GitHub data. Repository stats, user profiles, and code-search results (stars, forks, language, topics, issues) via the GitHub REST API. [$0.02/call]. Params — mode: what to fetch (repo|search|user); repos: comma list of owner/name (mode=repo); q: search query (mode=search); usernames: comma list (mode=user); maxItems: integer Example params: {'mode': 'repo', 'repos': 'microsoft/vscode'}
| Name | Required | Description | Default |
|---|---|---|---|
| params | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description only says 'GitHub data' without mentioning rate limits, authentication, or side effects, leaving behavioral implications unstated.
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 packed with information but arranged in a single dense paragraph with capitals and punctuation, which could be more readable with clear separation of modes and parameters.
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?
It lists the types of data returned but does not describe the output structure. Given the absence of an output schema, this leaves some ambiguity for an agent expecting a detailed response format.
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?
Although the schema only defines a generic 'params' object, the description explains the sub-parameters (mode, repos, q, usernames, maxItems) and provides an example, adding significant meaning beyond the sparse 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 that the tool retrieves GitHub data including repository stats, user profiles, and code-search results, making the purpose explicit and distinct from sibling 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?
It provides examples of modes and parameters, but does not explicitly mention when to prefer this tool over alternatives, nor when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.