AutoDocs MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| AUTODOCS_CACHE_DIR | No | Cache directory location | ~/.autodocs/cache |
| AUTODOCS_LOG_LEVEL | No | Logging level | INFO |
| AUTODOCS_MAX_CONCURRENT | No | Maximum concurrent PyPI requests | 10 |
| AUTODOCS_REQUEST_TIMEOUT | No | Request timeout in seconds | 30 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| scan_dependenciesC | Scan project dependencies from pyproject.toml Args: project_path: Path to project directory (defaults to current directory) Returns: JSON with dependency specifications and project metadata |
| get_package_docsA | Retrieve formatted documentation for a package with version-based caching. Args: package_name: Name of the package to fetch documentation for version_constraint: Version constraint from dependency scanning query: Optional query to filter documentation sections Returns: Formatted documentation with package metadata |
| refresh_cacheB | Refresh the local documentation cache. Returns: Statistics about cache refresh operation |
| get_cache_statsB | Get statistics about the documentation cache. Returns: Cache statistics and information |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: get_cache_stats retrieves cache statistics, get_package_docs fetches documentation for a specific package, refresh_cache updates the cache, and scan_dependencies analyzes project dependencies. The descriptions clearly differentiate their functions, making tool selection unambiguous.
All tool names follow a consistent verb_noun pattern (get_cache_stats, get_package_docs, refresh_cache, scan_dependencies) using snake_case throughout. This predictability aids in understanding and usage without any deviations or mixed conventions.
With 4 tools, the count is reasonable for a documentation-focused server, covering key operations like fetching docs, scanning dependencies, and managing cache. It feels slightly thin but well-scoped, as each tool serves a distinct and necessary function without bloat.
The tool set covers core workflows for documentation retrieval and management: scanning dependencies, fetching package docs with caching, and cache operations. A minor gap exists in not having tools for updating or deleting cached docs, but agents can work around this with the provided refresh and get operations.