The Mine Works

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses an important behavioral trait: 'No GitHub token required,' indicating authentication is not needed. It also outlines the returned metadata. However, it does not mention potential limitations like rate limits or maximum number of repos per call, 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences. The first enumerates the data points, the second gives a use case and auth info. Every word serves a purpose, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with two parameters and no output schema, the description provides a comprehensive list of returned fields and a usage context. It could mention the return format or processing behavior for multiple repos, but overall it is sufficiently complete for an AI agent to understand the tool's capabilities.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers both parameters with descriptions (100% coverage), so the baseline is 3. The description adds no extra meaning beyond the schema; it merely restates that README content is part of the returned metadata. No additional parameter guidance is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides deep metadata for public GitHub repositories, listing specific data points like stars, forks, language, etc. It uniquely identifies the tool's purpose among siblings, as no other sibling tool focuses on GitHub repository analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a use case ('Use to see what a company or project is actually building'), providing clear context for when to use. However, it does not explicitly mention when not to use or directly compare with alternatives, though the sibling list implies its distinct scope.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'guessed business emails,' indicating potential inaccuracy, but lacks information about rate limits, data freshness, source reliability, or output volume. The description does not disclose destructive or read-only nature, which is critical for a tool that scrapes or guesses data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, highly efficient, and front-loaded with the core purpose. Every word adds value—nothing is superfluous. It achieves maximum clarity with minimal length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description should provide more context about return values, error handling, or limitations. It only hints at output types (profiles, titles, emails) but does not specify structure, pagination, or success criteria. For a tool with three parameters and no output schema, this is insufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds general context (e.g., 'target companies,' 'job titles') but does not provide additional meaning or format details beyond the schema. Baseline score of 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool finds B2B leads with LinkedIn profiles, job titles, and guessed business emails. It positions itself as an alternative to Apollo and ZoomInfo and notes 'No API key required.' While it does not explicitly compare to sibling tools like find_linkedin_candidates, the unique offering of guessed emails and free access provides some differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal usage guidance. It implies the tool is for lead generation and is free, but it does not specify when to use this versus sibling tools such as search_linkedin_employees or find_linkedin_candidates. There is no mention of when not to use it or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description notes 'No login required,' which is a useful behavioral trait. However, it does not disclose other important details such as rate limits, data freshness, pagination, or handling of empty results. With no annotations, the description carries the full burden but covers only minimal 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: first states purpose and criteria, second lists output and login requirement. No wasted words, efficiently communicates core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers basic functionality and output fields, but lacks guidance on usage boundaries, error cases, or limitations. Given 7 parameters, no output schema, and no annotations, the description is adequate but leaves gaps in completeness (e.g., ordering, pagination, match confidence details).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description maps to parameters (role, skills, location, experience band, target companies), adding the concept of 'experience band' for min/max years. However, it does not elaborate on parameter formats, constraints, or defaults beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds LinkedIn profiles matching specified criteria (role, skills, location, experience, target companies). It identifies the verb 'find' and resource 'LinkedIn profiles'. However, it does not differentiate from sibling tools like search_linkedin_employees or get_linkedin_profiles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The phrase 'Recruiter sourcing' implies a use case, but no when-not-to-use or alternative suggestions are provided. Sibling tools with similar scope exist without distinction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It mentions crawling and extraction without addressing rate limits, data freshness, error handling, or JavaScript rendering. The claim 'No API key required' is a requirement, not behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with key information, no redundancy. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 2 parameters and no output schema, the description is mostly adequate but lacks behavioral details. It covers the core functionality well but omits important context like output format or pagination behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context on which pages are crawled, but parameter semantics are already adequately conveyed by the schema (e.g., 'Domains or URLs to crawl'). No additional meaning beyond schema is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (crawl and extract) and the resource (list of domains), specifying the extracted data types (emails, phones, social profile links) and target pages (homepages, contact, about pages). It distinguishes from sibling tools which focus on different data sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives like scrape_reddit or search_google_maps. The description does not mention prerequisites, limitations, 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavior. It mentions 'resolved best-effort' but does not detail rate limits, authentication requirements, error handling, or whether results are read-only. The behavioral transparency is insufficient given the lack of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the key output fields, then explains input format. Every word adds value; no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description covers return fields and input formats. It could mention limitations like batch size or error behavior, but the tool has low complexity, so it is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter has 100% schema description coverage. The description reinforces the schema by mentioning both URL and name formats. However, it adds minimal new meaning beyond the schema—'resolved best-effort' is a minor addition. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets LinkedIn company details and lists specific fields (employee count, industry, website, etc.). It also indicates accepted input types (URLs or names), distinguishing it from sibling tools like get_linkedin_profiles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains acceptable inputs but does not provide explicit guidance on when to use this tool versus alternatives (e.g., search_linkedin_employees for employee lists). No when-not-to-use or exclusion criteria are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must disclose behavior. It mentions 'no login or cookies required' indicating ease of access. However, it does not address rate limits, error handling for invalid URLs, data freshness, or whether only public data is returned. Some gaps exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single concise sentence front-loads the action and resource, then lists key return fields and the benefit of no login. Every part adds value; no wasted words. Could be slightly more structured, but very efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description lists some returned fields (experience, education, skills, headline, location) but does not explain full structure, handling of multiple URLs, or error scenarios. Basic completeness for a simple fetch tool, but could improve.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage for the single parameter 'profileUrls' with an example format. The tool description does not add much beyond the schema; it repeats the idea of fetching by URL but does not elaborate on format variations or batch behavior. Baseline 3 due to high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Fetch full LinkedIn profile details by profile URL' and lists specific fields (experience, education, skills, headline, location). Distinguishes from sibling tools like get_linkedin_company and search_linkedin_employees by focusing on individual profile URLs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies use case (fetching profile details) and mentions no login/cookies, but does not explicitly state when to use this tool over alternatives or when not to use it (e.g., for company profiles or searching for candidates). Lacks explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits. It reveals that the tool may return a pending status and that results may not be immediate, requiring repeated calls. However, it does not mention error handling, failure scenarios, or rate limits, limiting transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first defines the purpose, the second provides usage instructions. It is front-loaded and contains no extraneous words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (one parameter, polling behavior), the description covers basic purpose and usage. However, it lacks information about the return format or status codes when results are not ready. Since there is no output schema, this gap reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the parameter description already explains it is a run_id from a pending tool. The description adds value by explaining the usage pattern (polling), which enhances the semantic context beyond the schema. The agent learns that the parameter is used in a loop.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Collect the results of a previously started actor run.' It uses a specific verb (Collect) and resource (results of actor run), and distinguishes from sibling tools which are primarily data gathering tools, making it clear this is a result retrieval/polling tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use instructions: 'Use this when another tool returned status "pending" with a run_id.' It also gives a clear procedure: wait a few seconds, call with run_id, repeat until results. This fully guides the agent on how to employ the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 describes what data is fetched but does not disclose behavioral traits such as rate limits, authentication requirements, or error handling for invalid URLs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that front-loads the primary purpose and includes specific data points, making it efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a relatively simple tool with one parameter and no output schema, the description covers the core purpose and parameter usage. However, it lacks some behavioral context that would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description does not add meaningful information beyond what the schema already provides. The schema's description of 'urls' is essentially identical to the description's mention of 'listing URLs or ZPIDs'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it fetches deep Zillow property data and lists specific data points like price history, tax history, and school ratings. It also mentions chaining off search_zillow output, which distinguishes it from sibling tools like search_zillow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by noting it chains off search_zillow output, but it does not explicitly state when to use this tool versus alternatives 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description carries full burden. It states it returns specific data (LEI code, status, address) and is authoritative. However, it does not mention whether the operation is read-only, if there are rate limits, or any prerequisites. 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that front-load the action and outcome. No unnecessary words; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description effectively explains return values (LEI code, registration status, legal address, entity status). It covers the essential aspects for a lookup tool but could explicitly state it's read-only for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. Description adds no extra meaning beyond the schema; it mentions 'legal name' which corresponds to legalNames but provides no additional detail for matchesPerName or constraints on inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it looks up Legal Entity Identifier from the official GLEIF registry by legal name, a specific verb+resource. This distinguishes it from sibling tools like get_linkedin_company or resolve_company_identity, which have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context: use to verify a company legally exists via authoritative source. However, no explicit when-not-to-use or alternatives are mentioned, though it is implied by the unique function.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool resolves company names to authoritative identifiers and lists the returned fields, but does not mention important traits such as behavior on missing names, rate limits, or authentication requirements. This is adequate 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the key information (verb, resource, outputs) in the first sentence, and provides usage context in the second. Every sentence adds value with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description does a good job listing the identifiers returned and giving a usage context. It could be more complete by addressing potential error scenarios or batch behavior, but overall it is sufficient for an agent to understand the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters. The description adds minimal meaning beyond what the schema provides, as it merely restates the function and lists outputs. The baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'resolve' and the resource 'company name to authoritative identifiers across registries'. It lists specific outputs (GLEIF LEI, legal name, country, entity status, SEC EDGAR CIK plus last filing date), distinguishing it from siblings like lookup_lei and search_sec_filings by emphasizing the bundling of multiple registries in one call.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a usage recommendation ('Use to ground company facts before trusting them') but lacks explicit guidance on when not to use this tool or how it compares to alternatives like lookup_lei or search_sec_filings. The context is implied but not elaborated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description covers basic behavior (no auth, public APIs). It does not disclose potential rate limits, error handling, or whether all companies are supported, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the core purpose, and contains no unnecessary words. It is efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description omits details about return format or limitations, which would be helpful given no output schema. While functional, it could be more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema coverage is 100%, and the description adds value by providing examples for the 'company' parameter and listing supported platforms for 'platform'. This goes beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scrapes job listings from specific ATS boards (Greenhouse, Lever, Workday, Ashby), providing a specific verb and resource. It distinguishes itself from sibling tools that target different sources (e.g., search_linkedin_jobs).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly notes 'No auth required' and specifies it uses public job APIs, giving clear context for when to use this tool. However, it does not explicitly compare to alternatives or state 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It only states it returns 'full post data with comment trees', but omits behavioral details like rate limits, authentication requirements, error handling, or whether it respects Reddit's API policies.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise, front-loaded sentences. Every word adds value. No redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity (6 params, 4 modes) and no output schema, the description is minimally adequate. It states return type but lacks guidance on usage context, error scenarios, or edge cases. Could benefit from mentioning output structure or limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific information beyond what the schema already provides. The enum for mode is described in schema, and description only restates some modes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: scrape Reddit posts and comments by various modes. It specifies the verb ('scrape'), resource ('Reddit posts and comments'), and methods ('subreddit, keyword search, or post URL'), distinguishing it from sibling tools which focus on other platforms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives (e.g., when to use scrape_reddit vs search or other scraping tools). The different modes are implied but not explained in terms of use cases or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description should disclose behavioral traits. It explains what is scraped but omits details like rate limiting, authentication requirements, IP blocking risks, or pagination behavior. The listed return fields are helpful but insufficient for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no wasted words. Front-loaded with action and resource, then immediately lists output fields. Efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description adequately lists return fields. However, it lacks usage guidelines and behavioral context. For a scraping tool, missing information on reliability, source, and constraints reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and parameters are well-described in the schema. Description does not add significant meaning beyond what the schema provides for 'companyUrl' and 'maxResults'. The mention of return fields is about output, not parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'scrape', resource 'Trustpilot reviews', and explicitly lists returned fields ('reviewer name, star rating, review title, body, date, and verified status'). It distinguishes from siblings like 'scrape_reddit' and 'scrape_ats_jobs' by specifying the target platform.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description only says 'for any company' without context on prerequisites, limitations, or scenarios where other tools might be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears the full burden of behavioral disclosure. It only describes the function and output, but does not disclose any behavioral traits such as rate limits, authentication requirements, or impact on data sources.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two concise, front-loaded sentences with no unnecessary words. Every sentence adds value: the first states the action and returns, the second provides context for use.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no output schema, and no annotations, the description adequately covers the purpose, return content, and best-use domains. It lacks details on pagination, sorting, or error handling, but the core information is sufficient for basic usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds minimal extra meaning beyond the schema, mainly repeating the keyword/category concept. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Search arXiv preprints') and the resource ('arXiv preprints'), specifies the returned fields (title, abstract, authors, PDF link), and distinguishes from sibling tools by mentioning the domains it is best for (AI, CS, physics, biology).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context ('Best for AI, CS, physics, and biology research'), helping the agent decide when to use this tool. However, it does not explicitly mention when not to use it or suggest alternative tools for other domains.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the data fields returned and the need for an API key, but doesn't mention rate limits, pagination, behavior when both query and companyNumbers are provided, or potential performance hits from includeOfficers. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first conveys the purpose and result fields, the second the API key prerequisite. Every word serves a purpose, with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description covers the essential purpose, data fields, and authentication requirement. It doesn't explain edge cases or response structure, but for a straightforward search tool with 5 well-documented parameters, it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the schema descriptions are already detailed. The description adds no new parameter-specific meaning beyond confirming what fields are returned, so it doesn't go beyond the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search or look up UK companies on the official Companies House register' and enumerates specific data returned (status, type, incorporation date, registered office, SIC codes, optionally officers). This specific verb+resource combination distinguishes it from sibling tools like search_crunchbase or lookup_lei.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that this tool is for UK company data from the official register, and notes the API key requirement. While it doesn't explicitly name alternatives or exclusion criteria, the strong UK-specific framing implicitly signals when this tool should be selected over broader company search tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the burden. It describes the tool as a lookup (read-only), but does not mention authentication, rate limits, or other constraints. The phrase 'look up' implies non-destructive behavior, but without explicit safety cues, the transparency is only adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two sentences), front-loads the action and data, and contains no redundant or unclear language.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description helpfully lists the returned fields (funding, investors, etc.). It lacks details on error handling or pagination, but for a straightforward search tool, the coverage is good.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description restates parameter purposes ('Company name to search', 'Max companies') but adds no additional meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a clear verb-resource pair ('Look up company funding and firmographic data on Crunchbase') and specifies the output fields (total funding, last round, etc.). It distinguishes well from sibling tools like get_linkedin_company or search_sec_filings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description suggests a concrete use case ('Use to check whether a company is actually growing'), giving implicit guidance. However, it does not explicitly state when not to use this tool or mention alternatives, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the transparency burden. It discloses that the tool uses the official OpenFEC API and is a search operation, but it does not mention rate limits, authentication fallback, or error behavior. However, the schema's parameter descriptions (apiKey fallback, maxResults) cover some of these aspects, and the description adds the 'official' API context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that immediately identifies the tool's domain and enumerates the three modes. It is concise, free of redundancy, and every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides a clear overview of the tool's capabilities, and the schema fills in parameter details. While it doesn't describe return values, the simplicity of a search tool and the absence of an output schema make this adequate. It would benefit from mentioning the need for an API key, but the schema covers that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaningful domain context by labeling contributions as 'itemized Schedule A' and specifying 'for one committee,' which clarifies the committeeId requirement beyond the schema's description. This extra context justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Search') with a specific resource ('US federal campaign finance via the official OpenFEC API') and enumerates three distinct search modes (candidates, committees/PACs, Schedule A contributions). This clearly distinguishes it from sibling search tools with different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states the tool is for US federal campaign finance, providing clear context for when to use it. There are no sibling tools with similar functionality, so no explicit alternatives are needed. The schema further clarifies mode-specific requirements (e.g., committeeId for contributions mode).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description carries full burden. It discloses that no authentication is required ('No login, no Google Maps API key') and lists return fields. Missing details on rate limits or error handling, but adequate for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no redundancy. First sentence states purpose and return types; second adds key usage note (no auth). Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a straightforward search tool with 3 parameters and no output schema, the description covers purpose, return fields, and a notable usage point. Lacks sorting or pagination details, but remains fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds the context 'No login, no Google Maps API key' but does not enhance parameter meanings beyond the schema descriptions. For example, 'maxResults' default is mentioned but no additional guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'Search' and resource 'Google Maps business listings', listing specific return fields. Distinguishes from sibling tools like search_crunchbase and search_zillow, which target different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description does not explicitly state when to use this tool versus alternatives, nor provides exclusion criteria. However, it implies usage for Google Maps business listings and notes 'No login, no Google Maps API key', which gives some context about ease of use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Given no annotations, the description bears full responsibility for behavioral transparency. It describes outputs (interest over time, geographic breakdown, related queries) but does not disclose performance characteristics, rate limits, data freshness, or any potential limitations beyond the parameter constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two short sentences that front-load the core action and immediately state the key outputs. Every word serves a purpose, with no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has three parameters and no output schema or annotations. The description adequately outlines the tool's purpose and high-level output categories, but lacks detail on how to interpret the returned data or any examples. It is sufficient for simple use cases but could be more complete for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all three parameters with descriptions, achieving 100% coverage. The description adds minimal extra meaning by restating the keyword limit (up to 5) and listing what data is returned, but does not elaborate on parameter usage beyond what the schema provides. Per guidelines, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Get Google Trends interest data for up to 5 keywords.' It specifies the resource (Google Trends) and the action (get data), and distinguishes itself from sibling search tools by focusing on Google Trends specifically.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like search_google_maps or search_crunchbase. It does not mention when to use or avoid this tool, nor does it compare with other tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden for behavioral disclosure. It only states return values but does not mention read-only nature, authentication needs, rate limits, or error handling. Minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose, second lists return fields. No redundancy, highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with 2 well-documented parameters and no output schema, the description covers the core purpose and return data. Missing context like authentication or rate limits, but sufficiently complete given low complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds context about return fields but does not clarify parameter details beyond the schema. Adequate but not enhanced.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches IndiaMART for B2B suppliers and manufacturers in India, specifying the resource and scope. It also lists return fields (company name, products, location, contact details, ratings), distinguishing it from siblings that search other platforms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like find_b2b_leads or other platform-specific searches. No explicit when-to-use or when-not-to-use information is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses use of Google indexing and no authentication needed. Does not mention potential limitations like data freshness or coverage, but adds value beyond schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no filler, front-loaded with purpose. Efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, return fields, and key method. Lacks pagination or error info but sufficient for a simple 3-param tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all 3 parameters with descriptions. Description adds return field info but not parameter details; schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'Find', resource 'employees at a company on LinkedIn', and lists returned fields. Distinguishes from sibling tools like search_linkedin_jobs and get_linkedin_profiles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains no LinkedIn login or cookies required, indicating context for use. Lacks explicit when-not-to-use or alternatives, but provides clear guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 states 'No login required' and lists return fields (job title, company, location, seniority, applicant count). However, it lacks details on pagination, rate limits, or potential side effects. For a search tool, 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading the action and inputs, then listing outputs. Every sentence adds value with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the return data. However, it omits mention of the default maxResults (25) and does not clarify that location is optional. Still, it covers the main aspects well.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds value by stating the return fields ('Returns job title, company, location, seniority, and applicant count') and the no-login requirement, which goes beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search LinkedIn job listings by keyword and location.' It specifies the verb (search), resource (LinkedIn job listings), and key inputs. It also distinguishes from siblings like search_linkedin_employees and search_linkedin_posts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some usage context ('No login required') but does not explicitly guide when to use this tool versus alternatives like search_naukri_jobs or search_linkedin_posts. Usage is implied, but no exclusions 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description mentions 'uses Google indexing' and no login required, adding behavioral context. However, does not disclose limitations like incomplete coverage or rate limits, which would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no superfluous words. Purpose is stated upfront, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description hints at return fields. For a simple search tool with two params, this is fairly complete. Could add more on result limits or format, but adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and descriptions in schema are adequate. Tool description does not add extra meaning beyond what schema already provides for 'query' and 'maxResults'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Verb 'search' and resource 'LinkedIn posts' are clear. Differentiates from siblings like 'search_linkedin_employees' and 'search_linkedin_jobs' by specifying 'posts' and mentioning returned fields (snippet, author, headline, URL).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

States 'uses Google indexing — no LinkedIn login required', which helps decide when to use (no login needed) and implies public posts. Lacks explicit when-not-to-use or alternative suggestions, but context is sufficient for differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adequately states the tool returns specific job fields, which implies a read-only search operation. However, it does not disclose potential rate limits, pagination behavior, or result ordering. A score of 3 reflects adequate but minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence with no wasted words. It effectively conveys the tool's purpose and return data. This level of conciseness is optimal for an AI agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description provides a reasonable set of information: it states the source (Naukri.com), geographic scope (India), and returned fields. It does not mention result limits beyond the maxResults default or any API specifics, but it is largely complete for a straightforward search tool. A score of 4 indicates it is slightly above adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for the parameters (keywords, location, maxResults). It does mention return fields, which is helpful context but not parameter-specific. The schema descriptions are sufficient, hence a score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches Naukri.com for job listings in India and lists specific return fields (title, company, location, salary, experience, description). This verb+resource combination effectively distinguishes it from sibling tools like search_linkedin_jobs (LinkedIn) and scrape_ats_jobs (ATS scraping).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for Indian job searches on Naukri.com but provides no explicit guidance on when to use this tool over alternatives. Sibling tool names are listed in context, but the description itself lacks directives like 'Use this for Indian Naukri jobs; for global jobs use search_linkedin_jobs.' A neutral score of 3 is appropriate as the usage context is implied but not articulated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. Beyond stating it searches for grants, it offers no behavioral details such as rate limits, authentication requirements, or what happens when no results are found. The description adds minimal transparency beyond the 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the purpose and immediately follows with the output details. Every word adds value, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with three parameters and no output schema, the description adequately covers what is searched (grants), how to search (by topic, agency, institution), and what is returned. Missing details like pagination or error handling are acceptable for a simple search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, providing baseline parameter descriptions. The description adds value by listing the returned fields (title, abstract, award amount, PI, institution), which helps the agent understand what data to expect from the query. This goes beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool searches NIH RePORTER for grant awards, with clear specification of the resource (NIH RePORTER), action (search), and returned fields (title, abstract, award amount, PI names, institution). This clearly distinguishes it from sibling tools like search_pubmed (biomedical literature) or search_arxiv (preprints).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for grant searches but provides no explicit guidance on when to use this tool versus alternatives, nor does it mention when not to use it. Sibling tool names hint at different domains (e.g., grants vs. literature or jobs), but no direct comparison or context is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided; the description adds context about return fields and index size (36M+ articles). However, it lacks disclosure of rate limits, authentication needs, or handling of large queries.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences: first states purpose, second lists return fields and scale. No unnecessary words, front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with no output schema or nested objects, the description covers purpose, return fields, and index size. It lacks behavioral details like pagination or result limits, but is moderately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage; parameters are well-described. The tool description does not add extra meaning beyond the schema, so baseline score is 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The tool clearly states it searches PubMed for biomedical literature and lists return fields (PMID, title, etc.). It is distinct from siblings like search_arxiv and search_nih_grants, but does not explicitly differentiate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for biomedical literature searches with an example query, but provides no guidance on when to use this tool versus alternatives like search_arxiv (preprints) or search_nih_grants (grants).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries behavioral burden well by listing returned fields and noting sold mode via soldMode parameter. However, it omits potential rate limits, data freshness, and geographic scope (US only).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences front-load the core action and return data, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately lists return fields and key parameters. Missing details on pagination or error handling, but still sufficient for a straightforward search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description adds marginal value beyond schema. It restates 'city or ZIP' and 'for-sale or sold' but does not deepen understanding of minBeds, maxPrice, etc.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches Realtor.com for-sale or sold listings by city or ZIP, specifies returned fields (price, beds, baths, etc.), and distinguishes from siblings like search_zillow and search_redfin by targeting Realtor.com.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates usage via city/ZIP and mentions 'No MLS login' for ease, but does not explicitly advise when to choose this tool over siblings (e.g., search_zillow) 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It lists return fields but does not disclose behaviors like pagination, data freshness, rate limits, or authentication requirements. This is insufficient for a tool with 6 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (2 sentences), front-loaded with the core action, and contains no unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters and no output schema, the description covers the main function and one key parameter use case. It does not explain filtering options like minBeds or maxPrice, but the schema covers those. Adequate but not comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds minimal value beyond the schema, only highlighting that soldWithinDays enables recently-sold searches.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches Redfin for-sale or recently-sold listings by city, ZIP, or URL, and lists return fields. However, it does not explicitly differentiate from sibling tools like search_zillow or search_realtor, which provide similar functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives one specific use case ('Use soldWithinDays for comparable-sales analysis'), but does not provide guidance on when to use this tool vs 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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description must disclose behavior. It mentions the requirement for an API key and the 'live' nature, but does not state read-only intent, rate limits, or response format. For a search tool, safety is implied, but the disclosure is not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the action verb 'Search'. Includes essential information (scope, filters, API key requirement) without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, no annotations, and no output schema, the description covers the core purpose and key filters but omits return format, error behavior, and clarifications (e.g., 'agency' maps to organizationName). It is sufficient for tool selection but lacks details for full invocation confidence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters are fully described in the schema. The description reiterates some parameter names (keyword, NAICS, notice type, agency, set-aside) but adds no extra detail beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the tool's function: 'Search live US federal contract opportunities on SAM.gov' with a list of notice types. It distinguishes from sibling tools by naming SAM.gov contracts specifically, unlike generic search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool ('Search live US federal contract opportunities'), but does not explicitly mention alternatives or exclusions. The filter capabilities (keyword, NAICS, notice type, agency, set-aside) imply relevant use cases, so it's above minimal guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description mentions returns metadata and excerpts, but no details on limitations, rate limits, pagination, or error behavior. Since no annotations, this is insufficient for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two brief sentences conveying purpose and output. No superfluous words, front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Missing details on return format, pagination, query syntax, and date range of filings. With 4 parameters and no output schema or annotations, description should provide more context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters with descriptions; tool description adds no extra meaning beyond listing example filing types. Baseline 3 due to 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states full-text search across SEC EDGAR filings with specific filing types (10-K, 10-Q, 8-K). Distinguishes from siblings which cover other domains like arXiv, Crunchbase, LinkedIn.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage when needing SEC filing search, but no explicit guidance on when not to use or comparisons to sibling tools like resolve_company_identity or search_crunchbase.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavioral traits. It states 'No API key' but lacks details on rate limits, data freshness, caching, or restrictions (e.g., geographies covered). The read-only nature is implied but not explicitly stated, and no error behavior is described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise, with three sentences each adding information: first sentence defines purpose, second lists returned fields, third notes no API key. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 6 parameters and no output schema, the description provides a list of returned fields which partially compensates for missing output schema. However, it lacks details on sorting, pagination (maxItems is described in schema), and whether both for-sale and rental are searched simultaneously or need separate calls.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds marginal value by listing return fields (price, beds, baths, sqft, address, coordinates, URL) but does not enhance parameter meaning beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'Search', resource 'Zillow for-sale or rental listings', and acceptable location inputs. It distinguishes from sibling real estate tools like search_realtor and search_redfin by specifically mentioning Zillow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description implies use for searching Zillow listings and mentions 'No API key' as an advantage, but does not provide explicit guidance on when to use this tool over siblings like search_realtor or search_redfin, nor does it mention any limitations or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Given no annotations, the description carries the full burden. It discloses the key behaviors: bulk operation, syntax checking, MX record lookup, SMTP probing, and disposable/role-based detection. It does not mention rate limits, response format, or potential latency, but for a verification tool, the behavioral profile is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero wasted words. The first sentence states what the tool does, the second provides usage context. Information is front-loaded and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose and usage but lacks information about the tool's output (return values) and any limitations (e.g., email volume limits). Without an output schema, the description should hint at what the agent receives, which it does not. The tool is simple, so the gap is moderate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description does not add new meaning beyond the schema; it reiterates the checks but does not elaborate on parameter usage or constraints. The parameter semantics are adequately covered by the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs and resources: 'Bulk-verify email addresses' and lists the types of checks (syntax, MX, SMTP, disposable, role-based). It clearly differentiates from sibling tools, none of which perform email verification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use: 'before sending outreach' and the goal 'so you only contact addresses that actually exist.' It does not mention when not to use or alternatives, but the context is clear and sufficient for the tool's standalone purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.