github-mcp-server

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

The annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds useful behavioral detail by specifying the exact metrics returned and the side-by-side comparison format, which goes beyond the annotations. It does not disclose potential rate limits or output structure, but the substantial added context warrants a score above baseline.

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 with no wasted words. The first sentence front-loads the purpose and output, while the second provides actionable usage guidance. Every sentence earns its place, making it an ideal concise description.

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 simple single-parameter schema, strong read-only annotations, and no output schema, the description is complete enough. It covers what the tool does, what it returns, and when to use it, leaving no significant gaps for an AI agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100% and the 'repos' parameter is well documented in the schema with format examples and minimum count. The description adds minimal extra meaning, only reinforcing that the input is a list of GitHub repositories. Baseline 3 is appropriate since the schema already carries the semantic weight.

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

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

The description uses a specific verb ('Compare') with a clear resource ('multiple GitHub repositories') and lists the key metrics returned (stars, forks, issues, language, comparative analysis). It clearly distinguishes this tool from sibling tools like get_repo_stats (single-repo stats) and search_repos (search), establishing a unique comparison-focused purpose.

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 the tool: 'Use for choosing between similar projects or analyzing competitive landscape.' This provides clear usage context, though it does not explicitly mention when not to use it or name alternatives. The sibling tool names imply alternatives, but the lack of direct exclusion prevents a full 5.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, establishing this as a safe, read-only operation. The description adds valuable context by detailing the return values (percentage breakdown, dominant language, file counts), which is essential since no output schema is provided. It does not contradict annotations.

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

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

The description is compact and efficient: two sentences that convey purpose, output details, and usage context with no redundancy or unnecessary words.

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

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

For a simple read-only tool with two parameters and no output schema, the description fully explains what it does and what it returns. It covers the essential context for an agent to select and invoke the tool correctly, and it differentiates from sibling tools.

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

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

The input schema has 100% coverage with clear descriptions and examples for both parameters (owner and repo). The description does not add additional parameter-level detail, but with high schema coverage, baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Analyze the programming language composition of a GitHub repository.' It specifies the exact output (percentage breakdown, dominant language, file counts) and is easily distinguished from siblings like get_repo_stats or search_repos, which focus on general stats or searching.

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 the tool: 'Use for understanding project tech stack or evaluating language distribution.' It does not provide explicit alternatives or when-not-to-use guidance, but the use case is clear and context is sufficient for a focused tool.

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 declare readOnlyHint=true, so the description doesn't need to state safety. It adds value by enumerating the specific metrics returned (stars, forks, issues, etc.) and the purpose, which goes beyond the annotations. There is no contradiction.

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 action and resource. The second sentence efficiently lists return metrics and use case. No redundant words or repetition of schema 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?

For a simple 2-parameter tool with no output schema, the description is complete: it explains what the tool does, what data it returns, and its intended use. The schema and annotations cover the remaining context (parameters and safety).

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

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

Schema description coverage is 100%, with owner and repo fully documented in the input schema. The description adds no additional parameter semantics beyond the schema, such as format or special behavior, so the baseline of 3 is appropriate.

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

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

The description clearly states the tool 'Fetches comprehensive statistics for a specific GitHub repository', specifying the verb and resource. It distinguishes from siblings by focusing on individual repo stats, whereas compare_repos compares, get_repo_languages handles languages, and search_repos searches.

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: use when you need stats for a specific repo, and it mentions usefulness for assessing popularity and maintenance. However, it doesn't explicitly mention when not to use it or alternatives, though sibling names imply differentiation.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description is not required to explain safety. It adds value by describing the return contents (description, star count, language, last update timestamp) and the sorting behavior (relevance or metrics), which is behavioral context beyond the annotations.

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

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

The description is compact and front-loaded, with the core action stated in the first phrase. It avoids redundancy and uses only two sentences to convey purpose, behavior, and use case, earning its place entirely.

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, the description provides sufficient context: it states the input (keywords), optional sort behavior, and the output fields. There is no output schema, so the description appropriately covers return values. It does not discuss pagination or limits beyond the schema, but the schema already documents max_results.

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

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

Schema coverage is 100%, with parameter descriptions already explaining query, sort, and max_results. The description only reinforces the concept of 'keywords' and 'metrics' without adding new parameter-level details, so it meets the baseline but does not exceed 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 uses a specific verb ('Search') and resource ('GitHub repositories') and clearly communicates the scope: finding repos by keywords, sorted by relevance or metrics. It distinguishes itself from sibling tools like compare_repos and get_repo_stats by focusing on discovery rather than analysis.

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 the tool ('Use for finding projects, libraries, or code samples related to specific topics'), giving clear context. It does not mention alternatives or exclusions, but the use case is sufficiently distinct from the sibling tools.

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