[GitHub Intelligence] Get detailed statistics for a GitHub repository including stars, forks, issues, and activity.
Args: owner: Repository owner/organization (e.g. 'facebook') repo: Repository name (e.g. 'react')
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | ||
| owner | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe read operation. The description adds context by listing example statistics (stars, forks, issues, activity) but does not disclose additional behavioral traits like rate limits or authentication needs.
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 concise and front-loaded with the purpose. Every sentence adds value—the prefix identifies the category, the main sentence defines the action, and the parameter descriptions are succinct.
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 required parameters and no output schema, the description adequately covers purpose and parameter meanings. It lists included statistics (stars, forks, issues, activity), though more detail on output format could be beneficial.
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?
Despite 0% schema coverage, the description provides clear parameter descriptions with examples (e.g., 'facebook' for owner, 'react' for repo), adding meaningful guidance beyond the raw names in the input 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 it retrieves detailed statistics for a GitHub repository, listing specific metrics like stars, forks, issues, and activity. It distinguishes from sibling tools (e.g., get_repo_languages, compare_repos) by focusing on overall stats.
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 implies usage for obtaining repo stats but does not explicitly state when to use this tool versus alternatives like compare_repos or search_repos. No when-not guidance is provided.
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.
Many tools have overlapping functionality across categories (e.g., multiple search_arxiv, search_google_scholar, real estate tools, DNS/WHOIS checks). An agent would struggle to differentiate between similar tools from different categories, leading to ambiguity.
Tools follow a 'category__verb_noun' pattern mostly, but verbs vary (get, search, screen, check, etc.) and some categories use different orders (e.g., 'get_repo_stats' vs 'search_repos'). The consistency is acceptable but not uniform across the entire set.
With 152 tools, the server is excessively large for a single MCP server. While it aims to be an all-in-one gateway, the sheer number overwhelms the agent and likely exceeds practical limits for coherent selection.
The server covers a wide range of domains (finance, real estate, news, developer tools, etc.) but has notable gaps (e.g., social media APIs, CRM tools). Coverage is broad but not exhaustive, and some niche areas (e.g., global stock exchanges) are over-represented.