Compare multiple GitHub repositories side-by-side with key metrics. Returns star counts, fork counts, issues, primary language, and comparative analysis for each repository. Use for choosing between similar projects or analyzing competitive landscape.
| Name | Required | Description | Default |
|---|---|---|---|
| repos | Yes | List of repositories to compare (minimum 2) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds value by disclosing the exact output categories (stars, forks, issues, primary language, comparative analysis), which goes beyond the annotations' minimal safety hints without contradicting them.
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 exactly two sentences, front-loaded with the core purpose and immediately followed by output details and use case. Every word earns its place; no filler 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 simplicity (one array parameter, read-only, no output schema), the description fully covers what it does, what it returns, and when to use it. No missing information that would hinder correct selection or invocation.
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 fully documents the 'repos' parameter, including format and minimum count (100% schema description coverage). The description merely echoes 'multiple' without adding syntax or non-obvious constraints, so the baseline score 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 a specific verb ('Compare'), resource ('GitHub repositories'), and scope ('side-by-side with key metrics'). It explicitly lists the metrics returned and distinguishes itself from siblings like get_repo_languages and get_repo_stats by focusing on multi-repo comparison.
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 the tool: 'choosing between similar projects or analyzing competitive landscape.' It does not explicitly name alternatives or when-not-to-use scenarios, but the usage context is specific and unambiguous.
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 targets a distinct task: searching, analyzing a single repo, analyzing language composition, and comparing multiple repos. Even though stats and compare both return metrics, their purposes are clearly separated (single vs. multi-repo), so no ambiguity.
All tool names follow the verb_noun pattern in snake_case (compare_repos, get_repo_languages, get_repo_stats, search_repos). The verbs are semantically appropriate and consistent in style.
The server is scoped to repository analytics and discovery, and four tools cover the core workflows without being sparse or bloated. Each tool has a clear purpose and earns its place.
The tool set covers the main steps of repository analysis: finding repos, getting overall stats, examining language breakdown, and comparing multiple candidates. Minor gaps exist (e.g., no direct tool for fetching README or file structure), but the provided surface is sufficient for the implied use case.