Deep metadata for public GitHub repositories: stars, forks, language breakdown, topics, license, README content, contributor count, latest release, and commit recency. Use to see what a company or project is actually building. No GitHub token required.
| Name | Required | Description | Default |
|---|---|---|---|
| repos | Yes | Repos as "owner/name" or full GitHub URLs (e.g. ["anthropics/claude-code"]) | |
| includeReadme | No | Include full README text (default true) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses an important behavioral trait: 'No GitHub token required,' indicating authentication is not needed. It also outlines the returned metadata. However, it does not mention potential limitations like rate limits or maximum number of repos per call, which would enhance transparency.
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 extremely concise: two sentences. The first enumerates the data points, the second gives a use case and auth info. Every word serves a purpose, with no redundancy or filler.
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 tool with two parameters and no output schema, the description provides a comprehensive list of returned fields and a usage context. It could mention the return format or processing behavior for multiple repos, but overall it is sufficiently complete for an AI agent to understand the tool's capabilities.
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 covers both parameters with descriptions (100% coverage), so the baseline is 3. The description adds no extra meaning beyond the schema; it merely restates that README content is part of the returned metadata. No additional parameter guidance is provided.
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 provides deep metadata for public GitHub repositories, listing specific data points like stars, forks, language, etc. It uniquely identifies the tool's purpose among siblings, as no other sibling tool focuses on GitHub repository 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 includes a use case ('Use to see what a company or project is actually building'), providing clear context for when to use. However, it does not explicitly mention when not to use or directly compare with alternatives, though the sibling list implies its distinct scope.
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.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
