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?

Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe read operations with open-world assumptions. The description adds valuable behavioral context beyond annotations: it specifies the tool queries 5 specific external sources, returns a 'unified 5-source timestamped report,' mentions data sources like SEC filings and government contracts, and notes the 'max_length' parameter controls output size. It doesn't contradict annotations and provides useful implementation 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 efficiently structured: first sentence establishes purpose, subsequent sentences detail the 5 sources with parenthetical explanations, and final sentences describe output and uniqueness. Every sentence adds value—no wasted words. It's appropriately sized for a complex tool and front-loads key information about being a composite analysis 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?

Given the tool's complexity (4 parameters, multiple data sources, no output schema), the description provides strong context: it lists all 5 data sources, explains the composite nature, and notes the unified report output. However, without an output schema, it could better describe the report structure or format. The annotations cover safety aspects, and parameter documentation is mostly adequate, making it nearly complete for agent 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 75% (3 of 4 parameters have descriptions), providing good baseline documentation. The description adds meaningful context: it explains that 'github_url' improves changelog accuracy (beyond schema's 'optional' note) and implies 'max_length' controls report size (though not explicitly stated). However, it doesn't fully explain parameter interactions (e.g., how ticker affects finance data when blank).

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

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

The description explicitly states the tool performs 'company intelligence' and 'analysis', specifying it queries 5 unique sources (SEC EDGAR, USASpending.gov, GDELT, product changelog, Yahoo Finance) to return a unified report. It clearly distinguishes from siblings by emphasizing it's a 'composite' tool combining sources that are available individually in other tools like extract_sec_filings, extract_govcontracts, extract_gdelt, extract_finance_landscape, and extract_changelog.

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

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

The description provides explicit guidance on when to use this tool: it's 'the most complete single-call company analysis available' and 'this combination is not available in any other MCP server.' This clearly indicates it should be used when comprehensive multi-source analysis is needed, rather than the individual sibling tools that query single sources. It also implies when not to use it (when only specific single-source data is required).

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, but the description adds valuable behavioral context beyond this. It specifies that the tool queries five specific external sources simultaneously, returns a unified timestamped report, and has a practical limitation ('Up to 5 tickers' mentioned in schema). However, it doesn't disclose rate limits, authentication needs, or potential data freshness issues that would be helpful for an agent.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose and listing the five data sources. Every sentence adds value, though the Bloomberg Terminal comparison at the end feels slightly promotional rather than strictly functional. The structure efficiently communicates the tool's scope without unnecessary 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 (composite queries across five sources) and lack of output schema, the description does a good job explaining what the tool returns ('unified 5-source timestamped report') and its composite nature. However, it could better address how the five data streams are integrated or what the report structure looks like, which would help an agent understand the output format.

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?

With 75% schema description coverage, the baseline is 3. The description doesn't add meaningful parameter semantics beyond what's already in the schema descriptions (e.g., it doesn't explain how 'max_length' affects the report or provide examples for 'github_query' beyond what the schema says). The description focuses on the tool's composite nature rather than parameter details.

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

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

The description explicitly states the tool's purpose: it's a composite financial intelligence tool that simultaneously queries five specific data sources (Yahoo Finance, Hacker News, Reddit, GitHub, product changelog) for ticker symbols. It clearly distinguishes itself from sibling tools by being the only one that combines these five sources into a unified report, unlike specialized siblings 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: for developers needing a multi-source financial intelligence report. However, it doesn't explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., use extract_yc for Y Combinator data instead). The Bloomberg Terminal comparison implies a unique value proposition but doesn't offer concrete guidance on tool selection.

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 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 adds valuable behavioral context beyond annotations. While annotations indicate read-only and open-world operations, the description reveals this is a composite tool that queries four sources simultaneously, returns a unified timestamped report, and has specific output format expectations (answers three questions). It doesn't mention rate limits, authentication needs, or data freshness, but provides meaningful operational 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 efficiently structured and front-loaded: it immediately states this is a composite tool, lists the four sources, specifies the three questions it answers, and describes the output format. Every sentence adds value without redundancy. The final sentence about uniqueness appropriately emphasizes differentiation without being 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 tool's complexity (composite queries across four sources) and lack of output schema, the description does well to specify what questions it answers and the report format. However, it doesn't detail potential limitations (like data availability across sources) or error handling. With annotations covering safety aspects and good schema coverage, the description provides solid context but could be more complete about operational constraints.

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?

With 67% schema description coverage, the description doesn't add significant parameter semantics beyond what the schema provides. The schema already documents the three parameters well, including the optional github_url and default max_length. The description mentions using the query for GitHub/changelog sections when github_url is omitted, but this is already implied in the schema descriptions. Baseline 3 is appropriate given the good 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 explicitly states the tool's purpose: it's a composite intelligence tool that simultaneously queries four specific sources (USASpending.gov, GitHub, Hacker News, product changelog) to answer three specific questions about government contract winners, development activity, and developer community awareness. It clearly distinguishes itself from siblings by being 'Unique — not available in any other MCP server' and by combining these four sources into one unified report.

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 about when to use this tool: for analyzing companies/keywords/NAICS codes across government contracts, development activity, and community awareness. It implicitly distinguishes from siblings by being a composite tool, but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools (like extract_govcontracts, extract_github, extract_hackernews).

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 context about 'real-time dev/tech community sentiment' and 'post timestamps,' which are behavioral traits not in annotations. However, it lacks details on rate limits, authentication needs, or specific extraction behaviors like pagination or error handling.

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

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

The description is concise with two sentences, front-loaded with the core purpose. Each sentence adds value: the first states the action and resource, the second provides context on data characteristics. There is no wasted text, though it could be more structured with explicit usage guidelines.

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 annotations covering read-only and open-world aspects, the description is moderately complete. It adds context like 'real-time sentiment' and 'timestamps,' but lacks details on return format, error cases, or integration with sibling tools. For a tool with 2 parameters and 50% schema coverage, more parameter explanation would improve 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 'url' has a description). The description does not add meaning for parameters beyond the schema; it mentions 'top stories or search results' but does not explain how 'url' or 'max_length' relate to this. With low schema coverage, the description fails to compensate, leaving parameters partially undocumented.

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 top stories or search results from Hacker News, specifying the resource (Hacker News) and action (extract). It distinguishes from siblings by mentioning 'dev/tech community sentiment' and 'post timestamps,' which are unique to this data source compared to other extract tools like extract_github or extract_scholar.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions 'top stories or search results' but does not specify scenarios, prerequisites, or exclusions. For example, it does not clarify when to choose this over other extract tools like extract_github for tech-related 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 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 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 declare readOnlyHint=true and openWorldHint=true, indicating safe read operations with external data. The description adds valuable context beyond annotations by specifying the scraping behavior, source website, data freshness aspect, and the types of fields returned. No contradiction with annotations exists.

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 perfectly concise with two sentences that each earn their place: first sentence defines purpose and usage, second sentence specifies return data. No wasted words, front-loaded with the 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 scraping tool with readOnlyHint and openWorldHint annotations but no output schema, the description does well by specifying the source website, example URL pattern, and exact return fields. It could be more complete by explaining pagination behavior or error cases, but covers the essential context given the tool's complexity.

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

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

Schema description coverage is 50% (only the 'url' parameter has a description). The description adds some value by providing a concrete URL example pattern and mentioning the purpose ('to find startups in a space'), but doesn't explain the 'max_length' parameter's purpose or semantics beyond what the schema provides. With partial schema coverage, the description compensates somewhat but not fully.

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 ('Scrape YC company listings'), target resource ('YC company listings'), and distinguishes from siblings by specifying the YC domain. It provides a concrete example URL pattern and lists the exact data returned (name, batch, tags, description, freshness timestamp).

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 find startups in a space') with a specific URL pattern example. However, it doesn't explicitly state when NOT to use it or name alternatives among the sibling tools, though the specificity of the YC domain implicitly differentiates it from other 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 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.