Analyze the programming language composition of a GitHub repository. Returns percentage breakdown of languages used, dominant language, and file counts per language. Use for understanding project tech stack or evaluating language distribution.
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | Repository name (e.g. 'cpython', 'go') | |
| owner | Yes | Repository owner GitHub username or organization (e.g. 'python', 'golang') |
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.
Add one secure layer between your agents and this server.
Each tool has a clear primary purpose: search, single-repo stats, language breakdown, and multi-repo comparison. The only potential confusion is between get_repo_stats and compare_repos, as both return metrics, but the singular/comparative distinction is clear from names and descriptions.
All tool names follow a consistent verb_noun snake_case pattern (compare, get, get, search). The pattern is predictable and matches the GitHub domain.
With 4 tools, the server is slightly on the smaller side but appropriately scoped for repository analysis. It does not feel bloated or excessively thin for the apparent purpose.
The toolset covers search, stats, language, and comparison, but lacks basic repository metadata retrieval (e.g., description, topics, README) and contributor/commit details. These gaps may require workarounds, but core analysis workflows are covered.