MCP DeepWiki Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| deepwiki_fetchC | Retrieves GitHub repository documentation from DeepWiki with enhanced content organization and filtering capabilities. |
| deepwiki_summarizeB | Generates AI-powered summaries of GitHub repository documentation with different focus types (overview, technical, quickstart, api). |
| deepwiki_searchC | Search for GitHub repositories with documentation available on DeepWiki. Supports filtering by programming language and topics. |
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 3 tools
Each tool has a clearly distinct purpose: fetch retrieves documentation, search finds repositories, and summarize generates AI summaries. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent 'deepwiki_' prefix with descriptive action suffixes (fetch, search, summarize). This uniform naming pattern enhances predictability and readability.
Three tools is appropriate for a focused documentation server, covering core operations. It might be slightly lean, but each tool serves a distinct, valuable purpose without redundancy.
The tools cover key workflows: searching for repositories, fetching documentation, and summarizing content. Minor gaps may exist, such as advanced filtering or update operations, but the core domain is well-covered for typical agent tasks.