The Mine Works

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 lists the returned fields but does not mention potential failures (e.g., invalid repo names, rate limits) or whether the tool is read-only. The description is adequate but lacks error or side-effect details.

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 key functionality and providing a concise use case. No wasted words; every sentence is necessary.

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 only 2 parameters, no output schema, and no annotations. The description covers the main purpose, use case, and a key constraint (no token required). It could mention return format but is otherwise sufficiently complete for the tool's simplicity.

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

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds little extra: it reinforces the 'owner/name' format but does not elaborate on includeReadme. The schema already provides sufficient meaning.

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' and lists specific data fields (stars, forks, language breakdown, etc.). It distinguishes itself from sibling tools which cover different domains like LinkedIn, real estate, and leads.

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 gives a clear use case: 'Use to see what a company or project is actually building.' It also notes 'No GitHub token required,' which is helpful. However, it does not explicitly state when not to use the tool or mention alternatives.

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. Discloses 'guessed business emails' indicating uncertainty, and 'no API key required'. However, lacks details on rate limits, data accuracy, or any destructive potential. Falls short of comprehensive disclosure.

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 a single efficient sentence with two additional clarifications. Front-loads main verb and resource, but could be more structured with bullet points.

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 gives reasonable expectation of return content (profiles, titles, emails). For a simple lead generation tool, it covers key aspects, though pagination and limits are absent.

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 param semantics are already clear in schema. Description adds minimal value with example values ('stripe.com'), but does not significantly enhance understanding beyond the schema.

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

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

Description states specific verb 'Find' and resource 'B2B leads for target companies' including LinkedIn profiles, job titles, and emails. Positions itself as alternative to Apollo/ZoomInfo, distinguishing from sibling tools like find_linkedin_candidates and search_linkedin_employees.

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 when-to-use or when-not-to-use guidance. Provides minimal context ('No API key required') but does not compare with siblings or specify 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 must disclose behavioral traits. It mentions 'No login required,' which is helpful. However, it omits details on rate limits, data freshness (cached vs. real-time), pagination behavior, or whether results are limited to publicly indexed profiles. The lack of these disclosures leaves behavior partially opaque.

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 front-loading purpose and return fields. Every sentence serves a clear function with no redundancy. It is well-structured for quick parsing by 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 7 parameters, no output schema, and no annotations, the description covers core purpose and return fields adequately but misses some details. It does not explain the confidence score, result limits, or all parameter names explicitly. For a tool of this complexity, it is minimally complete but has gaps.

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%, with each parameter described adequately. The description adds value by listing return fields (e.g., name, headline, match-confidence score), which helps infer parameter purpose. However, it does not elaborate on the 'match-confidence score' range or explicitly mention maxResults, minYears, maxYears by name, leaving minor gaps.

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 role, skills, location, experience band, and target companies, and lists return fields. It distinguishes from sibling tools by explicitly labeling it as 'Recruiter sourcing' and focusing on candidate matching, which is distinct from employee search or job postings.

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 recruiter sourcing and notes 'No login required,' which is a usage condition. However, it does not provide explicit guidance on when to use this tool versus alternatives like search_linkedin_employees or search_linkedin_profiles, nor does it specify 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 should disclose behavioral traits. It only mentions no API key required and that it crawls specific page types. It fails to mention rate limits, delays, handling of inaccessible domains, or any potential blocking issues, leaving the agent without critical 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?

The description is two sentences long, front-loaded with the main action and extraction details, and the second sentence adds a key differentiator (no API key). Every word serves a purpose, making it 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?

While the tool is simple, the description lacks details about output format, error handling, request limits, and execution time. Given no output schema and no annotations, the agent is left without a complete picture of the tool's behavior and result structure.

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 description adds context beyond the schema by specifying that extraction occurs from homepages, contact, and about pages. This clarifies the scope of crawling, which the schema parameters alone do not convey.

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 crawls a list of domains to extract business emails, phone numbers, and social profile links. It explicitly lists the pages crawled (homepages, contact, about) and distinguishes it from sibling tools that focus on LinkedIn, Crunchbase, or 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 implies usage for extracting contact info from websites and notes no API key required, suggesting ease of use. However, it does not explicitly state when to choose this tool over alternatives like find_b2b_leads or search_linkedin_candidates, nor does it mention limitations.

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 cover behavioral traits. It discloses best-effort name resolution and return fields, but omits rate limits, authentication requirements, or response format (e.g., single object vs array).

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 purpose and key details. 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?

Adequate for a simple input schema (1 param), but lacks output schema and does not mention error handling or data freshness, which would improve agent decision-making.

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 already describes the 'companies' parameter with examples; description adds value by clarifying that names are resolved best-effort to slugs, which is critical for correct input.

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 'Get LinkedIn company details' and lists specific fields (employee count, industry, etc.), distinguishing from sibling tools like 'get_linkedin_profiles' or 'search_linkedin_employees' which target other data.

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

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

Explicitly mentions acceptable inputs (URLs or names) and resolution behavior, but lacks guidance on when not to use this tool or how it compares to search tools like 'search_linkedin_companies' (not in siblings, but implied context).

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 partially covers behavior by stating 'No login or cookies required', which is a key behavioral trait. However, it does not disclose error handling, rate limits, or what happens for invalid URLs. The list of returned fields adds value but lacks completeness.

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 immediately state the purpose and key benefit. No redundant information. Front-loaded with 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?

For a one-parameter tool with no output schema, the description adequately covers purpose, input, output fields, and a critical behavioral note. It could mention limitations (e.g., public profiles only) but is sufficiently complete 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 coverage is 100% for the single parameter, so baseline is 3. The description adds context about output fields and no-login requirement, but does not significantly enhance understanding of the parameter beyond what 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 specific verb 'Fetch' and resource 'full LinkedIn profile details', and lists output fields (experience, education, skills, headline, location). It clearly distinguishes from sibling tools like 'find_linkedin_candidates' which searches by criteria, and 'get_linkedin_company' which targets companies.

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 implicitly indicates usage when you have profile URLs, but does not explicitly state when to use vs alternatives (e.g., search vs direct URL). No when-not-to-use or comparison with sibling tools is provided.

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 carries full burden. It discloses polling behavior and need for repetition, but does not mention potential timeouts, errors, or whether results are guaranteed. Adequate for a simple tool but limited.

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 wasted words. Front-loaded with purpose, then immediate usage instructions. 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?

Simple tool with one param and no output schema. Description covers the polling workflow essential for correct usage. Could mention result format or error conditions, but overall complete for the task.

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 value by explaining the run_id comes from a 'pending' tool and should be used repeatedly, providing usage context beyond schema definition.

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 collects results of an actor run, using specific verb ('collect') and resource ('results of a previously started actor run'). It distinguishes from sibling tools by focusing on polling behavior.

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

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

Explicitly states when to use: when another tool returns 'pending' status with a run_id. Provides step-by-step guidance: wait a few seconds, call with run_id, repeat until results. No alternative tools mentioned 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.

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

No annotations provided; description only says 'Fetch' data, suggesting read-only. Does not disclose rate limits, auth requirements, or any side effects, leaving room for ambiguity.

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 with a bullet-style list of data fields. No wasted words; front-loaded with action and resource.

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?

Lists returned fields but lacks output schema. Does not explain structure or format of the response (e.g., per property, pagination). Incomplete for a deep-data fetching 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% with a good description. The description reinforces 'listing URLs or ZPIDs' and adds context about chaining, adding value beyond the schema.

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

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

Clear verb ('Fetch'), resource ('deep Zillow property data'), and specific fields listed. Distinct from sibling search_zillow by stating it chains off its output.

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

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

Explicitly states it chains off search_zillow output, implying when to use. Lacks explicit exclusions or alternatives from siblings like search_realtor or search_redfin.

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 convey behavioral traits. It lists what the tool returns (LEI code, status, address) but does not disclose any restrictions, rate limits, authentication needs, error handling, or side effects. For a read-only lookup, the safety profile is implied but not stated.

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 three sentences, each adding distinct value: what the tool does, what it returns, and its authoritative nature. No redundant or filler content.

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

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

For a simple lookup tool with only two parameters, no output schema, and no annotations, the description adequately covers the essential purpose and return information. It does not include details on error handling or pagination, but the tool's simplicity reduces the need for further 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 description coverage is 100% (both parameters have descriptions). The tool description mentions 'by legal name' but adds no semantic value beyond the schema's description of legalNames. The matchesPerName parameter is not discussed. 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?

Description clearly states it looks up a company Legal Entity Identifier (LEI) by legal name in the official GLEIF registry, and emphasizes it is an authoritative source for verification. This differentiates it from sibling tools that search general databases or scrape sites, providing a specific verb (look up), resource (GLEIF registry), and purpose (verify legal existence).

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 the tool should be used when official LEI verification is needed (authoritative source), but it does not explicitly state when to use or avoid it compared to alternative sibling tools like resolve_company_identity or search_sec_filings. No exclusions or alternative tool mentions are provided.

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 carries full burden. It explains the output fields and that it performs the resolution in one call, but lacks details on error handling, prerequisites (e.g., exact name matching), rate limits, or data freshness.

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 efficiently lists outputs and provides a usage note. No wasted words; front-loaded with action and result.

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?

No output schema, but description lists key return fields (LEI, legal name, country, status, CIK, filing date). It mentions 'in one call' and usage context. Missing details on how multiple companies are handled or not-found cases, but adequate for a tool with only 2 parameters.

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 additional meaning beyond the schema; it merely restates the purpose without elaborating on parameter usage or constraints.

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 resolves a company name to authoritative identifiers (GLEIF LEI, legal name, country, entity status, SEC EDGAR CIK, last filing date) in one call. It distinguishes from sibling tools like lookup_lei and search_sec_filings by being a comprehensive single-call solution.

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 says 'Use to ground company facts before trusting them,' which implies when to use. It does not explicitly state when not to use or mention alternative tools, but the context of siblings provides some 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 provided, so description carries full burden. It discloses no auth requirement, but omits rate limits, pagination behavior, or output format. 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 front-loaded with purpose and key detail (no auth). No redundancy, 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 simple 3-param tool with no output schema and no annotations, description covers platform scope, auth, and parameter hints. Could add output format but sufficient.

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 value with company slug example and platform enum/auto-detect explanation, exceeding baseline.

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 (scrape), resource (job listings), and specific ATS boards (Greenhouse, Lever, Workday, Ashby), distinguishing it from sibling 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 mentions 'No auth required — uses their public job APIs' indicating when it's appropriate. However, it lacks explicit exclusions or alternatives, though siblings context helps.

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 notes returns 'full post data with comment trees,' providing output details. However, no annotations exist; it lacks mention of rate limits, authentication, or pagination 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?

Two sentences, front-loaded with verb and resource, no fluff. 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?

Covers main modes (subreddit, search, post) but omits 'user' mode from enum. No output schema exists, so description should clarify return structure, which it partially does. Missing details on maxResults 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?

Input schema covers all 6 parameters with descriptions (100% coverage). The description recaps modes but adds no extra per-parameter meaning beyond what 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?

Description clearly states the tool scrapes Reddit posts and comments via subreddit, keyword search, or post URL, distinguishing it from siblings targeting other platforms (e.g., LinkedIn, GitHub).

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 usage scenarios via modes (subreddit, search, post, user) but does not explicitly state when to use this tool over alternatives or provide 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?

No annotations are provided, so the description must disclose behavioral traits. It mentions return fields but does not address potential issues like rate limits, IP blocking, or whether scraping is allowed. For a web scraping tool, this is insufficient.

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 no wasted words. The first sentence clearly states the action and scope; the second lists return fields. Front-loaded and 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, the description compensates by listing returned fields. It is complete for a basic scraping tool, though could optionally mention pagination or error handling. Overall, 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 description coverage is 100%, so both parameters are adequately documented in the schema. The description adds no additional meaning to parameters, only specifying output fields. 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 states a specific verb ('Scrape') and resource ('Trustpilot reviews'), and lists the exact fields returned. This clearly distinguishes it from sibling tools like scrape_reddit or search_crunchbase.

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 use for scraping Trustpilot reviews but does not provide explicit guidance on when to use this tool versus alternatives, nor any prerequisites or 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?

No annotations are provided, so the description must carry behavioral disclosure. It mentions return fields (title, abstract, authors, PDF link) but does not cover rate limits, pagination, result ordering, or error handling. 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 consists of two concise sentences. The first covers action and input, the second covers output and best domains. No unnecessary words, and the key information is front-loaded.

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 absence of output schema and annotations, the description provides basic information about inputs and outputs but lacks details on result format, pagination, or potential errors. It is sufficient for simple use but not fully 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 description coverage is 100%, so the schema already documents all parameters. The description adds no extra semantics beyond mentioning 'keyword or category,' which aligns with the 'query' and 'category' parameters. Thus, no additional value beyond baseline.

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 arXiv preprints by keyword or category, and lists the returned fields (title, abstract, authors, PDF link). This distinguishes it from sibling tools which focus on other domains like GitHub, LinkedIn, or real estate.

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 specifies it is 'Best for AI, CS, physics, and biology research,' implying the appropriate context for use. It does not explicitly state when not to use or mention alternatives, but the domain specificity 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 provided, so description carries full burden. It describes the type of data returned but does not disclose potential limitations like API key requirements, rate limits, or whether results are from public or private data. Still, it is transparent about the data fields.

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 wasted words, front-loaded with the purpose. 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?

For a simple search tool with two parameters and no output schema, the description covers the key return fields and gives a usage context. Could be more complete with a note about output structure, but it's 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%, so description adds minimal value beyond schema. It provides an example ('anthropic') and default value for maxResults, but these are not critical. 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?

Description clearly states the verb 'look up', the resource (company funding and firmographic data on Crunchbase), and lists specific data fields. It distinguishes from siblings by being the only Crunchbase-specific search 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?

Provides a clear use case: 'Use to check whether a company is actually growing.' However, it does not mention when not to use or compare with alternative tools like resolve_company_identity or get_linkedin_company.

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 discloses that no auth is required and lists return fields, which is helpful. However, it omits behavioral traits like read-only nature, rate limits, pagination behavior, or possible limitations on results.

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 concise: two sentences that front-load the purpose and key details. Every sentence adds value, with no fluff 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?

Given no output schema and no annotations, the description covers the basics (inputs, return fields, auth requirement) but lacks depth on usage context, error handling, or restrictions. It is 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 reinforces the query and location params but does not add substantial new meaning beyond the schema. The mention of return fields indirectly relates, but does not enhance param 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?

The description clearly states the action (search), the resource (Google Maps business listings), and the inputs (keyword and location). It also lists the returned fields, making the purpose unmistakable. No sibling tool directly competes, so differentiation is inherent.

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 implicitly tells when to use the tool (to search Google Maps listings) and highlights that no login or API key is needed, which eases usage. However, it lacks explicit when-not-to-use conditions or comparisons to alternatives, though no direct sibling exists.

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 full responsibility. It mentions constraints like 'up to 5 keywords' and lists returns, but omits critical behavioral traits such as authentication requirements, rate limits, data freshness, or whether the tool uses the official API. These gaps could affect agent decision-making.

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-loaded with the core purpose, and contains no filler. Every word serves a function, making it efficient for an agent 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 the tool has 3 parameters, no output schema, and no annotations, the description covers the main returns but lacks depth on edge cases, error handling, data source credibility, and operational limits. It is adequate for a simple tool but incomplete for complex decision-making.

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 does not add meaningful detail beyond the schema; it reiterates the keyword limit but offers no additional context on geo or timeRange formats. No examples or constraints beyond schema are 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 'Get' and the resource 'Google Trends interest data', specifying up to 5 keywords and return types (interest over time, geographic breakdown, related queries). It distinguishes itself from all sibling tools, which cover different domains like social media, job boards, and real estate.

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 retrieving Google Trends data, but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or prerequisites are provided, and sibling tools are for entirely different purposes, reducing the need for detailed 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 provided, so description carries full burden. It states the return fields, which is helpful, but lacks details on authentication, rate limits, pagination, or data freshness. The read-only nature is implied but not explicit.

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 that efficiently conveys the tool's purpose and output. No wasted words; front-loaded with the action verb.

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?

Tool is simple with 2 parameters and no output schema. Description adequately covers what it does and what it returns. Could mention result limits or pagination, but not essential for a basic 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 description coverage is 100% with both parameters described. The tool description adds minimal extra meaning ('Product or supplier search query') but mostly repeats schema info. 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?

Clearly states it searches IndiaMART for B2B suppliers in India and lists the returned fields (company name, products, location, contact details, ratings). The verb 'Search' and resource are specific, distinguishing it from sibling tools like search_crunchbase or search_google_maps.

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 context that it searches IndiaMART for B2B suppliers, but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or prerequisites 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions the Google indexing method and lack of login requirement, but fails to disclose important traits like data freshness, accuracy limitations, rate limits, or that results depend on Google's indexing coverage. This is a significant gap.

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: the first states the core purpose, the second adds the key methodological differentiator. No filler or redundant information. Highly concise and well front-loaded.

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 the return fields. However, it does not mention pagination, error handling, or that results are limited to Google-indexed data. For a simple search tool, it is minimally complete but lacks depth.

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% (all parameters have descriptions), so baseline is 3. The description does not add any additional meaning beyond the schema's parameter descriptions (e.g., company name, optional jobTitle, maxResults). No guidance on how to use parameters effectively or any constraints.

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 'Find employees at a company on LinkedIn' and lists specific returned fields (name, headline, location, profile URL). It distinguishes itself from siblings by highlighting the Google indexing method and no login requirement, 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.

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

The description mentions 'Uses Google indexing — no LinkedIn login or cookies required,' which provides context for when to use this tool (when no login is available). However, it does not explicitly state when to avoid this tool or compare it to alternatives like find_linkedin_candidates or get_linkedin_profiles, leaving room for confusion.

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 discloses that no login is required, which is a key behavioral trait. However, it omits details on rate limits, pagination behavior, or what happens when no results are found. With no annotations, the description should provide more 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 at two sentences, with the main action front-loaded. Every sentence adds value, and there is 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 absence of an output schema, the description adequately lists the returned fields and mentions the no-login requirement. However, it lacks details on error handling, result limits beyond maxResults, and data recency, which would make it 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?

Schema coverage is 100%, so the schema already documents parameters. The description adds context by mentioning 'keyword and location' and the returned fields (e.g., seniority, applicant count), 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.

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

The description clearly states that the tool searches LinkedIn job listings by keyword and location, listing the returned fields. It distinguishes itself from sibling tools like search_linkedin_employees and search_linkedin_posts by focusing on job listings.

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 job search but does not explicitly state when to use this tool versus alternatives such as scrape_ats_jobs or search_naukri_jobs. The 'No login required' note suggests public data access, but no direct guidance on alternatives.

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 carries full burden. It discloses the search method (Google indexing) and no authentication needed, but does not mention limitations like data freshness, rate limits, or potential missing results.

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: action+resource first, then key feature. No fluff, 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?

Lists output fields in absence of output schema. Lacks details on pagination or constraints, but for a straightforward search tool, it is mostly sufficient.

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% with both parameters well-described. The description adds no new parameter information beyond the schema, maintaining baseline score.

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 LinkedIn posts by keyword' and lists return fields (snippet, author, headline, URL). It distinctly separates from sibling tools like search_linkedin_jobs or search_linkedin_employees by focusing on 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 notes it uses Google indexing with no login required, implying public data access. However, it offers no explicit guidance on when to use this tool versus alternatives (e.g., when LinkedIn API is unavailable).

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 full burden. It lists returned fields but does not disclose pagination behavior, result limits (beyond maxResults default), authentication requirements, or update frequency.

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 is concise and front-loaded, but could include a brief note on result structure without becoming verbose.

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 3 parameters and no output schema, the description provides sufficient context for typical usage, listing key returned fields. Lacks detail on edge cases or empty results.

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% with adequate parameter descriptions. The tool description adds context that the search is for Indian Naukri listings, but does not significantly augment the parameter meaning beyond the schema.

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

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

Description clearly states the tool searches Naukri.com for job listings in India, specifying what it returns (title, company, location, etc.). This distinguishes it from siblings like search_linkedin_jobs which target a different site.

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 use for Indian job searches but does not provide explicit guidance on when to use vs. alternatives (e.g., search_linkedin_jobs, scrape_ats_jobs) or any 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?

No annotations provided. The description does not explicitly state that the tool is read-only or disclose any side effects, though the name implies a query. It adds minimal behavioral context beyond what is obvious.

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 efficiently covers what the tool does, search criteria, and returned fields, without waste.

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

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

The description lists return fields compensating for the lack of output schema. It adequately covers the 3 parameters. Minor gaps like default sort order or pagination do not severely impact 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% with descriptions for each parameter. The description does not add additional meaning beyond the schema, such as expected format for agency codes.

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 NIH RePORTER for grant awards by topic, agency, or institution, and lists return fields. This distinguishes it from sibling tools like search_pubmed.

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 use for NIH grant searches but provides no explicit guidance on when to use versus alternatives like search_pubmed for literature or search_arxiv for preprints.

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 discloses that the tool returns specific fields and indexes 36M+ articles, but it does not reveal behavioral traits such as rate limits, authentication requirements, sorting behavior, or error handling. With no annotations provided, the description fails to adequately inform the agent of important behavioral nuances.

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 at two sentences, front-loaded with the core action ('Search PubMed for biomedical literature'). However, it could be slightly more structured to convey additional context without becoming verbose.

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 annotations and output schema, the description should provide more context about limiting results, default settings, error scenarios, or alternative search syntax. It misses important details that could affect tool invocation, such as how maxResults behaves or how to handle pagination.

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?

Both parameters have schema descriptions (100% coverage), so the description adds no additional meaning beyond the schema. The tool description mentions return fields but does not elaborate on parameter usage or constraints, so a 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 tool searches PubMed for biomedical literature and lists return fields like PMID, title, abstract, etc. It is specific enough to distinguish from general search tools but does not explicitly contrast with sibling tools like search_nih_grants, which also target biomedical literature.

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 but provides no guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. Usage context is clear from the name and description, but explicit differentiation is missing.

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 carries the full burden. It adds 'No MLS login' and lists return fields, but does not disclose rate limits, data freshness, or side effects. 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 sentences, front-loaded with purpose, followed by useful details. No wasted 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, the description adequately lists return fields. Missing pagination, sorting, or error handling, but overall fairly complete for a 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%, and the description does not add meaning beyond the existing parameter descriptions. The schema already documents all parameters sufficiently.

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 listings by city or ZIP and lists the returned fields. It is specific but does not explicitly differentiate from sibling tools like search_zillow or search_redfin.

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. While 'No MLS login' is mentioned, there is no explicit when-to-use or when-not-to-use context.

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 burden. It correctly implies a read-only search operation and mentions the soldWithinDays parameter for sold listings. However, it does not disclose rate limits, auth requirements, or other behavioral 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?

Two sentences: first describes purpose and output, second gives usage tip. No redundant words, efficient structure.

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 6 parameters, full schema coverage, no output schema, and no annotations, the description provides adequate context for tool selection and basic usage, including a key use case. Could mention pagination or result limits beyond maxItems.

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. The description adds value by explaining the purpose of soldWithinDays (comparable-sales analysis) and enumerating return fields, which helps agents understand parameter usage beyond schema definitions.

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 Redfin listings (for-sale or recently-sold) by city, ZIP, or URL, and lists the returned fields. It distinguishes from siblings like search_realtor or search_zillow by specifying 'Redfin'.

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 specific usage tip: 'Use soldWithinDays for comparable-sales analysis.' While it doesn't explicitly exclude cases or name alternatives, this guidance is actionable and contextually relevant.

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 full burden. It discloses that it returns metadata and excerpts, but omits details on rate limits, authentication, pagination, or how to handle no results. For a search tool, this is minimally 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 a single, concise sentence that front-loads the purpose and includes examples of filing types. 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?

The description mentions return values vaguely as 'metadata and matched text excerpts', but without an output schema, more detail on the structure or pagination would be useful. For a tool with 4 parameters, it is 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 description coverage is 100%, so parameters are well-documented in the schema. The tool description does not add meaning beyond the schema. 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 it performs full-text search across SEC EDGAR filings and lists specific filing types (10-K, 10-Q, 8-K) and what is returned (metadata and excerpts). This distinguishes it from sibling tools like search_arxiv or search_crunchbase.

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 use for searching SEC filings but does not provide explicit guidance on when to use vs. alternatives, scenarios to avoid, or prerequisites. The sibling context shows many search tools, but no comparative guidance 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, so the description carries the full burden. It mentions 'No API key' but does not disclose rate limits, data freshness, geographic coverage, error handling, or pagination behavior. For a data retrieval tool, these are significant 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?

Two sentences: first covers purpose and outputs, second adds a key behavioral note (no API key). Efficient and front-loaded, with no redundant 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 moderate complexity (6 parameters, no output schema). The description covers the basic purpose and a behavioral trait, but lacks usage guidance, parameter clarification, and deeper behavioral context (e.g., rate limits, data scope).

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%; all six parameters have descriptions in the input schema. The tool description adds no additional detail beyond what the schema provides. 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 it searches Zillow for sale or rental listings by location, and lists the return fields (price, beds, baths, sqft, address, coordinates, listing URL). It explicitly names the source (Zillow), distinguishing it from sibling tools like search_realtor or search_redfin.

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 does not mention when not to use it or provide criteria for choosing it over other real estate listing tools like search_realtor or search_redfin.

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 carries full burden. Lists verification types but does not disclose potential side effects, authentication requirements, rate limits, or whether the tool is read-only. This is a significant gap for a bulk operation.

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

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

Two concise sentences, no wasted words. Front-loaded with the action and key details, followed by usage advice.

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 covers the core functionality well. It explains what checks are performed but could be slightly more complete by hinting at the result format or error handling. Still quite 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 description coverage is 100% with detailed descriptions. The description adds context like 'bulk-verify' and lists verification categories but adds minimal meaning beyond what the schema provides. 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?

Description clearly states the tool verifies email addresses with specific checks (syntax, MX records, SMTP, disposable-domain, role-based). Distinguishes from sibling tools which focus on other data sources like GitHub or 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?

Explicitly states when to use: 'before sending outreach so you only contact addresses that actually exist.' Provides clear context, though does not mention when to avoid using the tool.

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