[GitHub Intelligence] Search GitHub repositories by query. Returns repo names, descriptions, stars, forks.
Args: query: Search query (e.g. 'machine learning python') sort: Sort by 'stars', 'forks', or 'updated' (default: stars) max_results: Maximum results to return (default 20)
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | stars | |
| query | Yes | ||
| max_results | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and openWorldHint, so the description adds value by detailing the return fields (name, description, stars, forks) and default behaviors (sort default 'stars', max_results default 20). This goes beyond the annotations without contradiction.
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: one line for purpose and return fields, then a structured Args section. Every sentence adds value; no redundant information.
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 search tool, the description covers query, sorting, and result limit. No output schema exists, but the return fields are listed. Could optionally mention pagination or result format, but overall sufficient for effective use.
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?
Schema coverage is 0%, so the description carries full burden. It explains each parameter: query with example, sort with valid values and default, max_results with default. This adds critical meaning beyond the bare 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 starts with '[GitHub Intelligence] Search GitHub repositories by query' which clearly states the verb 'Search' and the resource 'GitHub repositories'. It further specifies what is returned: 'repo names, descriptions, stars, forks'. This distinguishes it from siblings like 'developer-tools__search_github' by its GitHub-specific context and detailed output fields.
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 a clear query format example ('machine learning python') and explains sort and max_results options with defaults. It does not explicitly state when not to use, but the context is sufficient for typical usage. The sibling list includes related tools, but the description itself doesn't offer direct comparisons.
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.