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool, including a two-step flow for full itineraries, parameter requirements (e.g., return_date required for type=1), mutual exclusions (exclude/include airlines, departure_token/booking_token), and the return format. However, it does not explicitly state when NOT to use the tool or alternatives, but the sibling list makes it obvious.
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 describes retrieval behavior, optional filtering, pagination mechanism, and return format. No mention of side effects or authentication, but for a read-only tool, it's sufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with main purpose upfront, then notes and return format. The credit note is minor but not excessive. Every sentence provides value; could be slightly shorter but remains efficient.
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?
Given no output schema, the description provides a good summary of return structure and pagination. Covers category filter workflow. Lacks error handling or limits, but is fairly complete for a photo retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds substantial meaning: explains how to obtain and use category_id, notes data_id origin from search endpoint, and describes next_page_token usage. This goes well beyond the schema descriptions.
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 it retrieves photos for a Google Maps location using a data_id, with optional category filtering. It distinguishes from sibling tools like google_maps_search and google_maps_reviews, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit instructions for category filtering (two-step process) and pagination (cursor-based with ready-made URL). Mentions data_id source from google_maps_search. Lacks explicit when-not-to-use or alternatives, but context is clear.
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 are provided, so the description carries the full burden. It discloses the return format and fields, but lacks information on error handling, rate limits, authentication, or what happens if no place is found. The credit note is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections and front-loaded purpose. The credit note is slightly extraneous, but overall it's efficient and provides necessary detail in a readable format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the number of parameters (4), no output schema, and no annotations, the description is comprehensive. It explains the input parameters' relationships, describes the output fields in detail, and covers the core functionality adequately for an agent to use it correctly.
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?
With 100% schema coverage, baseline is 3. The description adds value by explaining the alternative required-parameter paths (type+data_id vs place_id) and clarifying that place_id can be used independently, which goes beyond the schema's field 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?
The description clearly states the tool retrieves a complete business profile for a specific Google Maps location, with explicit details on what fields are included. This distinguishes it from sibling tools like google_maps_search (which returns lists) and other specific data tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains two lookup modes (type+data_id vs place_id) and clarifies the parameter requirements. While it doesn't explicitly say when to use this versus alternatives, the context and sibling tool names imply it's for individual place details after a search.
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 are provided, so the description carries the burden. It discloses the credit cost, the special summary object at the end, and the input format. However, it does not mention potential issues like scraping rate limits, error handling, or the fact that Indeed might block requests, which would be useful for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections, front-loading the main purpose, then credits, notes, and return format. It is slightly verbose in the returns section, repeating some field names, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description fully specifies the return structure including the array of job objects and the trailing summary object. It covers input requirements, credit cost, and output format, leaving no major gaps for an AI agent.
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% with a single 'url' parameter. The description adds value by explaining the URL must be a complete Indeed search-results URL and how to construct it, going beyond the schema's basic description.
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 ('Extract job listings') and resource ('Indeed search results URL'), and lists the structured fields returned. It distinguishes itself from sibling scrapers by naming Indeed explicitly and detailing the URL input format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear guidance on building the input URL via Indeed's search UI and notes that it requires a complete search-results URL rather than discrete parameters. Lacks explicit when-not-to-use scenarios or comparisons with other job scrapers, but the context is sufficient.
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 are provided, so the description carries full responsibility. It details mechanics (query-string parameters, method change), credit structure, and that the return value is the raw response body. It also mentions optional parameters like custom headers and wait. However, it does not cover error scenarios or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lengthy and includes some redundancy (e.g., credit structure repeated). It front-loads the core purpose but could be more concise without losing necessary detail.
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?
Given no output schema, the description explains the return value. It covers input parameters, behavior, and credits. It does not discuss error handling or edge cases, but for a 3-parameter tool it provides sufficient context for an agent to use it correctly.
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%, so baseline is 3. The description adds value by explaining that the body is forwarded verbatim, the content_type default, and notes that api_key and url are query parameters (even though api_key is not in the input schema). This goes beyond the schema's 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?
The description clearly states it sends a POST request through Scrapingdog, with specific use cases like form submission or hitting POST-only APIs. It distinguishes itself from the GET variant, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use this tool (for POST requests) and implicitly contrasts with the GET version. It mentions that other /scrape parameters are available, but does not explicitly state when not to use it or provide direct alternatives.
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 are provided, so the description carries the full burden. It discloses credit cost and clarifies parameter behavior (prefix effectively required), but does not mention read-only nature or rate limits. Overall, it provides useful behavioral context beyond basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with purpose. It includes necessary details about credits and return format without unnecessary verbosity. A slight amount of extra text could be trimmed, but overall efficient.
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?
Given that there is no output schema, the description explains the return format (flat array of suggestion objects). All parameters are covered both in schema and description, and the effective requirement for prefix is noted. Fairly complete for a tool of this complexity.
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%, so baseline is 3. The description adds value by explaining that prefix is effectively required, describing the return format, and providing context for parameters like last_prefix and suffix. This goes beyond what the schema provides.
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 clearly states it retrieves keyword suggestions from Amazon's autocomplete feature based on partial search terms. It uses specific verbs and resources, and is distinct from sibling tools like amazon_search and google_autocomplete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it's useful for keyword research and search-driven features, and notes that prefix is effectively required. It implies when to use but does not explicitly state when not to use or name alternatives, though context from sibling tools makes it clear.
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 full burden. It discloses credit costs (1 vs 5 credits), explains domain/country independence, notes no pagination, and outlines return fields. Missing details like error handling or rate limits but adequate for a read tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a main purpose sentence, then notes on credits and domain/country, and a return structure summary. It is informative without being overly verbose, though slightly long due to the return format details.
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?
Given 5 parameters, no annotations, and no output schema, the description provides significant context: return structure (partial), credit costs, domain/country semantics, and no pagination. It could be more complete with error handling but covers the essentials for a product lookup.
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%, baseline is 3. The description adds value by explaining the domain/country interaction and credit cost implications, which go beyond the schema descriptions. This extra context justifies a score of 4.
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 clearly states the tool retrieves comprehensive product data from any Amazon product page using its ASIN. This specific verb+resource+identifier distinguishes it from sibling tools like amazon_offers, amazon_reviews, and amazon_search which focus on specific subsets of data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool (single product lookup) and explains the relationship between domain and country parameters. However, it does not explicitly contrast with sibling tools or state when not to use it, leaving room for improvement.
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?
Since no annotations are provided, the description carries the full burden. It discloses credit costs per country and premium, and explains pagination behavior (page parameter and informational pagination array). It does not mention rate limits, error handling, or side effects, but for a read-only search tool, these are less critical.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear main sentence, credit details in brackets, pagination note, and return format. It is slightly verbose but front-loaded with the primary purpose, making it effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description thoroughly details the return structure with example fields. It covers all necessary aspects of the tool's functionality, including multiple parameters and their effects, making it complete for an agent to use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with each parameter documented. The description adds value beyond the schema by explaining credit cost implications for country and premium, and clarifying that the page parameter starts at 1 and the pagination array is informational.
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 clearly states the tool retrieves search result listings from Amazon for any query, listing specific data points (titles, prices, ratings, etc.) and pagination links. It distinguishes itself from sibling tools like amazon_product, amazon_reviews, etc., which focus on single product details or reviews.
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 includes credit costs and pagination notes but does not explicitly state when to use this tool versus alternatives. The context of sibling tools implies usage, but the description lacks direct guidance on scenarios 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 are provided, so the description carries the full burden. It discloses the credit cost (5 API credits) and the return format, but does not mention failure modes, rate limits, or explicitly state it is read-only. Adequate but not comprehensive.
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 concise, front-loaded with purpose, and every sentence adds value. It includes a note about credits and a sample return structure, all within a few sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the moderate complexity and lack of output schema, the description provides a complete picture: purpose, parameter usage, return format, and cost. The example return structure compensates for the missing output schema.
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?
The description adds value beyond the schema by clarifying that the 'url' parameter must be a fully built search URL with q= parameter. The schema already covers both parameters with 100% coverage, and the description reinforces usage without adding redundancy.
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 clearly states it scrapes Flipkart search result pages and lists the returned fields (titles, prices, etc.). It distinguishes from siblings like flipkart_product by specifying search results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to pass a full pre-built Flipkart search URL via the 'url' parameter, providing clear usage guidance. It does not mention when not to use or list alternatives, but the context is clear.
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 are provided, so the description carries full burden. It discloses the read-only nature (retrieves posts), mentions pagination behavior, and specifies the return structure. No contradictions exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement, notes on prerequisites, pagination, and return format. The credits note is slightly extraneous but not detracting.
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?
Despite no output schema, the description fully explains the return JSON structure (location_details, post_data, next_page_token). It covers data source and pagination, making it complete for a paginated list tool.
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%, but the description adds value by explaining that data_id comes from google_maps_search and next_page_token from previous responses, which is beyond the schema's basic 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?
The description clearly states the verb 'Retrieves' and the resource 'posts and updates published on a Google Business Profile listing', and distinguishes it from sibling tools like google_maps_reviews or google_maps_photos by specifying it's for posts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains how to obtain the required data_id via google_maps_search and that pagination is cursor-based. It provides clear context but does not explicitly state when not to use this tool or list alternatives.
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 are provided, so the description carries full burden. It details credit cost (5 API credits per request), pagination behavior, and return structure (including related_searches, scholar_results, pagination). It does not explicitly state read-only nature, but the context implies it. It is transparent about input-output relations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a one-sentence overview, then adds credits, usage notes, and return format. It is relatively dense but well-organized, avoiding unnecessary repetition. The return schema is provided in lieu of an output schema.
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?
Given 16 parameters and no output schema, the description provides a complete return object definition and explains inter-parameter dependencies. It covers pagination and language filters adequately, making the tool usable without external references.
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%, but the description adds value by clarifying relationships: query becomes optional if cites is provided, cluster cannot be used with query and cites, and IDs from results tie to parameters. This goes beyond individual schema 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?
The description clearly states it 'Searches academic papers and scholarly content on Google Scholar' and lists specific capabilities (citation lookups, author/source helpers, year filters, pagination). This distinguishes it from siblings like google_scholar_author (author details) and google_scholar_cite (citation output).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit notes on how to use IDs from results for other endpoints (e.g., 'id' values feed google_scholar_cite, cluster_id and cites_id are used with cluster and cites parameters). It also explains pagination (0-indexed) and provides return schema. However, it lacks direct comparison with all sibling tools, though the context is clear.
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 are provided, so the description carries full burden. It explains cursor-based pagination, token precedence, credit mention, and the linkage to other tools. Could further disclose rate limits or limitations but is sufficient for basic transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative but slightly verbose with bracketed notes. Every sentence adds value, though the structure could be streamlined by integrating the pagination note into the main flow.
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?
With 3 parameters, no output schema, and no nested objects, the description provides a clear return structure and pagination details. It is complete enough for an AI agent to understand usage, though an explicit return format table would improve it.
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%, and the description adds meaning by noting that 'mauthors' supports query helpers like 'label:', and that 'after_author' takes precedence over 'before_author'. The pagination token retrieval note adds value beyond the schema.
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 specifies a clear verb ('searches') and resource ('academic researcher profiles on Google Scholar by author name'), and distinguishes from sibling tools like google_scholar_author and google_scholar_author_citation by noting that its author_id is used as input to those endpoints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly guides usage by stating that the author_id feeds into google_scholar_author and google_scholar_author_citation endpoints, and explains pagination tokens. However, it does not explicitly contrast with google_scholar (which searches articles) or provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses credit cost, endpoint sharing, absence of premium/webhook params, and lack of published example response. Acknowledges uncertainty about exact field names, providing honest expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with separate sections for credits and notes. Information is relevant, though slightly verbose; could be trimmed without losing clarity.
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?
Given no output schema, description provides reasonable expectations for return fields and acknowledges documentation gaps. Lacks details on error handling or authentication requirements, but adequate for a two-parameter tool.
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% with clear descriptions. Description adds minor context (e.g., id from URL), but does not significantly enhance understanding beyond the schema. Baseline score of 3 applies.
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?
Explicitly states it scrapes LinkedIn company or school profiles by ID. Distinguishes from the sibling person profile scraper by noting the same endpoint but different type parameter. Verb 'scrape' and resource are clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clearly indicates the tool is for company/school profiles and differentiates from the person profile scraper via the type parameter. However, lacks explicit 'when-to-use' versus other siblings like LinkedIn jobs or posts.
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 explains endpoint behavior (shares with search API, job_id triggers detail mode), credits cost, and honestly acknowledges that no example response is published with expected fields but field names not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise (two sentences plus notes) and well-structured: purpose, credits, endpoint hints, parameter source, return expectations. No wasted words, though slightly dense.
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?
Given simple tool (one parameter, no output schema or annotations), description covers purpose, parameter acquisition, endpoint behavior, credits, and expected return fields (with caveat). Lacks error handling details but adequate for scope.
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% with one parameter described. Description adds meaning beyond schema by explaining how to obtain job_id (from search or URL) and its role in triggering detail mode, providing valuable context.
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 action ('Retrieve detailed information') and resource ('specific LinkedIn job posting') with specific details (full description, requirements, company details). It distinguishes from sibling 'linkedin_jobs_search' by noting job_id is typically from a prior search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explains when to use (when job ID is available) and where to obtain job_id (from search or URL). It mentions credits cost and endpoint sharing, but does not explicitly state when not to use or provide alternatives beyond the implied search tool.
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 are provided, so the description takes full responsibility. It discloses the 5-credit cost, shared /jobs endpoint, pagination behavior, and uncertain return format (noting missing documentation). This honesty about lack of example response is transparent, though it could mention rate limits or 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose sentence followed by 'Notes' section for operational details. It is dense with information but avoids unnecessary verbosity. Minor improvement could be to integrate notes into a more flowing structure.
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?
Given 9 parameters, no output schema, and no annotations, the description provides a solid overview of all parameters, cost, endpoint behavior, and expected return fields. It acknowledges documentation gaps. For a complex search tool, it is fairly complete, though missing explicit error states or status codes.
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%, so baseline is 3. The description adds value by explaining parameter interactions (e.g., geoid vs location, search mode trigger) and providing defaults (geoid=92000000). This goes beyond the schema's individual parameter 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?
Description clearly states the tool searches and scrapes LinkedIn job listings with multiple filters (keyword, location, job type, experience level, work model). It distinguishes itself from 'linkedin_job_overview' by noting the shared endpoint and search mode trigger via 'field' without 'job_id'. This is specific and helps the agent differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides context on when to use this tool versus linkedin_job_overview (search mode vs overview) and explains the geoid/location relationship and pagination. It does not explicitly state when not to use it, but the guidance on endpoint sharing and credits implies appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully compensates by disclosing credits cost ('[Credits: 5 API credits per successful request]'), parameter pairing rules, pagination behavior, and the complete return structure including nested fields. This is a comprehensive disclosure of what the tool does and what to expect, exceeding typical expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two sentences) and front-loaded with the core purpose. The second sentence densely packs credits, query type notes, pagination, and return structure, which could be more readable with bullet points or a list. However, it remains within acceptable length and contains no unnecessary fluff.
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?
Given the tool has 7 optional parameters and no output schema, the description covers all essential aspects: search modes, pagination, return fields, and credits. Missing details like error handling or rate limits are minor omissions. Overall, it is complete enough for an agent to use effectively.
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%, meaning all parameters already have descriptions. The description adds context like pairing rules and default for `time_period`, but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate as the description adds modest value (e.g., credit cost mention) but does not fundamentally elevate parameter semantics.
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 clearly states the tool's purpose: 'Searches and extracts ad listings from TikTok's Ad Library by keyword or advertiser ID'. The verb 'searches and extracts' paired with the specific resource 'ad listings from TikTok's Ad Library' leaves no ambiguity. It distinguishes itself from sibling tools (e.g., google_ads_transparency, tiktok_post) by focusing on TikTok's Ad Library with specific parameter modes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains how to use the two query modes ('query_type=1 (default) pairs with `query`; query_type=2 pairs with `advertiser_id`') and how to paginate ('Pagination is handled via next_page_token echoed in the response'). While it doesn't explicitly state when not to use the tool, the context is clear enough for an agent to decide. A slightly higher score would require explicit exclusions or alternatives.
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 are provided, so the description carries full burden. It discloses the credit cost, shares the /youtube endpoint with other tools, and explains that the presence of `v` selects this behavior. It also describes the return format. It does not mention destructive actions, which is appropriate for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly concise and front-loaded with the main purpose. It includes necessary operational notes (credits, endpoint sharing, verified live date, return format) without being overly verbose. Minor improvements could remove redundant info like the date.
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?
Given no output schema, the description describes the return format. It also addresses the endpoint nuance and credit cost. It does not cover error conditions or prerequisites, but for a simple transcript extraction, it is fairly complete and distinguishable from sibling tools.
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%, so baseline is 3. The description adds value by explaining that `v` selects the Transcripts behavior on the shared endpoint and provides an example of how to find the video ID. This enhances understanding beyond the schema 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?
The description clearly states the tool extracts the complete transcript from a YouTube video as an array of text segments with start time and duration. The verb 'extracts' and resource 'transcript' are specific, and it distinguishes itself from sibling tools like youtube_search, youtube_comments, and youtube_video.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is the cheapest YouTube endpoint at 1 credit and explains how the `v` parameter selects this behavior on the shared /youtube endpoint. It also notes the endpoint discrepancy (docs show bare /youtube but working endpoint is /youtube/transcripts). However, it does not explicitly say when not to use it, though the context with sibling tools makes it clear.
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, the description fully discloses credits, pagination behavior, patent_id format, and return structure. It does not mention rate limits or error handling, but provides sufficient behavioral details for an AI agent to use the tool correctly.
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 well-structured: main purpose in first sentence, credits, then key behavioral notes, return format. Every sentence adds value without redundancy. It is front-loaded with essential information.
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?
Given 17 parameters, no output schema, the description covers return structure and key behavioral notes. It explains how to use results with a sibling tool, but could mention more about error states or edge cases for completeness.
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%, so the baseline is 3. The description adds significant value by explaining date format (type:YYYYMMDD), semicolon usage for multiple query terms, and the patent_id format conversion, which are not fully covered in the schema 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?
The description clearly states the tool searches patent records across Google Patents with advanced filtering, specifying the verb (Searches) and resource (patent records). It differentiates from sibling tool google_patent_details by noting the patent_id format difference and how to pass parameters to that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context on pagination, date format, and how to use results with google_patent_details, but does not explicitly state when to use this tool versus alternatives like google_scholar or other search tools. The context is clear but lacks explicit exclusions.
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 exist, so the description must fully disclose behavior. It does so by describing the read-only retrieval operation, the return structure, and the required parameters. It could mention rate limits or authentication, but for a simple data retrieval tool, it's sufficiently transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is multi-sentence but each sentence adds necessary information: purpose, endpoint note, citation_id format, and output structure. It is front-loaded with the core action. Slightly verbose but not wasteful.
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?
Given no output schema, the description provides a detailed breakdown of the return structure. It covers all required parameters and their interrelation. Missing error handling or edge cases, but sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions, but the description adds significant value by explaining the citation_id format (author_id:opaque_token) and its source (from google_scholar_author's articles). This extra context helps the agent use the parameters correctly.
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 clearly states the tool retrieves citation metrics (h-index, i10-index, yearly graph) and article citation details, using view_op=view_citation on the author endpoint. This distinguishes it from siblings like google_scholar_author (profile retrieval) and google_scholar_cite (citation format).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that citation_id is required and obtainable from google_scholar_author, and points to an ambiguity note about the shared endpoint. While it doesn't explicitly list when not to use, it provides enough context to avoid confusion with similar tools.
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 burden. It discloses that the tool requires a specific ID, returns citations and links, and mentions credits not stated. It does not mention auth or rate limits, but as a read-only citation retrieval, this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose, followed by notes and return structure. It is slightly long but every sentence provides value. Could be slightly more concise but overall well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description includes a clear return structure. It provides all necessary information to use the tool correctly, including parameter clarification, return format, and context from sibling tools.
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% with both parameters described. The description adds critical value by clarifying that 'query' is actually a result ID and referencing the id field from google_scholar results, and it explains the 'language' parameter default.
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 clearly states it retrieves formatted academic citations (MLA, APA, etc.) and export links using a Google Scholar result ID. This uniquely identifies its function and distinguishes it from sibling tools like google_scholar (search) and google_scholar_author (author info).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It specifies when to use (to get citations for a paper given its result ID) and clarifies that the 'query' parameter is a result ID, not free text. However, it does not explicitly mention when not to use or provide alternatives, though the sibling context implies its narrow use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden; it discloses credit cost, pagination, filter flow, and return structure effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is front-loaded with purpose and includes necessary notes, though could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers return structure, pagination, filtering, and credits. No output schema, but provides sufficient detail for a complex scraping tool.
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%, so baseline is 3. Description adds some context for shoprs and page but doesn't significantly enhance meaning beyond schema.
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 clearly states it scrapes Google Shopping search results including ads, listing, and filter facets. It distinguishes from siblings like bing_shopping or google_search by focusing on Google Shopping.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides pagination guidance and two-step filter flow, but lacks explicit alternatives or when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: it mentions credit cost (5 per request), explains input format, notes no pagination, and provides a detailed return structure. This gives the agent clear expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the main purpose. It includes credits, notes, and return structure in a single paragraph. Minor improvement could be more structured formatting, but it is effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema), the description covers all necessary aspects: purpose, input, behavior, cost, and return data. No gaps are evident.
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?
The single parameter 'url' is well-described in the schema. The description adds context that it must be a full product page URL (not an ID), which is helpful but not critical. With 100% schema coverage, the description adds moderate value.
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 clearly states the tool scrapes Walmart product pages via URL and lists the data retrieved (title, price, images, etc.). It distinguishes from sibling tools like walmart_reviews, walmart_search, and walmart_autocomplete, which handle different functions.
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 notes that a full product page URL is needed (not a product ID) and that localization is implicit, but it does not explicitly state when to use this tool versus alternatives. Usage context is implied but not directive.
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. Discloses endpoint sharing, ID format requirement, and provides detailed return structure. Missing behavioral details like error handling, rate limits, or consequences of invalid input, but the return structure adds significant transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is front-loaded with main action ('Scrapes comprehensive YouTube channel data') followed by credits, endpoint note, ID requirement, and return structure. Fairly concise but includes a long JSON example; could be slightly tighter but still well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description provides a full, nested return structure with field details, credits, and endpoint notes. This compensates for the missing schema and gives complete context for an agent to understand what the tool returns and how to use it.
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%, so baseline is 3. Description adds value by clarifying that channel_id must be UC-prefixed (not a handle) and that it selects Channel behavior. Also notes defaults for country and language from schema, though no major extra beyond schema.
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 it scrapes comprehensive YouTube channel data including about info, subscriber counts, video sections, and Shorts. Distinguishes from sibling tools like youtube_search and youtube_video by focusing on channel data, and specifies it requires UC-prefixed channel ID, not a handle.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: requires UC-prefixed channel ID, presence of channel_id selects Channel behavior on shared /youtube endpoint, and credits cost. However, does not explicitly state when not to use this tool versus alternatives like youtube_search or youtube_video, nor mention exclusions or prerequisites beyond the ID format.
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?
Discloses credit cost per request and describes the return structure in detail. No annotations provided, so description carries full burden; it adequately informs the agent about behavior (a read scrape with cost implications).
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?
Efficient use of three sentences plus structured Notes and Returns sections. No redundancy; every sentence adds value. Front-loaded with purpose and cost, then usage details, then output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description provides a detailed schema for the return object. Covers credit cost, URL construction, and parameter semantics. Fully adequate for a simple two-parameter tool.
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 description coverage is 100%, but description adds practical context: explains that url must be a full pre-built search URL, gives example, and clarifies that domain sets locale. This goes beyond what the schema offers.
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?
States specific verb ('scrape'), resource ('eBay search result pages'), and what is returned (product titles, item IDs, etc.). Clearly distinguishes from sibling tools like ebay_product by referring to search pages, not product pages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to pass a full eBay search URL and explains how to build it using eBay's filters. Notes that the domain determines locale. Lacks explicit when-not-to-use or alternatives, but guidance is strong and actionable.
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, the description carries full burden. It discloses API credit cost (5 per request), pagination mechanism, endpoint path quirk, and return structure. While it omits auth or rate limits, it covers key behavioral aspects well.
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?
Description is efficiently structured: front-loaded purpose, followed by usage notes, pagination, endpoint correction, and return schema. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 11 parameters, no annotations, and no output schema, the description is remarkably complete. It explains alternative parameter groups, pagination, cost, and return format. Users have enough to use tool correctly without external docs.
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% (all params described). The description adds value beyond schema by explaining usage relationships (advertiser_id vs text, political_ads+region), pagination token origin, and endpoint path note, raising it above baseline 3.
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 clearly states it pulls ad data from Google Ads Transparency Center, specifying lookup methods (advertiser ID or keyword/domain) and multiple filters. It distinguishes itself from sibling Google scraping tools by focusing on ad transparency, making purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage conditions: provide either advertiser_id or text, political_ads requires region, pagination via next_page_token. It lacks explicit comparison to siblings, but given no direct competition, this is sufficient.
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, the description carries full burden. It details return format, pagination behavior (requires ll), and notes that each result contains follow-up links. It does not mention any destructive or auth-related behavior, but the tool appears read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose first, then notes, then return format. It is somewhat long but flows logically, and every sentence adds value. Could be slightly more terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
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 details the return JSON structure, pagination, and parameter interactions. It addresses complex scenarios like using place_id independently and pagination requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds significant context: format details for ll and data, pagination increments, independent use of place_id, and domain/country/language parameter usage.
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 explicitly states it returns Google Maps business listings including rating, reviews, address, etc. for a search query or specific place. It distinguishes itself from siblings (google_maps_photos, etc.) by noting that it provides follow-up links to those endpoints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (search query or place_id), and includes pagination requirements. It implies when to use sibling tools via the follow-up links, but does not explicitly state when not to use this tool or provide alternative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it returns a specific result structure, notes undocumented 'story_token' concept, warns about ambiguity, and states lack of pagination parameter. This level of transparency is exemplary for a potentially complex endpoint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with key information front-loaded (improvements, search modes). However, it includes a superfluous [Credits] line and the Notes section is verbose, slightly hindering conciseness.
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?
The description covers return format, parameter constraints, undocumented features, and differences from v1. It lacks explicit mention of error handling or rate limits, but given the tool's complexity, it is largely complete and helpful for an agent.
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 description coverage is 100%, but the description adds value by explaining token concepts, mutual exclusivity, and the undocumented story_token. It provides high-level context beyond the schema, justifying a score above baseline 3.
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 clearly states it's a faster v2 endpoint for Google News, highlighting key improvements (image URLs, ISO dates). It distinguishes from v1 and explains the different search modes (topic, publication, section tokens vs free-text query), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool (v2 for faster results, image URLs, ISO dates) and explains mutual exclusivity among parameters. However, it does not explicitly compare to sibling google_news_search or state when to prefer v1, leaving some ambiguity for the agent.
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?
Discloses credit cost (5 API credits) and return format. With no annotations, the description carries the burden well, though it could mention rate limits or potential errors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Detailed but slightly verbose; all sentences are useful. Could be trimmed by merging some notes, but overall well-structured with key info up front.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Fully covers the tool's functionality: input, behavior, return format. Even provides example return structure. No output schema needed; description is self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single 'url' parameter is well-explained: shows how to construct the URL and notes pagination. Schema coverage is 100%, and the description adds value beyond the schema.
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 clearly states it scrapes Walmart search results and lists returned fields (titles, prices, ratings, etc.). It distinguishes from sibling tools like walmart_product and walmart_reviews by focusing on search pages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear instructions: pass a Walmart search URL, handle pagination via URL parameters. However, it lacks explicit guidance on when to use this vs. other search tools (e.g., google_shopping) or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: credits cost, live-verified endpoint (/youtube/video), parameter name must be v (not video_id), full return structure, and default values. 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose, containing a full return JSON in text, verification notes, and credits info. While front-loaded with purpose, the length and unstructured details hurt conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description compensates thoroughly: includes endpoint verification, parameter differences, default values, and a complete return object shape. All 3 parameters are explained. Very complete for a scraping tool.
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%, baselining at 3. The description adds significant value: clarifies that v is the video ID from URL, warns against using video_id, and explains country/language defaults. The live verification note is extra context.
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 clearly states it scrapes detailed metadata for any YouTube video, listing specific fields (title, views, likes, description, etc.). It distinguishes from sibling tools by noting the differing parameter name for video metadata vs. transcripts/comments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
While it doesn't give explicit when-to-use/when-not-to-use statements, it contrasts with transcript and comment tools by pointing out the parameter difference. This implies its usage for video metadata, but lacks clear alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: credit cost (5 API credits), date format (YYYY-MM-DD), filter mutual exclusivity between hotels and vacation rentals, pagination via next_page_token, and detail lookup via property_token. Example return structure is also included.
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 paragraph front-loaded with purpose, then credit cost, then parameter constraints, then pagination, then return structure. Every sentence adds value with no redundancy.
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?
Given 26 parameters, 3 required, no output schema, the description covers core flows (search and detail lookup), filter constraints, pagination, and sample output. Could be enhanced with rate limit info, but overall adequate for agent understanding.
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% with parameter descriptions. The description adds context beyond schema: mutual exclusivity of certain filters, credit cost, pagination mechanism, and property_token usage. This justifies a score above the baseline of 3.
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 retrieves Google Hotels search results including property listings, pricing, ratings, amenities, and detailed property info via property_token. The verb 'retrieves' and specific resource 'Google Hotels search results' make the purpose unambiguous and distinguishable from siblings like google_search or google_flights.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides usage guidelines like credit cost, date format, and filter compatibility notes (e.g., hotels filters not supported for vacation rentals). It explains pagination and property token usage. However, it does not explicitly contrast with alternative tools like google_search for general hotel queries.
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 full burden. It explains the two modes (default profile vs list_colleagues) and the ambiguity with the sibling endpoint, and provides a return structure. It could mention authentication or rate limits but is still clear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured well with a clear main purpose followed by notes. The ambiguity note is lengthy but important. Front-loaded with core functionality. Could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description provides a complete return structure and covers all parameters and modes. It addresses the sibling tool relationship and potential confusion, making it fully informative for an AI agent.
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%, so each parameter is described. The description adds context beyond schema by explaining the behavioral differences of view_op values and the relationship between author_id and citation_id. However, it repeats some schema info.
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 clearly states it retrieves comprehensive author information (name, affiliation, email, publication/citation history) and also exposes a co-author list mode. It explicitly distinguishes from the sibling tool 'google_scholar_author_citation' by explaining the ambiguity and the two modes of the same endpoint.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus the citation tool, including the note about potential merging. It also mentions how to obtain the author_id from the profiles tool and prerequisites for using citation_id.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses credits cost, endpoint verification, endpoint discrepancy (docs vs working), and detailed response structure including pagination tokens. Fully transparent about behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is structured with clear sections (credits, notes, endpoint note, returns) but is somewhat lengthy. Could omit some details or condense, but still effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must cover return format. It does so comprehensively with field names and types. Endpoint and pagination details are included. Complete for a 4-parameter tool.
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%, baseline 3. Description adds value by explaining next_page_token usage for paginating both comments and replies, and notes defaults for country/language. Exceeds baseline.
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 clearly states the tool scrapes comments from YouTube videos, listing returned fields and pagination support. It distinguishes from sibling tools like youtube_transcripts and youtube_video by noting endpoint sharing and behavior differences.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on when to use (comments) and indicates differentiation from transcripts/video via next_page_token support and response shape. Implicitly advises against using for other YouTube data types, though explicit when-not-to-use is lacking.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description compensates fully. It discloses credit cost (5 API credits), pagination behavior (offset-based via pn/rn), localization (ct parameter), advanced filters (mimicking Baidu operators), and the ability to return HTML instead of parsed JSON. This is rich behavioral context.
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 concise (4 sentences) and well-structured: purpose, cost, pagination, localization, advanced filters, return format. Every sentence provides essential information with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description specifies the exact return format (Baidu_data array with title, link, snippet, rank). With 11 parameters and complex behavior (pagination, localization, operators), the description covers all key aspects, making it complete for selecting and invoking the tool.
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 description coverage is 100%, so each parameter has a description. The tool description adds value by grouping parameters (q5, q6, bs, oq, f, gpc) as 'advanced filters' and explaining their combined purpose, and clarifying pagination semantics. This synthesizes usage context beyond individual schema 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?
The description clearly states 'Scrape Baidu search engine results pages' with specific verb+resource, and supports Baidu-specific search operators. It distinguishes itself from sibling search tools (e.g., google_search, bing_search) by being Baidu-specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context on when to use the tool (for scraping Baidu results) and details pagination/localization settings, but does not explicitly state when not to use it or compare to alternatives. The sibling list includes many search tools, so the name and description sufficiently differentiate it.
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?
Discloses cost, single-use and time-limited nature, and return format. No annotations provided, so description carries full burden. Missing failure modes for expired URLs.
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 plus concise notes on credits and output. Front-loaded with purpose, minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given single parameter and no output schema, description details when, why, how, cost, limitations, and expected JSON structure. Very complete.
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% coverage with a description. Description adds context about the URL source and expiration, enhancing understanding beyond schema.
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 it fetches Google AI Overview using a follow-up URL from Google Search API. Distinguishes from sibling tools like google_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (only when primary response indicates secondary fetch), notes URL is single-use and time-limited (2 minutes), and provides credit cost.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden of disclosing behavior. It explains pagination (cursor-based via next_page_token), the exception for results param on first page, and the detailed return structure including locationDetails, topics, reviews_results, and pagination.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat lengthy but well-structured, front-loading the main purpose and then detailing nuances. Every sentence adds value, though a slight trim could improve conciseness without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters and no output schema, the description provides a thorough explanation of inputs, behavior, and return fields. It covers edge cases, pagination, and the structure of the response, making it fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant meaning beyond the schema: it explains the origin of topic_id, the edge case for results on first page, and the meaning of next_page_token. This helps the agent understand parameter usage beyond syntax.
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 clearly states it retrieves customer reviews for a Google Maps location given a data_id. It uses specific verb 'retrieves' and resource 'customer reviews', distinguishing it from sibling tools like google_maps_search or google_maps_photos.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that data_id comes from google_maps_search and topic_id from a prior response, providing context for usage. It does not explicitly list when not to use or direct alternatives, but the purpose is clear among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavior: credits per request, required URL structure (numeric ID before /buy), and detailed return format including optional HTML output and nested fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose and includes a detailed return schema. While the return schema is lengthy, it is valuable for an agent. Minor verbosity could be trimmed but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description enumerates the full return structure including nested objects (sizes, offers, reviews). It also covers the URL format requirement, making it complete for proper tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds significant meaning beyond the schema: it clarifies the url must be a product page with a numeric ID, and explains the html parameter returns full HTML instead of parsed JSON.
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 clearly states it scrapes a Myntra product page by URL to retrieve product details, distinguishing it from sibling tools like myntra_search which handles search queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies to use this tool for product page URLs, includes credit cost, and explains URL format. While not explicitly excluding alternatives, the context of siblings (e.g., myntra_search) makes the usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Very detailed credit costs, session reuse, CAPTCHA bypass, JavaScript rendering, and undocumented features disclosed. No annotations present, so description carries full burden and exceeds expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with main purpose first, then details. Slightly verbose in credit explanation but organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive coverage of behavior, all parameters, return value, and limitations. No output schema but description explains raw HTML response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds significant meaning beyond schema: credit costs, defaults, parameter interactions (e.g., premium+dynamic). Schema coverage is 100% but description adds crucial operational context.
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?
Describes scraping any public webpage with specific verb and resource. Distinguishes from sibling tools which are specific site scrapers or search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear context: 'Scrapes any public webpage' implies general purpose. No explicit when-not or alternatives, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses credit costs, pagination inconsistencies (0-based sequential vs. 'start' offset), response shape dynamics (only present SERP blocks), mutual exclusivity of location/uule, and parameter overrides. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is long but well-structured: core purpose first, then credits, then pagination clarifications, then parameter notes. Every sentence adds value; minimal redundancy. Slightly verbose but does not waste words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 23 parameters, no output schema, and no nested objects, description is remarkably thorough. Covers credits, pagination behavior, response shape, parameter interactions, and edge cases. Agent has all needed information for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (baseline 3), but description adds significant value beyond schema: pagination inconsistency note for 'page', credit cost for 'advance_search'/'mob_search', mutual exclusivity for 'location'/'uule', override semantics for 'kgmid'/'si', and usage example for 'ibp'. Elevates agent understanding.
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 verb 'scrapes' and resource 'Google Search results' (organic, ads, AI overview, etc.), distinguishing it from sibling tools like bing_search or duckduckgo_search. Specificity about SERP features and credits ensures unambiguous purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explicitly says when to use (scraping Google results without proxy management) and provides usage guidance: credits for standard vs. advanced/mobile search, mutual exclusivity of location/uule, pagination nuance, and override behavior of kgmid/si. Implicitly suggests alternatives via sibling list.
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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