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

Annotations already declare readOnlyHint=true, and the description adds meaningful operational context: this tool does not fetch or retrieve anything, only evaluates provided signals. This goes beyond the annotation's safety implication and clarifies the tool's pure judgment 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 with no wasted words. The core action and result are front-loaded, followed by precise exclusions that help differentiate from siblings.

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 non-behavior, and the schema fully documents parameters. However, there is no output schema, and 'decision-ready output' is vague about what the agent should expect in the return value, which is a meaningful gap for a judgment 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 each parameter. The description only adds a general phrase about 'caller-provided candidate context,' which maps to the signals parameter but does not enrich the semantics of profile, intent, or now 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 uses a specific verb ('Evaluate') and a specific resource ('caller-provided candidate context') and states the outcome ('decision-ready output'). It also distinguishes itself from siblings by explicitly listing operations it does not perform ('does not fetch, crawl, scrape, browse, read folders, or call adapters'), making its role clear.

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 positions this as 'the primary FreshContext judgment path' and gives clear exclusions for when not to use it, implying fetch/crawl/scrape tools are for retrieval. It does not name specific alternative tools, but the sibling list makes the intended separation 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?

Annotations already declare readOnlyHint and openWorldHint. The description adds useful context about using the official API and the returned fields, but it does not disclose max_length behavior, pagination, or truncation, which would be valuable 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?

The description is two tight sentences with no wasted words. The core action and accepted input types are front-loaded, and the return content is summarized efficiently.

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 return fields are listed even though there is no output schema, which is helpful. But the max_length parameter's effect is never explained, and there is no mention of result limits or error behavior, so completeness is only 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?

Only the url parameter is described in the schema; max_length has a default but no meaning is provided. The description repeats the url semantics but does not compensate for the undocumented max_length parameter, leaving a real gap at 50% 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?

The description uses a specific verb and resource: 'Search arXiv for research papers via the official API.' It clearly distinguishes this tool from siblings by naming the source and the output fields it returns.

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 makes it clear this tool is for arXiv searches and accepts topics, keywords, or full arXiv API URLs. However, it does not explicitly mention when not to use it or name alternatives like extract_scholar, so it stops short of full 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?

Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe read operations with flexible inputs. The description adds valuable behavioral context beyond annotations: it specifies auto-discovery of changelog paths (/changelog, /releases, etc.), mentions using GitHub Releases API for GitHub URLs, and describes the return format (version numbers, release dates, entry content, timestamped). No contradiction with 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 efficiently structured in two sentences: the first explains the tool's function and input types, the second provides usage examples. Every sentence adds value with no redundant information, making it front-loaded and appropriately sized.

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's moderate complexity (2 parameters, 100% schema coverage, read-only annotations), the description is mostly complete. It explains purpose, usage, and behavioral context well. The main gap is the lack of output schema, but the description partially compensates by describing return content (version numbers, dates, entries).

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%, providing full documentation for both parameters. The description adds some semantic context by explaining what types of inputs are accepted (GitHub URL, npm package name, website URL) and hinting at auto-discovery behavior, but doesn't provide additional syntax or format details beyond what the schema already covers.

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 specific action ('Extract update history') and resource scope ('from any product, repo, or package'), distinguishing it from sibling tools focused on extracting other data types like finance, GitHub, or SEC filings. It provides concrete examples of input types (GitHub URL, npm package name, website 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?

The description explicitly states when to use this tool ('to check if a tool is actively maintained, when a feature shipped, or how fast a team moves'), providing clear context and use cases. It distinguishes from siblings by focusing on changelog extraction rather than other data landscapes or searches.

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 transparently lists the 5 data sources and states it returns a unified 5-source timestamped report. The readOnlyHint annotation is consistent, and no side effects are implied. It goes beyond the annotation by detailing the multi-source 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?

The description is somewhat long and includes marketing phrases ('most complete', 'not available in any other MCP server'), but the essential structure is clear and the information is well-organized. It does not waste space on irrelevant details.

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 complexity of querying 5 sources, the description sufficiently explains the composite nature and the output (unified 5-source timestamped report). Parameter details like max_length and min_freshness_score are left to the schema, which is acceptable. No output schema exists, but the description sets expectations for the return type.

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 80% (4 of 5 parameters documented). The tool description itself adds no additional parameter meaning beyond what the schema already provides, so 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 what the tool does: a composite company intelligence tool that queries 5 unique sources and returns a unified report. It distinguishes itself from siblings by emphasizing the completeness and uniqueness of the combination.

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 comprehensive company analysis ('most complete single-call') and notes the combination is unique, but does not explicitly state when to choose this over alternatives like extract_finance or extract_landscape. Some guidance is given via the ticker parameter, but no direct when-to-use/when-not-to-use contrast.

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?

Annotations already declare readOnlyHint and openWorldHint, and the description adds useful behavioral context: no API key is required, Stooq is the source, and only successful observations produce fresh context. The 'freshcontext' phrasing is awkward, but it still discloses auth and success/failure behavior beyond the 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 compact and front-loaded with the core purpose and returned fields, followed by ticker limits and behavior. The 'freshcontext' typo and slightly awkward 'timestamped freshcontext' phrasing prevent a perfect score, but there is little wasted text.

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 read-only quote tool with no output schema, it covers source, fields, ticker limit, and success-only behavior. Still, it does not explain max_length semantics, the return shape, or behavior on invalid or mixed tickers, leaving an agent with meaningful 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?

The description adds meaning to the url parameter by specifying 'up to 5 comma-separated tickers,' which is not fully captured in the schema. However, max_length is left unexplained in both the schema and the description, and with 50% schema coverage this is a notable gap.

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 identifies a specific resource — stock quote data via Stooq — and enumerates the returned fields (close, open, high, low, volume, timestamp, source). It does not use an explicit verb like 'retrieves' and does not differentiate from sibling extract_finance_landscape, but the resource is unmistakable.

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 when to use the tool: when keyless stock quotes from Stooq are needed, with up to 5 comma-separated tickers. However, it gives no explicit guidance about when not to use it or how it compares to sibling tools such as extract_finance_landscape or extract_company_landscape.

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?

Consistent with readOnlyHint and openWorldHint; describes read-only multi-source queries and a timestamped report. It does not contradict annotations, though it omits potential latency or rate-limit caveats.

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?

Uses a numbered source list and concise answer bullets, making the behavior easy to scan. Slight redundancy in phrasing ('simultaneously queries' and 'returns') but generally 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?

No output schema is provided, yet the return value is only vaguely described as a unified 5-source timestamped report. The relationship between max_length, freshness_score, and the report structure is not explained, leaving some ambiguity for the 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?

Most parameters have solid schema descriptions, but max_length has no description and the tool description does not clarify it. With 80% schema coverage, the description adds limited 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?

States a specific composite resource (finance landscape) and clearly identifies the multi-source approach, distinguishing it from source-specific siblings like extract_finance, extract_reddit, and extract_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?

Describes what the tool queries and what questions it answers, but never explicitly states when to choose this over extract_finance or individual source tools; the use case is implied rather than directly guided.

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?

Annotations indicate readOnlyHint=true and openWorldHint=true, which the description supports by mentioning 'Free, no auth' and 'updated every 15 minutes.' It adds valuable context beyond annotations by specifying the update frequency, coverage breadth, and that it returns articles with specific fields like title and source domain. No contradictions with annotations are present.

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 front-loaded with key information (action, resource, scope) and efficiently structured in three sentences that each add value: the first explains the tool's function, the second details coverage and output, and the third highlights uniqueness and usage. There is no wasted text, making it highly concise and well-organized.

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's moderate complexity (2 parameters, no output schema), the description is largely complete. It covers purpose, usage context, behavioral traits, and output format (articles with specific fields). However, it lacks details on error handling, rate limits (implied by 'updated every 15 minutes'), or explicit output structure, which could enhance completeness for an 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?

Schema description coverage is 50%, with the 'url' parameter well-described in both schema and description as a query for topics. The description adds meaning by explaining what to pass ('any company name, topic, or keyword') and implies the tool's flexibility. However, it does not address the 'max_length' parameter or provide additional details beyond the schema's default value, leaving some gaps in parameter understanding.

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 specific action ('fetch global news intelligence'), resource ('GDELT Project'), and scope ('monitors broadcast, print, and web news from every country in 100+ languages'). It explicitly distinguishes this tool from siblings by stating 'Unique: not available in any other MCP server,' making its purpose distinct within the server's ecosystem.

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 this tool: for fetching global news intelligence with broad coverage, especially 'covering news worldwide that Western sources miss.' However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as extract_hackernews or extract_scholar, which might serve similar information-gathering purposes.

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?

Annotations already cover readOnlyHint and openWorldHint, and the description adds useful context: free access, no authentication, official data source, and the types of returned data. It does not mention rate limits or pagination, but the disclosed no-auth behavior goes beyond the 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?

Four sentences with the main purpose and source front-loaded, followed by return types and usage guidance. The 'Unique: not available in any other MCP server' sentence is somewhat promotional, but it is brief and does not detract much.

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 exists, so the description correctly lists return content (tenders, contracts, agencies, amounts, closing dates) and explains how to search. Still, it omits the meaning of max_length, response formatting, and any pagination or truncation behavior, leaving an agent to guess on larger result sets.

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 url parameter is well explained in both schema and description with examples and the blank-all option. However, max_length is undocumented in the schema and the description does not clarify what it controls, so the 50% schema coverage gap is only partially compensated.

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 uses a specific verb ('Fetch') and a specific resource ('Singapore Government procurement opportunities from GeBIZ via the data.gov.sg open API'), and names the official Ministry of Finance dataset. It also claims uniqueness among MCP servers, which helps distinguish it from siblings like extract_govcontracts.

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 usage context: search by keyword, agency name, or leave blank for all recent tenders. It does not explicitly state when not to use this tool or name alternatives, but the usage modes are actionable and sufficient for most invocations.

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?

Annotations indicate readOnlyHint=true and openWorldHint=true, covering safety and data scope. The description adds value by specifying 'real-time data' and 'timestamped freshcontext', implying freshness and temporal context, and lists exact data fields extracted. It doesn't contradict annotations, but also doesn't disclose additional traits like rate limits, authentication needs, or error handling beyond what annotations provide.

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, efficient sentence that front-loads the purpose and key details (data fields, return context). It avoids redundancy and wastes no words, though it could be slightly more structured by separating usage notes. Overall, it's appropriately sized for the tool's complexity.

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

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

Given the tool's moderate complexity (2 parameters, no output schema), annotations cover safety and scope, and the description adds specific data fields and temporal context. It adequately informs the agent about what the tool does and returns, though it lacks details on output format or error cases. With annotations handling key behavioral aspects, the description is sufficiently complete for effective use.

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 parameters 'url' and 'max_length' well-documented in the schema. The description doesn't add meaning beyond the schema, as it doesn't explain parameter usage, constraints, or interactions. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 'extract' and resource 'real-time data from a GitHub repository', listing specific data fields (README, stars, forks, etc.) and mentioning the return includes 'timestamped freshcontext'. It distinguishes from siblings by specifying GitHub as the source, unlike other extract tools targeting different platforms (e.g., HackerNews, SEC filings). However, it doesn't explicitly contrast with sibling 'extract_landscape' or 'search_repos', which might overlap in scope.

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 GitHub repository data, but provides no explicit guidance on when to use this tool versus alternatives like 'extract_landscape' (which might handle broader data) or 'search_repos' (which might involve querying). It mentions 'real-time data' and 'timestamped freshcontext', suggesting timeliness, but lacks clear when-not-to-use scenarios 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe read operations with open-world data. The description adds valuable context beyond annotations: it specifies 'No API key required' (convenience/access detail) and describes the return content ('award amounts, dates, awarding agency, NAICS code, and contract descriptions — all timestamped'). However, it doesn't mention rate limits, pagination, or error handling, which keeps it from a perfect score.

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 front-loaded with the core purpose, followed by usage details and examples. Every sentence adds value: the first defines the tool, the second explains parameters and returns, and the third provides use cases. There is no redundant or vague language, making it 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?

Given the tool's moderate complexity (2 parameters, no output schema), the description is largely complete. It covers purpose, usage, parameters, returns, and access details. However, without an output schema, it could benefit from more detail on response structure (e.g., format of returned data). The annotations help, but some behavioral aspects like error cases are omitted.

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 thoroughly. The description adds some semantic context by explaining what the 'url' parameter accepts ('company name, keyword, or NAICS code') and implying its search functionality, but it doesn't provide additional syntax or format details beyond what the schema provides. This meets the baseline for 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?

The description clearly states the specific action ('Fetch US federal government contract awards'), resource ('from USASpending.gov'), and scope ('by company name, keyword, or NAICS code'). It distinguishes itself from sibling tools by focusing on government contracts rather than changelogs, finance data, GitHub repos, or other 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?

The description explicitly states when to use this tool: 'to find buying intent signals, competitive intelligence, or GTM targeting.' It provides concrete examples (e.g., 'a company that just won a $5M DoD contract is actively hiring and spending'), which helps differentiate it from alternatives like extract_finance_landscape or extract_sec_filings that might serve overlapping but distinct purposes.

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 key behaviors not covered by annotations: it 'simultaneously queries' four sources and 'returns a unified 4-source timestamped report'. It also hints at size/freshness controls via max_length and min_freshness_score. No contradictions with the readOnlyHint or openWorldHint 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 structured with a clear lead ('Composite government intelligence tool'), numbered source list, and purpose statement. It is slightly verbose with the marketing claim 'Unique — not available in any other MCP server', but the structure helps readability.

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's composite complexity and lack of an output schema, the description adequately explains what it queries, why, and what it returns. It also clarifies how parameters affect sections. A more detailed output structure or error behavior would improve completeness, but it's not critically missing.

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 describes three of four params; the description adds essential context for query (company/keyword/NAICS) and github_url (optional, scopes GitHub/changelog sections). max_length lacks a description but its name and default are self-explanatory, and the overall description compensates for the 75% 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?

The description clearly states the tool is a 'Composite government intelligence tool' and enumerates the four data sources (USASpending, GitHub, Hacker News, changelog) and the questions it answers. It distinguishes itself from more focused siblings like extract_govcontracts or extract_hackernews by covering multiple sources 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 implies when to use it—when a broad multi-source government intelligence snapshot is needed—by listing the integrated sources and output. However, it does not explicitly state 'use this when…' or contrast with alternatives, leaving the decision to the agent's inference.

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?

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds a useful compatibility nuance ('preserving the url field'), but does not disclose return format, pagination, rate limits, or max_length behavior. With annotations covering safety, 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?

Two concise sentences with no filler. The primary action is front-loaded, and the compatibility note is brief and purposeful.

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?

Gives the resource and input modes clearly, but with no output schema it omits return format details and the effect of max_length. Sufficient for a simple read tool, but not fully complete for an agent expecting to consume the result.

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 the url parameter with HN/Algolia/query examples, and the description adds the compatibility rationale. However, max_length has no schema description and is not mentioned in the description, leaving a required behavior (truncation/limit) undocumented. With 50% schema coverage, the description does not fully compensate.

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

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

States a specific verb ('Extract'), resource ('Hacker News'), and scope ('top stories or search results'). This clearly distinguishes it from sibling extract_* tools by source and content type, with no tautology.

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 for Hacker News data via 'Accepts an HN/Algolia URL or a plain search query', but does not explicitly state when to prefer this over siblings or provide exclusions/alternatives. Context is understandable but routing is left to inference.

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?

Annotations already cover readOnly and openWorld hints. The description adds that it 'simultaneously queries 6 sources' and 'returns a unified report', giving useful behavioral context without contradicting the 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 long but well-organized, starting with purpose, listing questions, then detailing each source and ending with the return type. The verbosity adds valuable information rather than 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?

With no output schema provided, the description only says 'returns a unified 6-source idea validation report' without specifying the report's structure, scoring, or fields. While adequate for an overview, it lacks detail that would help an agent interpret the result.

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 idea parameter is well described with examples, and min_freshness_score has a clear filter explanation. However, max_length is only given a default with no description of its effect or units, leaving a gap in 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?

The description clearly states that this is a composite tool for idea validation, listing the specific questions it answers and the 6 sources it queries. It is easily distinguished from sibling tools that target individual 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?

The description implies use for idea validation across multiple sources, contrasting with single-source sibling tools. It does not explicitly state when not to use it, but the composite nature and source list make the intended use 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?

Annotations indicate readOnlyHint=true and openWorldHint=true, which the description aligns with by describing a querying operation without implying mutations. The description adds valuable context beyond annotations: it specifies the 6 sources queried, the types of questions answered, and that it returns a 'unified 6-source timestamped landscape report,' which helps the agent understand the tool's scope and output format.

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 front-loaded and efficient: it starts with the tool's composite nature, lists the sources, specifies the questions answered, and describes the output in a single, well-structured sentence. Every part earns its place without redundancy.

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's complexity (querying 6 sources) and lack of output schema, the description does a good job of explaining what the tool does and what it returns. However, it could be more complete by detailing the report structure or any limitations (e.g., rate limits, data freshness). With annotations covering safety and openness, the description is largely adequate but has minor 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 description coverage is 50% (one of two parameters has a description). The description mentions 'project idea or keyword,' which aligns with the 'topic' parameter but doesn't add details beyond the schema's description. It doesn't mention the 'max_length' parameter at all. Since schema coverage is moderate, the description provides minimal additional parameter semantics, meeting the 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's purpose: it's a 'composite intelligence tool' that 'simultaneously queries YC startups, GitHub repos, HN, Reddit, Product Hunt, and package registries' to answer specific questions about who is building something, funding status, and traction. It distinguishes itself from siblings by specifying the 6 sources it queries and the unified report it returns, unlike more focused sibling tools like extract_yc or extract_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?

The description provides clear context for when to use this tool: 'Given a project idea or keyword' to get a comprehensive landscape report. It implies usage for broad research rather than specific source queries, but it doesn't explicitly state when not to use it or name alternatives among the sibling tools (e.g., using extract_yc for YC-only data).

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?

Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and external access. The description adds useful behavioral context by disclosing the Product Hunt GraphQL API usage and the HTML scrape fallback, plus timestamped results. It does not mention rate limits or failure behavior, but with annotations covering the core safety profile, this is a reasonable disclosure level.

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 tight sentences with no filler. The primary purpose is front-loaded, and the API/fallback note plus return field list are concise and directly useful for an agent deciding whether to invoke the tool.

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 no output schema, the description appropriately lists the returned fields. It covers purpose, input semantics, external dependency, and output contents. The only notable gap is max_length semantics, which is optional and has a default, so the description is nearly complete for a read-only retrieval 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 50%: the url parameter is described in the schema, while max_length is not. The description reinforces that url can be a keyword or topic URL and explains what data the tool returns, but it does not clarify the meaning of max_length, which remains underdocumented. It adds some value but does not fully compensate for the schema gap.

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 opens with a specific verb+resource combination: "Recent Product Hunt launches by keyword or topic," and then enumerates the returned fields. This clearly distinguishes it from sibling extract_* tools (extract_yc, extract_reddit, etc.) by naming the exact source platform and data type.

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 clear context for when to use the tool: when you need recent Product Hunt launches filtered by keyword or topic. It does not explicitly name alternatives or exclusion criteria, but the platform-specific phrasing and sibling tool names make the use case evident.

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 readOnlyHint annotation is true, and the description does not contradict it. The description adds useful behavioral details about returning specific post fields and using the public JSON API, which goes beyond the annotation.

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, using two sentences to convey the tool's purpose, input formats, and output fields. No extraneous information is included.

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 the return fields, which addresses the lack of an output schema. However, the ambiguity around 'max_length' leaves a minor gap in understanding the tool's full behavior, though overall it is adequate for a simple extraction 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?

The schema covers only the 'url' parameter with a description; 'max_length' has no description in the schema or in the tool description. The description does not clarify the meaning or effect of 'max_length', leaving its semantics ambiguous. Since coverage is only 50%, the description fails to compensate.

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 extracts posts and sentiment from Reddit, specifies accepted URL formats, and lists the returned fields. This is a specific verb and resource, and it is distinct from sibling tools that target other 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?

The description explicitly states the accepted inputs (subreddit URL, search URL, or shorthand), making it clear when to use this tool. It does not explicitly mention alternatives, but the context of sibling tools specialized in other sources makes the usage context implicit yet sufficiently 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?

Annotations provide readOnlyHint=true (safe read operation) and openWorldHint=true (can handle diverse inputs), which cover basic behavioral traits. The description adds value by specifying the output format (titles, authors, etc.) and timestamping, but does not disclose additional behavioral aspects like rate limits, authentication needs, or error handling. There is no contradiction with 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 a single, efficient sentence that front-loads the purpose and output details without unnecessary words. Every part earns its place by conveying essential information about the tool's function and results.

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's moderate complexity (2 parameters, no output schema), the description is reasonably complete: it covers the purpose, source, and output format. However, it lacks details on parameter usage (especially 'max_length'), error cases, or behavioral nuances like pagination or data limits, which could be helpful for an agent. Annotations provide some safety context, but more operational guidance would enhance 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 description coverage is 50% (only the 'url' parameter has a description). The description adds no specific parameter semantics beyond what the schema provides—it mentions 'Google Scholar search URL' which aligns with the schema's 'url' description, but does not explain 'max_length' or provide additional context like format examples or constraints. With partial schema coverage, the description does not fully compensate for the gap.

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 specific action ('extract research results'), source ('from a Google Scholar search URL'), and output format ('titles, authors, publication years, and snippets — all timestamped'). It distinguishes itself from sibling tools by specifying Google Scholar as the source, unlike other extraction tools targeting different platforms like GitHub, HackerNews, or 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 implies usage context by specifying 'Google Scholar search URL' as the input, indicating this tool is for extracting data from Scholar searches rather than other sources. However, it does not explicitly state when to use this tool versus alternatives (e.g., other extract_* tools) or provide exclusions, such as whether it works with non-search URLs or other academic databases.

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?

Annotations indicate read-only and open-world hints, which the description aligns with by mentioning 'Free, no auth, real-time.' It adds valuable behavioral context beyond annotations: the tool fetches legally mandated disclosures filed within 4 business days and uses the SEC EDGAR API. No contradictions with annotations are present, and the description enhances understanding of the tool's operational constraints and data source.

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 front-loaded with the core purpose, followed by additional context and unique selling points. Each sentence adds value: defining 8-K filings, explaining input types, and highlighting uniqueness. There is no wasted text, making it efficient and well-structured for quick comprehension.

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's complexity (fetching legal filings with real-time data), annotations cover safety aspects, and the description adds context on data source and constraints. However, without an output schema, the description does not detail return values or format, which could be helpful for an agent. It compensates well with input guidance and behavioral transparency but leaves output expectations implicit.

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 50%, with the 'url' parameter documented in the schema but 'max_length' lacking a description. The description adds meaning by explaining that the 'url' parameter accepts 'company name, ticker, or keyword' and provides examples, which clarifies its semantics beyond the schema. However, it does not address the 'max_length' parameter, leaving a gap in parameter understanding.

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 specific action ('Fetch SEC 8-K filings'), the resource ('for any public company'), and the source ('from the SEC EDGAR full-text search API'). It distinguishes this tool from siblings by explicitly stating 'Unique: not available in any other MCP server,' which highlights its distinctiveness among the extraction-focused sibling 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 this tool: for fetching SEC 8-K filings related to material corporate events, with examples of input types ('company name, ticker, or keyword'). However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools, such as which other extraction tools might be more appropriate for different data types.

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?

Annotations already indicate read-only and open-world behavior. The description adds a useful transparency note about freshness ('Freshness is unknown'), which helps set expectations. However, it does not mention rate limits or data volume, though these are not critical given the read-only annotation.

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, direct, and information-dense. It covers the action, the URL pattern, the expected output, and the freshness caveat without any redundancy or irrelevant detail.

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?

Since there is no output schema, the description compensates by listing the returned fields (name, batch, tags, description) and noting the freshness limitation. It does not cover error handling or pagination, but for a simple scraping tool, the provided context is sufficient for most use cases.

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 url parameter is well documented with an example, but max_length has no description in the schema or the description text. The name is somewhat self-explanatory, and the default value (6000) hints at output length, but the lack of explicit documentation keeps this at a mid-level 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?

States a specific action ('Scrape YC company listings') and a clear resource (Y Combinator companies). The URL template further clarifies the target, and the tool is clearly distinguished from sibling extract_* tools by its YC focus.

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 concrete usage instruction ('Use https://www.ycombinator.com/companies?query=KEYWORD to find startups in a space') and describes what it returns. It does not explicitly contrast with alternatives, but the specific YC scope and keyword-based query make usage 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?

Annotations indicate read-only and open-world operations, which the description aligns with by describing a lookup function. The description adds valuable context beyond annotations by specifying supported ecosystems (npm and PyPI), the ability to handle comma-separated lists, and the purpose of gauging activity, though it lacks details on rate limits or authentication needs.

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

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

The description is front-loaded with the core purpose, followed by usage context and parameter details in two efficient sentences. Every sentence adds value without redundancy, making it highly concise 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?

Given the tool's moderate complexity (2 parameters, no output schema), the description covers the purpose, usage, and key parameter semantics adequately. However, it lacks details on output format or error handling, which would enhance completeness for an agent invoking 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 50%, with the 'packages' parameter well-described in both schema and description. The description adds meaning by explaining the comma-separated list format and ecosystem prefixes, but does not clarify the 'max_length' parameter's purpose or units, leaving a gap in parameter understanding.

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 specific action ('Look up npm and PyPI package metadata') and resources ('package metadata — version history, release cadence, last updated'), with explicit ecosystem scope. It distinguishes itself from sibling tools by focusing on package metadata rather than changelogs, company data, or other extraction 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?

The description provides clear context for when to use this tool ('to gauge ecosystem activity around a tool or dependency'), but does not explicitly state when not to use it or name specific alternatives among sibling tools like 'extract_changelog' or 'extract_github' that might overlap in some contexts.

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?

Annotations indicate read-only and open-world hints, which the description doesn't contradict. The description adds valuable behavioral context beyond annotations: it mentions 'freshness badges on every result,' timestamped results sorted freshest first, and that it prevents applying to closed roles. This provides insight into output behavior and user benefits not covered by 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 front-loaded with the core purpose and key feature (freshness badges), followed by supporting details in a single, efficient sentence. Every part adds value: sources, filtering capabilities, and result sorting without 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 tool's complexity (6 parameters, no output schema), the description is fairly complete. It covers purpose, sources, key features, and result behavior. However, it could be more explicit about error handling or rate limits, though annotations provide some safety context with readOnlyHint and openWorldHint.

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 high at 83%, so the baseline is 3. The description adds some semantic context by mentioning 'location filtering, remote-only mode, keyword spotting (e.g. FIFO), and max age filtering,' which aligns with parameters but doesn't provide additional details beyond what the schema already describes in its property 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 searches for 'real-time job listings with freshness badges' from specific sources (Remotive, RemoteOK, The Muse, HN 'Who is Hiring'), distinguishing it from sibling tools that extract data from other domains like GitHub, SEC filings, or HackerNews. It specifies the verb 'search' and resource 'job listings' with unique freshness features.

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 this tool by listing its supported features (location filtering, remote-only mode, keyword spotting, max age filtering) and sources. However, it doesn't explicitly state when NOT to use it or name specific alternatives among sibling tools, though the context implies it's for job searches versus other data extraction 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?

Annotations indicate readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, open-ended search. The description adds context: 'Returns top results by stars with activity signals,' which discloses sorting behavior and additional data beyond basic results. However, it doesn't mention rate limits, authentication needs, or pagination details, so it adds some value but not rich behavioral context beyond 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 concise and front-loaded: the first sentence states the core purpose, followed by additional context. Both sentences earn their place by clarifying behavior and usage. It's appropriately sized without wasted words, though it could be slightly more structured for optimal clarity.

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's moderate complexity (2 parameters, no output schema, annotations present), the description is somewhat complete but has gaps. It covers purpose and basic behavior but lacks details on error handling, exact return format, or how 'activity signals' are defined. With annotations providing safety info, it's adequate but not fully comprehensive for an open-world 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 50% (only the 'query' parameter has a description). The description doesn't add specific meaning for parameters beyond what's in the schema; it mentions 'keyword or topic' which aligns with 'query' but doesn't explain 'max_length' or provide additional syntax details. With partial schema coverage, the description compensates minimally, meeting the baseline for adequate but not enhanced 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?

The description clearly states the tool's purpose: 'Search GitHub for repositories matching a keyword or topic.' It specifies the verb ('Search'), resource ('GitHub repositories'), and scope ('matching a keyword or topic'). However, it doesn't explicitly differentiate from sibling tools like 'extract_github' or 'package_trends', which might have overlapping functionality, so it doesn't reach the highest score.

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 implied usage guidance: 'Use to find competitors, similar tools, or related projects.' This gives context for when to use the tool but doesn't explicitly state when not to use it or mention alternatives among sibling tools. For example, it doesn't clarify if 'extract_github' is for different GitHub operations, leaving some ambiguity.

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