Server Quality Checklist

Tool Scores

• get_issueA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description must carry the behavioral burden. The verb 'Get' signals a read-only operation, and the description says it returns details. However, it does not disclose potential edge cases such as 404s, private-repo authentication requirements, or rate limits. This is acceptable for a simple read tool but not richly transparent.

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

  Conciseness5/5

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

  Each sentence earns its place: a front-loaded definition, two concrete trigger examples, and a compact argument list. There is no fluff or redundant restating of the schema.

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

  Completeness4/5

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

  For a simple three-parameter read operation with an output schema present, the description covers what the tool acts on, when to use it, and what each argument means. A minor gap is not naming list_issues as the alternative for browsing multiple issues, but this does not impede correct invocation.

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

  Parameters4/5

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

  The input schema provides no parameter descriptions, so the description must compensate. It defines all three required parameters: owner, repo, and issue_number, including the helpful hint that issue_number appears in the GitHub URL after /issues/. This is sufficient for an agent to supply correctly formed arguments.

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

  Purpose5/5

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

  The first sentence names a specific verb ('Get'), a resource ('a specific GitHub issue'), and the scope ('full details and description'). The focus on a single issue clearly distinguishes it from listing tools like list_issues. The examples reinforce the intended object without ambiguity.

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

  Usage Guidelines4/5

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

  The description explicitly states when to use the tool: when the user wants to read the body/description of an issue. It provides two concrete example queries. It does not explicitly contrast with list_issues, but the single-issue framing is clear enough for correct selection.

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

• list_issuesA
  4.3/5.0
  Behavior3/5

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

  There are no annotations, so the description carries the full burden, but it only states the list behavior and parameter defaults. It does not mention authentication, ordering, whether pull requests are included in issue results, or other non-obvious API behavior.

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

  Conciseness5/5

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

  The description is compact and front-loaded: one sentence says what it does, another says when to use it with two examples, then a clear Args block follows. There is no filler or repetition.

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

  Completeness4/5

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

  For a simple list endpoint with an output schema, the description covers the core invocation requirements. It could be more complete by noting GitHub API quirks like the issues endpoint also returning pull requests, but that is an edge case.

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

  Parameters5/5

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

  Schema descriptions are absent (0% coverage), and the Args section fully compensates: owner is defined as a GitHub username/organisation, state lists its allowed values and default, and limit gets its numeric range and default. This provides exactly the meaning an agent needs beyond bare schema types.

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

  Purpose5/5

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

  The description opens with 'List issues for a GitHub repository,' a specific verb and resource that clearly separates it from siblings like get_issue and list_pull_requests. The examples ('bugs, feature requests, or tasks') reinforce that this targets issue lists rather than repositories or PRs.

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

  Usage Guidelines4/5

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

  'Use this when the user wants to see what bugs, feature requests, or tasks are open' gives explicit triggering scenarios with concrete examples. It does not name alternatives or state when not to use it, so it stops short of a 5.

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

• search_reposA
  4.3/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the behavioral burden. It explains query qualifiers, sort options, and limit behavior, and the search wording implies a read-only operation. However, it does not mention rate limits, authentication needs, result truncation, or error behavior, which would add useful transparency.

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

  Conciseness5/5

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

  The description is well-organized and efficient: an opening purpose statement, a clear usage trigger, and a compact Args section. Every sentence adds value, and the most important information is front-loaded.

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

  Completeness4/5

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

  With an output schema present, the description does not need to explain return values. It sufficiently covers all three parameters, how to construct queries, valid sort values, and limits. Minor omissions like rate limits and pagination are not critical for a simple search tool, but would make it fully complete.

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

  Parameters5/5

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

  The input schema has 0% description coverage, so the description must fully explain the parameters. It does: query includes qualifiers and examples, sort lists all valid values with the default, and limit specifies range and default. This is strong compensation for the bare schema.

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

  Purpose5/5

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

  The description opens with a specific verb and resource — 'Search GitHub repositories' — and immediately distinguishes this tool from siblings like get_repo, list_issues, and get_issue. The example user phrasings further clarify the exact discovery use case.

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

  Usage Guidelines4/5

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

  It explicitly states when to use the tool: 'Use this when the user wants to discover repositories' and gives concrete example queries. It does not explicitly say when not to use it or name alternative tools, but the stated context is clear enough for an agent to select it correctly.

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

• list_pull_requestsA
  4.4/5.0
  Behavior3/5

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

  With no annotations, the description must carry behavioral disclosure, and it does convey that this is a read-only listing action with state and limit controls. However, it does not mention authentication, rate limits, pagination, or ordering behavior. There is no contradiction, but the behavioral profile is only minimally fleshed out.

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

  Conciseness5/5

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

  The description is compact, front-loads the core purpose, and uses a short example block plus an Args list. Every sentence earns its place, with no redundant restatement of the tool name.

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

  Completeness5/5

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

  Given the tool's low conceptual complexity, the presence of an output schema, and full parameter documentation in the description, nothing essential is missing for an agent to select and call the tool. It covers what the tool does, when to use it, and how to fill each argument.

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

  Parameters5/5

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

  Schema description coverage is 0%, yet the description explains all four parameters in plain language: owner, repo, state ('open', 'closed', or 'all'), and limit (1–50, default 20). This fully compensates for the bare schema, adding value beyond the field names and types.

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

  Purpose5/5

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

  The description names a specific verb and resource — 'List pull requests for a GitHub repository' — and adds a distinguishing use case: showing proposed or recently merged code changes. Example queries like 'what PRs are open in fastapi/fastapi?' make it easy for an agent to separate this from sibling tools such as list_issues.

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

  Usage Guidelines4/5

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

  It explicitly says 'Use this when the user wants to see...' and gives realistic example queries. It does not state when-not-to-use or name an alternative tool, but the context is clear enough for list-related PR requests.

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

• get_repoA
  4.5/5.0
  Behavior4/5

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

  No annotations are present, so the description carries the burden of disclosing behavior. 'Get' and 'detailed information' convey a read-only lookup, and the examples (stars, language) imply what fields the response contains. It does not mention auth, rate limits, or error behavior, but the output schema covers return details.

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

  Conciseness5/5

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

  The description is front-loaded with the core purpose, followed by a brief usage rule with examples and a compact Args section. Every sentence adds value, and the examples are well-chosen rather than redundant.

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

  Completeness4/5

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

  For a two-parameter read-only tool, the description covers purpose, usage, and parameter meaning. The presence of an output schema means return values need not be spelled out. It lacks explicit error-handling or authentication notes, but these are minor for a public GitHub lookup.

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

  Parameters5/5

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

  The schema has 0% description coverage, so the Args section must compensate, and it does. It defines 'owner' as a GitHub username or organization, and 'repo' as the repository name (not the full URL), with several examples for each.

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

  Purpose5/5

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

  The opening sentence, 'Get detailed information about a specific GitHub repository,' names a clear verb, object, and scope. The three example queries reinforce that the tool targets a single, known repository, distinguishing it from sibling tools like search_repos or list_issues.

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

  Usage Guidelines4/5

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

  The description explicitly says 'Use this when the user asks about a known repository' and gives concrete query examples. It does not list alternative tools or state when not to use it, so it stops short of full when/when-not guidance.

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