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

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, open-world), but the description adds valuable context by specifying what data is extracted (skills, languages, patterns) and the structured nature of the output. It doesn't contradict annotations and enhances understanding of the tool's scope.

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 in the first sentence, followed by specifics on returns. Both sentences earn their place by adding clarity without redundancy, 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 (analyzing profiles), rich annotations, and presence of an output schema, the description is largely complete. It outlines what the tool does and returns, though it could benefit from more usage guidance. The output schema handles return values, so no need to detail them here.

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 0% schema description coverage, the schema only indicates a 'username' parameter without details. The description compensates by clarifying it's a 'GitHub username' and implies the analysis is user-centric, though it doesn't specify format constraints like the 39-character max length from the schema.

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

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

The description clearly states the tool's purpose with specific verbs ('analyze', 'extract') and resources ('GitHub user's profile'), listing concrete outputs like skills, languages, and contribution patterns. It distinguishes itself from siblings by focusing on comprehensive profile analysis rather than specific tasks like checking issues or comparing repos.

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 doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'opencollab_match_me' or 'opencollab_contributor_leaderboard', which might overlap in analyzing user 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 provide safety and idempotency hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true). The description adds valuable behavioral context beyond annotations: it specifies the availability criteria ('checks assignees and linked pull requests') and clarifies what 'available' means ('no one has claimed it or opened a PR for it'). This enhances transparency without contradicting 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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds critical behavioral details. Both sentences earn their place by providing essential information without redundancy. The structure is efficient and zero-waste.

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

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

Given the tool's moderate complexity (checking issue availability), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is complete enough. It explains the tool's purpose, usage context, and behavioral criteria, leaving no significant gaps for the agent to understand its function.

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 0%, meaning parameter descriptions are missing in the schema. The tool description does not mention any parameters or their semantics (owner, repo, issue_number). However, since there is only one parameter (a nested object with three properties), the baseline is 4, but the description fails to compensate for the schema gap, resulting in a score of 3 due to lack of parameter guidance.

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

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

The description clearly states the tool's purpose with specific verbs ('check if a GitHub issue is still available') and resources ('GitHub issue'), distinguishing it from siblings like 'opencollab_find_issues' (which finds issues) or 'opencollab_stale_issue_finder' (which finds stale issues). It explicitly defines availability criteria ('no one has claimed it or opened a PR for it'), making the purpose unambiguous.

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

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

The description provides clear context for when to use this tool: to check issue availability based on assignees and linked pull requests. However, it does not explicitly mention when NOT to use it or name specific alternatives among siblings (e.g., 'opencollab_find_issues' for finding issues regardless of availability). The guidance is implicit but sufficient for basic usage.

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 provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering the safety and idempotency profile. The description adds value by specifying what metrics are compared (stars, PR merge rate, activity) and that it provides a recommendation, which are useful behavioral details 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 perfectly concise with two sentences that each earn their place: the first establishes the purpose and scope, the second specifies the outputs. There's zero wasted language, and the information is front-loaded appropriately.

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 that annotations cover safety/idempotency, there's an output schema (so return values don't need explanation in the description), and the description clearly explains what the tool does and what metrics it compares, this is complete for a comparison tool. The description provides exactly what's needed beyond the structured data.

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 0% schema description coverage (the schema has no descriptions for the parameters), the description carries the full burden of explaining parameter meaning. While it doesn't explicitly name the four required parameters (owner_a, repo_a, owner_b, repo_b), it clearly states that it compares 'two GitHub repositories,' which implicitly defines what inputs are needed. This provides good semantic context despite not listing parameters explicitly.

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 ('compare two GitHub repositories side-by-side') and the purpose ('for contributor-friendliness'), distinguishing it from sibling tools like 'opencollab_repo_health' or 'opencollab_similar_repos' which have different analytical focuses. It explicitly mentions the comparative nature and the specific evaluation criteria.

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 (when comparing repositories for contributor-friendliness), but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools. The context is sufficient for an agent to understand the primary use case without explicit exclusions.

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

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

Annotations already provide safety hints (readOnly, non-destructive, idempotent, openWorld), but the description adds valuable behavioral context by specifying what the tool inspects (Dockerfile, CI configs, documentation, etc.) and what it returns (a readiness checklist with difficulty rating), which 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?

The description is front-loaded with the main purpose in the first sentence, followed by specifics in the second, with no wasted words. Every sentence adds value, 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, rich annotations, and the presence of an output schema (which likely covers the return format), the description is mostly complete. It explains what the tool does and returns, though it could benefit from more explicit usage guidelines relative to siblings.

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 0%, but the description does not add any parameter-specific information beyond what the schema provides (owner and repo). However, since there are only 2 parameters and the schema is clear, the baseline is 3 as the description doesn't compensate but the schema handles the basics adequately.

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 with specific verbs ('check', 'looks for') and resources ('repository'), and distinguishes it from siblings by focusing on contribution readiness assessment rather than other repository analysis tasks like health, activity, or issue finding.

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 mentioning what the tool looks for (Dockerfile, CI configs, etc.), but does not explicitly state when to use this tool versus alternatives like 'opencollab_repo_health' or 'opencollab_first_timer_score', which might overlap in assessing repository accessibility.

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 provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context by specifying it returns 'top 10 contributors' and 'ranked by number of commits,' which clarifies the ranking method and output limit beyond what annotations indicate. No contradictions with annotations exist.

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

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

The description is front-loaded with the core purpose in the first sentence and adds specific behavioral details in the second. Both sentences earn their place by providing essential information without redundancy, making it efficiently structured and 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, rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is mostly complete. It clearly states the tool's purpose and key behavioral traits (top 10 ranking by commits). However, it lacks explicit guidance on when to use versus siblings, which slightly reduces completeness.

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

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

Schema description coverage is 0%, but the description doesn't mention parameters at all. The schema defines 'owner' and 'repo' parameters with good descriptions, so the baseline is 3 since the schema handles parameter documentation adequately. The description adds no parameter semantics beyond what the schema provides.

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

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

The description clearly states the tool's purpose with specific verbs ('Get the top contributors') and resources ('of a repository'), including what information is returned ('with their commit counts and profiles'). It distinguishes itself from sibling tools by focusing specifically on contributor ranking rather than analysis, health checks, or issue-related functions.

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 it returns 'top 10 contributors ranked by number of commits,' suggesting it's for ranking analysis. However, it doesn't explicitly state when to use this tool versus alternatives like 'opencollab_analyze_profile' for individual profiles or 'opencollab_repo_health' for broader metrics, leaving some ambiguity about optimal use cases.

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 provide excellent behavioral hints (read-only, non-destructive, idempotent, open-world). The description adds valuable context by specifying which dependency files are checked (package.json, pyproject.toml, etc.) and that it 'shows what libraries and frameworks the project uses' - information not covered by annotations. 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?

Two sentences with zero waste. First sentence states the core purpose, second sentence provides specific file examples and outcome. Every word earns its place, and information is front-loaded appropriately.

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, excellent annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint), and the existence of an output schema, the description provides sufficient context. It explains what the tool does, which files it examines, and what information it reveals. The output schema will handle return value documentation.

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 0%, but the input schema clearly documents the 'owner' and 'repo' parameters with descriptions and constraints. The tool description doesn't add any parameter-specific information beyond what's in the schema, but the schema provides adequate documentation. With only 2 well-documented parameters, this meets the baseline expectation.

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 with specific verbs ('inspect', 'check', 'read', 'show') and resources ('repo's tech stack', 'dependency files', 'libraries and frameworks'). It distinguishes from siblings by focusing specifically on dependency analysis rather than broader repo analysis, profile analysis, or issue tracking.

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 (analyzing a project's dependencies) but doesn't explicitly state when to use this tool versus alternatives like 'opencollab_repo_languages' or 'opencollab_repo_health'. It provides no guidance on prerequisites, exclusions, or comparison to sibling 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 already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context beyond annotations: it specifies the result limit ('up to 15'), recency filter ('recently created'), and source scope ('public repos'), which helps the agent understand practical constraints.

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

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

Two sentences, front-loaded with the core purpose. Every word earns its place: first sentence defines the tool's function, second sentence adds important behavioral details (limit, recency, scope) 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 moderate complexity (single parameter, read-only operation), rich annotations, and presence of an output schema, the description is complete enough. It covers purpose, constraints, and scope, leaving detailed parameter and return value documentation to the structured fields.

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 0%, so the description carries full burden. It explains that the parameter is for 'a given programming language,' which clarifies the purpose of the single 'language' parameter. However, it doesn't provide format examples or constraints beyond what's implied.

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 ('Find beginner-friendly open-source issues') and target resource ('labelled "good first issue" for a given programming language'), distinguishing it from siblings like opencollab_analyze_profile or opencollab_recent_prs. It provides precise scope about what kind of issues are retrieved.

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 (finding beginner-friendly issues for a specific language) but doesn't explicitly state when to use this tool versus alternatives like opencollab_weekend_issues or opencollab_stale_issue_finder. No exclusions or prerequisites are mentioned.

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

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

Annotations provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds behavioral context by specifying search criteria (mentorship labels, contributing guides, programs), which isn't in the annotations. However, it doesn't detail rate limits, authentication needs, or output format, leaving some gaps.

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

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

The description is front-loaded with the main purpose in the first sentence, followed by specific search criteria in the second. Both sentences are necessary and earn their place, with no wasted words. 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 has annotations (covering safety and idempotency) and an output schema (so return values are documented elsewhere), the description is somewhat complete. However, it lacks parameter details (0% schema coverage with no compensation) and doesn't fully explain behavioral aspects like search scope or limitations, making it adequate but with 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 0%, and the description doesn't mention the 'language' parameter at all. The schema defines 'language' as a required string for programming language, but the description fails to add any semantic meaning or usage context for this parameter, leaving it undocumented beyond the schema.

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

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

The description clearly states the tool's purpose: 'Find repositories that actively mentor newcomers.' It specifies the verb 'find' and resource 'repositories' with the qualification 'that actively mentor newcomers.' However, it doesn't explicitly differentiate from sibling tools like 'opencollab_find_issues' or 'opencollab_trending_repos' beyond the mentorship 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?

The description implies usage context by mentioning specific criteria (mentorship labels, contributing guides, programs like GSoC). It suggests when to use this tool—for finding repos suitable for beginners—but doesn't explicitly state when not to use it or name alternatives among siblings. The guidelines are helpful but not comprehensive.

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 provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds context about what gets scored (profile completeness, coding activity, language diversity) and that it provides personalized tips, which is useful behavioral detail beyond annotations. No contradictions 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 states the core purpose, and the second elaborates on scoring dimensions and outputs. Every sentence adds value with no wasted words, making it appropriately sized and front-loaded.

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

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

Given the tool's moderate complexity (scoring readiness with personalized tips), annotations cover safety aspects, and an output schema exists (so return values needn't be described), the description is largely complete. It could benefit from more explicit differentiation from siblings, but it adequately conveys the tool's function and scope.

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

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

Schema description coverage is 0%, with only one parameter ('username') documented in the schema without a description. The description doesn't explicitly mention parameters, but it implies the input is a GitHub user ('Rate how ready a GitHub user is'), which aligns with the username parameter. Since there's only one parameter and the description contextually covers it, this compensates well for the low 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 tool's purpose: 'Rate how ready a GitHub user is for open source contributions' with specific scoring dimensions (profile completeness, coding activity, language diversity) and outputs (personalized tips). It distinguishes from siblings like 'opencollab_analyze_profile' by focusing on readiness scoring rather than general analysis, though it doesn't explicitly name alternatives.

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 ('before contributing') but doesn't explicitly state when to use this tool versus alternatives like 'opencollab_contribution_readiness' or 'opencollab_analyze_profile'. No exclusions or prerequisites are mentioned, leaving usage guidance at an implied level.

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, non-destructive, idempotent, and open-world behavior. The description adds valuable context by specifying the exact data sources fetched (issue body, comments, labels, contributing guidelines, directory structure) and the purpose (PR planning), which goes beyond annotations. 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 two sentences, front-loaded with the main purpose and followed by specific data sources. Every sentence adds value: the first states the goal, the second lists exactly what is fetched. No wasted words or 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 (gathering multiple data sources), rich annotations (read-only, idempotent, etc.), and the presence of an output schema (which handles return values), the description is complete. It clearly states the purpose, data gathered, and usage context without needing to repeat structured information.

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 0%, but the description does not mention parameters at all. It implies parameters through context (e.g., 'GitHub issue'), but provides no details on required inputs like owner, repo, or issue number. Baseline is 3 since the schema fully documents the single nested parameter object with good descriptions.

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

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

The description clearly states the tool's purpose with specific verbs ('gather', 'fetches') and resources ('GitHub issue', 'issue body, comments, labels, contributing guidelines, and repo directory structure'). It distinguishes itself from sibling tools by focusing on comprehensive context gathering for PR planning rather than analysis, matching, or health checks.

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 ('so the AI can draft a PR plan') and specifies what data is gathered, but does not explicitly state when to use this tool versus alternatives like 'opencollab_issue_complexity' or 'opencollab_find_issues'. It provides clear context but lacks explicit exclusions or named alternatives.

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

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds that it 'Produces an impact tier (MASSIVE/HIGH/MEDIUM/LOW) and a suggested resume line,' which gives useful context on output format beyond annotations, but doesn't detail how the estimation works or any limitations.

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 concise sentences with zero waste. It front-loads the purpose and efficiently states the output format, making it easy to scan and understand quickly.

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

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

Given the annotations cover safety and idempotency, and an output schema exists (implied by context signals), the description is reasonably complete. It specifies the output includes an impact tier and resume line, which helps the agent understand what to expect, though it could benefit from more behavioral context like estimation criteria.

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 0%, but the input schema clearly defines 'owner' and 'repo' parameters with descriptions. The tool description doesn't add any parameter details beyond what the schema provides, so it meets the baseline of 3 for adequate schema coverage without extra value.

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: 'Estimate the impact of contributing to a specific repository.' It specifies the action (estimate impact) and resource (repository), but doesn't differentiate from siblings like 'opencollab_contribution_readiness' or 'opencollab_repo_health' which might overlap in assessing repository suitability for contributions.

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

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

No guidance is provided on when to use this tool versus alternatives. It doesn't mention sibling tools like 'opencollab_contribution_readiness' for readiness assessment or 'opencollab_repo_health' for health metrics, leaving the agent to guess based on tool names alone.

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 provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), indicating this is a safe, repeatable read operation. The description adds valuable context beyond annotations by detailing the analysis factors (issue body length, comments, labels, linked PRs, discussion depth) and the output (complexity rating), which helps the agent understand the tool's behavior and scope. No contradictions with annotations exist.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by specific analysis details in the second. Every sentence adds value without redundancy, and the structure is clear and efficient, making it easy for an agent to parse and understand quickly.

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

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

Given the tool's moderate complexity (analysis of multiple factors), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is complete enough. It details the analysis factors and output, aligning well with the structured data to provide a holistic understanding without unnecessary repetition.

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

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

The input schema has 0% description coverage, but the description compensates by clarifying that the tool analyzes a 'specific GitHub issue,' implying parameters like repository owner, repo name, and issue number. Although it does not explicitly list or explain each parameter, it provides enough semantic context to infer the required inputs. With 0% schema coverage, the description does well to add meaning, but could be more explicit about parameter roles.

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 ('Estimate the complexity') and target resource ('a specific GitHub issue'), with explicit details about what factors are analyzed (issue body length, number of comments, labels, linked PRs, and discussion depth) and the output (complexity rating). It distinguishes itself from siblings like 'opencollab_find_issues' or 'opencollab_stale_issue_finder' by focusing on complexity analysis rather than discovery or filtering.

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 it analyzes a 'specific GitHub issue,' suggesting it should be used when an issue is already identified. However, it does not explicitly state when to use this tool versus alternatives (e.g., compared to 'opencollab_issue_availability' for checking issue status or 'opencollab_find_issues' for discovering issues), nor does it provide exclusions or prerequisites. The guidance is present but limited to implied context.

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

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

Annotations already provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), covering safety and idempotency. The description adds useful context by specifying what information is included (descriptions and open issue counts) and the tool's goal (helping contributors discover label types), which enhances understanding 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 appropriately sized with two sentences that are front-loaded and efficient. The first sentence states the core functionality, and the second adds value by explaining the tool's utility, with no wasted words or 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 low complexity (1 parameter), rich annotations (covering safety and behavior), and the presence of an output schema (which handles return values), the description is complete enough. It effectively communicates the tool's purpose and usage context without needing to repeat structured information.

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 0%, but the description compensates by clarifying that parameters specify a repository ('in a repository'), aligning with the schema's 'owner' and 'repo' fields. It adds semantic meaning by linking parameters to the tool's purpose, though it doesn't detail parameter formats or constraints beyond what the schema provides.

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

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

The description clearly states the tool's purpose with specific verbs ('List all labels') and resources ('in a repository'), and distinguishes it from sibling tools by focusing on labels with descriptions and issue counts. It provides concrete examples of label types (beginner-friendly issues, bugs, features, documentation tasks), making the purpose highly specific and differentiated.

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 contributors to discover labels, but does not explicitly state when to use this tool versus alternatives (e.g., compared to sibling tools like 'opencollab_find_issues' or 'opencollab_issue_complexity'). It provides some context ('Helps contributors discover...') but lacks explicit exclusions or named alternatives.

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

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

The description adds valuable behavioral context beyond annotations: it specifies that it 'detects the user's primary language' and 'returns 10 matching good-first-issues', which are not covered by annotations. Annotations already provide safety hints (readOnly, non-destructive, idempotent, openWorld), so the bar is lower. The description doesn't contradict annotations and adds useful operational 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 appropriately sized with two sentences: the first states the core functionality, and the second adds specific behavioral details. Every sentence earns its place by providing essential information without redundancy. It's front-loaded with the main purpose and efficiently 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 (profile analysis + issue matching), rich annotations (readOnly, idempotent, etc.), and the presence of an output schema, the description is complete enough. It covers the purpose, usage context, and key behavioral traits (language detection, 10 issues). The output schema handles return values, so the description doesn't need to explain them.

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 0% (the parameter 'username' has only basic validation in schema). The description doesn't mention the parameter at all, failing to compensate for the lack of schema documentation. However, with only 1 parameter, the baseline is 4, but the description provides no parameter information, so it scores lower. It implies the tool takes a GitHub username but doesn't explicitly state it.

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 with specific verbs ('analyze a GitHub profile' and 'find issues matched to that user's top skills') and resources ('GitHub profile', 'issues'). It distinguishes from siblings like 'opencollab_analyze_profile' (which only analyzes) and 'opencollab_find_issues' (which finds issues without profile analysis) by combining both functions in one step.

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: for analyzing a GitHub profile and instantly finding matched issues in one step. It implies when not to use it (e.g., if you only need analysis without issue matching, use 'opencollab_analyze_profile'; if you need issue finding without profile analysis, use 'opencollab_find_issues'). The 'All-in-one' phrasing highlights its integrated nature versus alternatives.

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

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

Annotations already provide key behavioral hints: readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, indicating it's a safe, repeatable read operation. The description adds some context by mentioning the insights gained (accepted PR types, merge speed, active reviewers), but doesn't disclose additional traits like rate limits, authentication needs, or pagination behavior. With annotations covering the safety profile, a 3 is appropriate as the description adds moderate value without contradictions.

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: the first sentence clearly states the purpose, and the second sentence elaborates on the benefits without unnecessary details. Every sentence earns its place by adding value, 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 (a read operation with repository input), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is mostly complete. It explains the purpose and usage context but could be more explicit about sibling tool differentiation. Overall, it provides sufficient guidance for an AI agent to understand when and how to use it.

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

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

The input schema has 1 parameter (a nested object with 'owner' and 'repo'), and schema description coverage is 0%, meaning the schema lacks descriptions for these fields. The description doesn't add any parameter-specific information beyond implying repository context. Since there are 0 parameters with explicit semantics in the description, and schema coverage is low, the baseline is 3—it doesn't compensate for the coverage gap but doesn't worsen it either.

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: 'Show recently merged pull requests in a repository.' It specifies the verb ('show') and resource ('recently merged pull requests'), making the action explicit. However, it doesn't explicitly differentiate from sibling tools like 'opencollab_repo_activity_pulse' or 'opencollab_contributor_leaderboard', which might also involve PR-related data, 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 context by stating it 'Helps contributors see what kind of PRs get accepted, how fast they're merged, and who the active reviewers are.' This suggests it's for analysis and learning, but it doesn't explicitly state when to use this tool versus alternatives (e.g., compared to 'opencollab_find_issues' or 'opencollab_repo_activity_pulse') or any exclusions, leaving some ambiguity.

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 provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable context beyond this: it specifies the 30-day timeframe and the types of metrics returned (commit frequency, issue/PR activity, momentum). This helps the agent understand the tool's scope and output format, compensating for the lack of output schema details in the description.

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 in the first sentence, followed by specific details in the second. Both sentences earn their place by clarifying timeframe and metrics. No wasted words or redundancy, making it highly efficient for an AI agent to parse.

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

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

Given the tool's moderate complexity (analyzing repo activity), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is reasonably complete. It specifies the 30-day window and key metrics, though it could benefit from mentioning sibling differentiation or parameter context. The annotations and output schema reduce the burden on the description.

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 0%, but the description doesn't mention parameters at all. The input schema defines 'owner' and 'repo' parameters with good descriptions in the schema itself. Since the description adds no parameter information, it doesn't compensate for the low coverage, but the schema handles the basics adequately, resulting in a baseline score of 3.

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

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

The description clearly states the tool's purpose: 'Get an activity pulse for a repo over the last 30 days' with specific metrics listed (commit frequency, issue open/close rate, PR activity, momentum trend). It uses a specific verb ('Get') and resource ('repo'), but doesn't explicitly differentiate from siblings like 'opencollab_repo_health' or 'opencollab_trending_repos' which might overlap in analyzing repository activity.

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 a 30-day timeframe and specific metrics, but doesn't compare to siblings like 'opencollab_repo_health' (which might offer broader health metrics) or 'opencollab_trending_repos' (which might focus on popularity). No exclusions or prerequisites are stated.

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 read-only, non-destructive, idempotent, and open-world hints, which the description does not contradict. The description adds valuable context by listing the specific checks performed (activity recency, community size, PR merge patterns, open issues, essential files), enhancing transparency about what the tool evaluates beyond the safe operational profile indicated 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 score range, followed by a concise bullet-style list of checks. Every sentence adds value without redundancy, making it efficiently structured and easy to parse for key information.

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

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

Given the tool's moderate complexity, rich annotations (covering safety and behavior), and the presence of an output schema (which handles return values), the description is complete. It clearly explains what the tool does, the scoring aspects, and aligns with annotations, leaving no significant gaps for agent understanding.

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 0%, but the input schema clearly defines 'owner' and 'repo' parameters with descriptions. The tool description does not add parameter details, but since there are only two straightforward parameters (repository identifier), the schema alone is sufficient for understanding. The description's focus on scoring criteria compensates adequately for the lack of parameter elaboration.

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 ('Score a repository's health and contributor-friendliness') with a defined output range (0-100). It distinguishes from siblings by focusing on comprehensive health assessment rather than specific aspects like activity pulse, languages, or issue analysis, making its purpose distinct and well-defined.

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 evaluating repository health and contributor-friendliness, but does not explicitly state when to use this tool versus alternatives like 'opencollab_repo_activity_pulse' or 'opencollab_contribution_readiness'. No exclusions or prerequisites are mentioned, leaving usage context partially inferred rather than clearly 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 already cover key behavioral traits (read-only, non-destructive, idempotent, open-world), so the bar is lower. The description adds useful context about what the tool returns ('detailed language breakdown,' 'percentage of each programming language'), but does not disclose additional behavioral aspects like rate limits, authentication needs, or error conditions. 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 appropriately sized and front-loaded, with the first sentence stating the core purpose, followed by two concise sentences that add value without redundancy. Every sentence earns its place by clarifying the output and usage context, with zero waste.

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

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

Given the tool's low complexity (simple read operation with two parameters), rich annotations (covering safety and behavior), and the presence of an output schema (which handles return values), the description is complete enough. It provides purpose, output details, and usage context without needing to explain parameters or behavioral traits already covered elsewhere.

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 0%, but the input schema has a single nested object 'params' with 'owner' and 'repo' properties that are well-described in the schema itself (e.g., 'Repository owner (e.g., 'facebook')'). The description does not add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 without compensating for the low 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 ('Get a detailed language breakdown') and resource ('for a repository'), distinguishing it from siblings by focusing on programming language analysis rather than issues, PRs, or other repository metrics. It explicitly mentions 'percentage of each programming language used in the codebase,' which is unique among the listed 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 ('Helps you decide if you have the right skills before contributing'), which implicitly suggests it's for pre-contribution assessment. However, it does not explicitly state when not to use it or name specific alternatives among siblings, such as 'opencollab_contribution_readiness' which might overlap in purpose.

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 annotations already provide key behavioral hints: readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, indicating a safe, repeatable query with external data. The description adds context about the tool's focus on 'welcoming to contributors,' which isn't covered by annotations, but it doesn't disclose additional traits like rate limits, authentication needs, or output format details. 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 appropriately sized and front-loaded: the first sentence directly states the purpose, and the second adds usage context. Both sentences earn their place by providing essential information without redundancy or fluff, making it easy to scan and understand quickly.

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

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

Given the tool's moderate complexity (a similarity search with 2 sub-parameters), annotations cover safety and behavior well, and an output schema exists (so return values needn't be explained). The description adds purpose and usage context, but parameter semantics are lacking due to 0% schema coverage. Overall, it's mostly complete but could benefit from parameter details.

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

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

Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool has 1 parameter (an object with 'owner' and 'repo' sub-parameters). The description doesn't add any semantic details about these parameters beyond implying they refer to a repository ('repo X'). It doesn't explain format, constraints, or examples, leaving 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 tool's purpose: 'Find repositories similar to a given one based on topics and language.' This specifies the verb ('Find'), resource ('repositories'), and criteria ('similar... based on topics and language'). However, it doesn't explicitly differentiate from sibling tools like 'opencollab_compare_repos' or 'opencollab_trending_repos,' which may also involve repository comparisons or discovery.

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 usage: 'If you like contributing to repo X, this finds other repos in the same domain that are also welcoming to contributors.' This implies the tool is for finding similar, contributor-friendly repositories, which helps guide when to use it. However, it doesn't explicitly state when not to use it or name alternatives among the sibling tools, such as for non-contribution-related similarity 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 already provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), covering safety and idempotency. The description adds valuable context by specifying the criteria ('older than 30 days with no assignees') and the outcome ('hidden easy wins'), which clarifies what the tool returns beyond just being a read operation. 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 front-loaded with the core purpose in the first sentence and adds clarifying details in the second. It uses no wasted words, efficiently conveying the tool's function and criteria in two concise sentences, making it easy to scan and understand quickly.

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

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

Given the tool's low complexity (2 parameters, no nested objects), rich annotations (covering read-only, non-destructive, idempotent, open-world behavior), and the presence of an output schema (which handles return values), the description is mostly complete. It specifies the filtering criteria and purpose well, though it lacks parameter details, which is partially mitigated by the structured fields.

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 0%, meaning parameters are undocumented in the schema. The description does not mention any parameters, such as 'owner' and 'repo', leaving their semantics unexplained. However, since there are only 2 parameters and the tool's purpose is clear, the baseline score of 3 reflects minimal adequacy without compensating for the coverage 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 tool's purpose with specific verbs ('Find old, unclaimed issues') and resources ('in a repo'), and distinguishes it from siblings like 'opencollab_find_issues' by specifying criteria (older than 30 days, no assignees) and framing them as 'hidden easy wins'. This provides precise differentiation beyond generic issue-finding.

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 stating 'Find old, unclaimed issues... that no one is working on', suggesting it's for identifying low-hanging fruit or abandoned tasks. However, it does not explicitly mention when not to use it or name alternatives among siblings, such as 'opencollab_find_issues' for broader searches, leaving some guidance implicit rather than explicit.

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 provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), so the description does not need to repeat these. It adds useful context by specifying the search criteria ('recently created repos with good-first-issue labels') and sorting method ('by stars'), which are not covered by annotations. 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 the main purpose in the first sentence and adds specific details in the second. Both sentences earn their place by clarifying scope and sorting without any wasted words. The structure is efficient and easy to parse.

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

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

Given the tool's moderate complexity (1 parameter, annotations provide safety info, output schema exists), the description is mostly complete. It explains what the tool does and how results are filtered and sorted. However, it lacks details on the 'language' parameter's effect, which is a gap since the output schema's existence doesn't fully compensate for this omission in the description.

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

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

The input schema has 1 parameter with 0% description coverage in the schema itself (the schema only provides a title and type). The description does not mention the 'language' parameter at all, failing to compensate for the low schema coverage. This leaves the parameter's role and impact on the search undocumented beyond the schema's basic field definition.

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

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

The description clearly states the tool's purpose with specific verbs ('Find trending repositories', 'Searches for recently created repos') and resources ('repositories', 'repos with good-first-issue labels'). It distinguishes itself from siblings like 'opencollab_find_issues' by focusing on trending repositories seeking contributors rather than general issue discovery.

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 trending repositories actively seeking contributors, sorted by stars. However, it does not explicitly mention when not to use it or name specific alternatives among the sibling tools, such as 'opencollab_find_issues' for general issue searching.

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 provide key behavioral hints: readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds context by specifying the types of issues (low-effort tags) and the time frame (weekend or 1-2 hours), which is useful beyond annotations. However, it does not disclose additional traits like rate limits, authentication needs, or detailed search behavior, so it earns a baseline 3 for adding some value without contradictions.

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 main purpose in the first sentence and adds necessary details in the second. Both sentences earn their place by defining the tool's scope and search criteria without redundancy. It is appropriately sized for the tool's complexity, with zero waste or unnecessary elaboration.

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 (search with filtering), rich annotations, and the presence of an output schema, the description is mostly complete. It clearly states what the tool does and the context for use. However, it lacks details on the 'language' parameter's semantics, which is a gap since the schema coverage is low. With output schema handling return values, the description is sufficient but not fully comprehensive, warranting a score of 4.

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

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

The input schema has 1 parameter with 0% description coverage, as the schema only provides a basic description ('Programming language'). The description does not mention or explain the 'language' parameter at all, failing to compensate for the low schema coverage. This leaves the parameter's role in the search (e.g., how it filters issues) undocumented, resulting in a score of 2 for minimal added meaning.

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

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

The description clearly states the tool's purpose with specific verbs ('Find', 'Searches for') and resources ('small, quick issues', 'issues labelled documentation, typo, test, chore, or other low-effort tags'). It distinguishes itself from sibling tools like 'opencollab_find_issues' by specifying the type of issues (weekend-friendly, low-effort) and the specific labels it searches for, making its scope explicit and unique.

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 finding 'small, quick issues perfect for a weekend or 1-2 hour contribution.' It implies usage by specifying the target audience and time constraints. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'opencollab_find_issues' for general issue searches, which limits the score to 4.

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