Skip to main content
Glama

DeepWiki MCP Server

Give Claude superpowers to understand any codebase instantly.

This MCP (Model Context Protocol) server connects Claude to DeepWiki, an AI-powered platform by Cognition Labs (creators of Devin AI) that provides "Deep Research for GitHub" - interactive, up-to-date documentation for any public repository. With this integration, Claude can explore, analyze, and answer questions about any GitHub codebase - without you having to manually copy-paste code or documentation.

⚠️ Disclaimer

This is a learning/experimental project created through AI-assisted "vibe coding".

  • The author is not a professional developer

  • This entire MCP server was built using Claude Opus 4.0 (vibe coding)

  • Code quality improvements and auditing done with Claude Sonnet 4.5

  • Provided as-is for educational and experimental purposes only

  • Use at your own risk - see LICENSE for full disclaimer

Related MCP server: GitHub Context MCP Server

What is DeepWiki?

DeepWiki is an AI platform by Cognition Labs (the team behind Devin AI) that automatically transforms any public GitHub repository into interactive, conversational documentation.

How it works:

  • Visit any repo: github.com/facebook/react

  • Change URL to: deepwiki.org/facebook/react

  • Get instant access to AI-generated architecture diagrams, documentation, and an interactive chatbot

DeepWiki analyzes the code structure, relationships, and patterns, then creates up-to-date documentation you can have conversations with - like having an expert who has deeply studied the codebase and can answer questions about it.

What Does This MCP Do?

This MCP server acts as a command-line tool that Claude can use automatically when you ask questions about code. When you chat with Claude Desktop and mention a GitHub repository, Claude can:

  1. Read Documentation: Fetch and parse DeepWiki's generated documentation for any repo

  2. Ask Questions: Query DeepWiki's AI to understand how code works, find implementations, or explore architecture

  3. Get Code Snippets: Retrieve exact code references that answer your questions

  4. Deep Research: Trigger comprehensive analysis for complex questions (3-15 minutes)

  5. Cache Results: Store responses locally for instant retrieval in follow-up questions

You don't directly use this tool - instead, you talk to Claude naturally, and Claude decides when to use these tools to help answer your questions.

Key Benefits

Understand unfamiliar codebases instantly - No need to clone repos or wade through docs ✅ Ask natural questions - "How does authentication work?" instead of reading thousands of lines ✅ Get exact code references - Automatically retrieves relevant snippets with file paths and line numbers ✅ Follow-up conversations - Ask deeper questions based on previous answers ✅ Save research - Export findings to markdown files for documentation ✅ Works with any public GitHub repo - React, Next.js, your company's repos, etc.

Installation

Prerequisites

  • Node.js 18 or higher

  • Claude Desktop app

Setup Steps

  1. Clone this repository:

    git clone https://github.com/ai-vivid/deepwiki-mcp.git
    cd deepwiki-mcp
  2. Install dependencies:

    npm install
  3. Install Playwright browsers (required for web automation):

    npx playwright install chromium
  4. Build the project:

    npm run build
  5. Add to Claude Desktop configuration:

    On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%\Claude\claude_desktop_config.json

    {
      "mcpServers": {
        "deepwiki": {
          "command": "node",
          "args": ["/absolute/path/to/deepwiki-mcp/dist/index.js"]
        }
      }
    }

    Replace /absolute/path/to/deepwiki-mcp with your actual installation path.

  6. Restart Claude Desktop

How to Use

Just chat naturally with Claude! Here are example conversations:

Example 1: Understanding a new framework

You: "Can you explain how Next.js handles server-side rendering?"

Claude: *uses wiki_question tool to query DeepWiki about vercel/next.js*

Claude: "Next.js handles SSR through..."

Example 2: Finding implementations

You: "Show me how React implements hooks"

Claude: *retrieves code snippets from facebook/react*

Claude: "Here's the hooks implementation from ReactFiberHooks.js..."

Example 3: Deep research

You: "I need a comprehensive explanation of how Kubernetes manages container orchestration"

Claude: *triggers deep research mode for kubernetes/kubernetes*

Claude: "Starting deep research - this will take 5-10 minutes..."
*returns detailed architectural analysis*

Available Tools (For Claude's Use)

Claude automatically decides when to use these tools. This section explains what each tool does for your understanding, but you don't need to call them directly.

Tool 1: wiki_parser

Purpose: Reads DeepWiki's static documentation pages for a repository.

When Claude uses it:

  • You ask for an overview of a project's structure

  • You want to see the table of contents for documentation

  • You request specific chapters from the documentation

Parameters Claude can set:

Parameter

What it does

Example value

repo

GitHub repository to query

"facebook/react"

action

"structure" = show table of contents"extract" = get specific content

"extract"

chapters

Specific chapters to retrieve

["Getting Started", "API Reference"]

depth

How many heading levels to show (1-4)

2

saveToFile

"save-only" = just save to file"save-and-show" = save and display

"save-and-show"

Example - What Claude does:

// When you ask: "Show me the React documentation structure"
wiki_parser({
  repo: "facebook/react",
  action: "structure",
  depth: 2
})

// Returns: A table of contents with 2 levels of headers
// Output looks like:
// 1: Getting Started
//   ## Installation
//   ## Quick Start
// 2: Main Concepts
//   ## Components
//   ## Props & State

Example - Extracting specific content:

// When you ask: "Get me the Getting Started guide from React docs"
wiki_parser({
  repo: "facebook/react",
  action: "extract",
  chapters: ["Getting Started"]
})

// Returns: Full markdown content of the "Getting Started" chapter

Tool 2: wiki_question

Purpose: Asks DeepWiki's AI questions about a repository and gets intelligent answers with code references.

When Claude uses it:

  • You ask how something works in a codebase

  • You want to find specific implementations

  • You need architectural explanations

  • You ask follow-up questions about previous answers

Parameters Claude can set:

Parameter

What it does

Example value

repo

GitHub repository to query

"facebook/react"

question

Your question (for new queries)

"How does React implement hooks?"

queryId

ID from previous answer (for follow-ups)

"query-abc123"

useDeepResearch

Enable 3-15 minute deep analysis

true

goDeeper

Get more details on previous query

true

followUpQuestion

Ask follow-up on same topic

"What about error handling?"

includeAnswer

Show the text explanation

true (default)

includeReferencesList

Show numbered file references

true (default)

referencesNumbers

Get specific code snippets by number

[1, 2, 3]

contextFiles

Get full content of specific files

["src/hooks.js"]

contextRanges

Get specific line ranges

{"src/hooks.js": {"start": 1, "end": 50}}

saveToFile

Save response to file

"save-and-show"

Example 1 - Basic question:

// When you ask: "How does React handle component state?"
wiki_question({
  repo: "facebook/react",
  question: "How does React handle component state?"
})

// Returns:
// Query ID: query-abc123
//
// # Answer
// React handles component state through the useState hook...
// [1] [2]
//
// # References
// [1]: facebook/react: src/ReactHooks.js:45-67
// [2]: facebook/react: src/ReactFiberHooks.js:120-145

Example 2 - Getting specific code snippets:

// When you say: "Show me the code for reference 1 from that last answer"
wiki_question({
  repo: "facebook/react",
  queryId: "query-abc123",           // From previous response
  includeAnswer: false,              // Don't repeat the explanation
  includeReferencesList: false,      // Don't show the reference list again
  referencesNumbers: [1]             // Just show code for reference #1
})

// Returns:
// Query ID: query-abc123
//
// # Referenced Files
// ## facebook/react: src/ReactHooks.js
// **[45-67]:**
// ```
// function useState(initialState) {
//   const hook = mountState(initialState);
//   return [hook.state, hook.dispatch];
// }
// ...
// ```

Example 3 - Deep research mode:

// When you ask: "Give me a comprehensive analysis of Next.js routing architecture"
wiki_question({
  repo: "vercel/next.js",
  question: "Explain the complete routing architecture",
  useDeepResearch: true,
  saveToFile: "save-and-show"
})

// What happens:
// 1. DeepWiki spends 3-15 minutes doing deep analysis
// 2. Returns comprehensive architectural breakdown
// 3. Saves to: ~/.deepwiki-mcp/output/vercel-next.js/questions/2025-09-30_14-30-00_routing-architecture_query-xyz789.md
// 4. Shows you the full analysis

Example 4 - Follow-up questions:

// You: "What about error handling in that routing system?"
wiki_question({
  repo: "vercel/next.js",
  queryId: "query-xyz789",           // References previous deep research
  followUpQuestion: "How does error handling work in the routing system?",
  includeFullConversation: false     // Only show new answer, not previous one
})

// Returns: Answer about error handling, building on previous context

Example 5 - Getting full file contents:

// You: "Show me the full content of that routing file"
wiki_question({
  repo: "vercel/next.js",
  queryId: "query-xyz789",
  includeAnswer: false,
  contextFiles: ["packages/next/src/server/router.ts"]
})

// Returns: Complete contents of router.ts file

Understanding API Polling (Advanced)

When you ask DeepWiki a question, it doesn't respond instantly. Instead, your question is processed asynchronously by their AI, and this MCP periodically checks if the answer is ready.

How it works:

  1. Question submitted → DeepWiki starts processing

  2. MCP checks for answer → Polls every few seconds/minutes

  3. Answer ready → Returns results to Claude

Polling schedules:

  • Regular mode: Checks at 10s, 15s, 20s, 30s, 45s, 75s, 2m, 3m, 5m

    • Total: up to 5 minutes for an answer

  • Deep research mode: Checks at 3m, 4m, 5m, 7m, 9m, 12m, 15m

    • Total: up to 15 minutes for comprehensive analysis

Why this matters:

  • Regular questions: Usually answered in 30-60 seconds

  • Deep research: Takes 5-15 minutes but provides much more thorough analysis

  • You can customize these intervals (see Configuration below)

Visual example:

You ask question → DeepWiki AI thinks → MCP checks → Gets answer → Claude shows you

Regular: [0s] ----10s----15s----20s---[Answer!]
Deep:    [0s] ----------------3m--------------5m---------7m----[Answer!]

Configuration

You can customize the MCP's behavior through environment variables in your Claude Desktop config.

File Storage

default_directory - Where to save exported files

  • Default: ~/.deepwiki-mcp/output

  • Example: "/Users/me/Documents/deepwiki-research"

allowed_directories - Security: which directories can be written to

  • Default: ~/.deepwiki-mcp and default_directory

  • Example: "/Users/me/Documents/deepwiki-research,/Users/me/projects"

API Polling

DEEPWIKI_POLL_INTERVALS_REGULAR - When to check for regular answers (in seconds)

  • Default: "10,15,20,30,45,75,120,180,300" (checks at these intervals)

  • Faster polling: "5,10,15,20,30,60" (checks more frequently)

DEEPWIKI_POLL_INTERVALS_DEEP - When to check for deep research answers (in seconds)

  • Default: "180,240,300,420,540,720,900" (3min, 4min, 5min, 7min, 9min, 12min, 15min)

  • Faster polling: "60,120,180,300,600" (checks more frequently)

Example Configuration

{
  "mcpServers": {
    "deepwiki": {
      "command": "node",
      "args": ["/path/to/deepwiki-mcp/dist/index.js"],
      "env": {
        "default_directory": "/Users/me/Documents/deepwiki-research",
        "allowed_directories": "/Users/me/Documents/deepwiki-research,/Users/me/projects",
        "DEEPWIKI_POLL_INTERVALS_REGULAR": "5,10,15,30,60",
        "DEEPWIKI_POLL_INTERVALS_DEEP": "60,120,240,480"
      }
    }
  }
}

Caching & Storage

Cache location: ~/.deepwiki-mcp/cache/

  • Stores previous responses for instant retrieval

  • Organized by repository

  • Automatically used for follow-up questions

Output location: ~/.deepwiki-mcp/output/ (or your default_directory)

  • Saved files use this structure: {repo}/{type}/{date}_{time}_{description}_query-{id}.md

  • Example: facebook-react/questions/2025-09-30_14-30-00_hooks-implementation_query-abc123.md

Tips & Best Practices

💡 Start with regular questions - Use deep research only for complex architectural questions 💡 Use follow-ups - Build on previous answers instead of asking everything at once 💡 Save important findings - Ask Claude to save responses for future reference 💡 Be specific - "How does React implement the useState hook?" vs "How does React work?" 💡 Request code when needed - "Show me the code for reference 2" to get actual implementation

Troubleshooting

"No cached response found" → The queryId you provided doesn't exist. Ask a new question first.

"Tool timeout" → DeepWiki is taking longer than expected. Try again or increase polling intervals.

"Repository not found" → Check the repo format is owner/repo and that it's a public GitHub repository.

Playwright errors → Run npx playwright install chromium to install browser dependencies.

Development

Project Structure

deepwiki-mcp/
├── src/
│   ├── index.ts              # MCP server entry point
│   ├── tools/                # Tool implementations
│   │   ├── wiki-parser.ts    # Documentation parser tool
│   │   └── wiki-question.ts  # Question/answer tool
│   ├── automation/           # Playwright browser automation
│   ├── parsers/              # Response parsers & transformers
│   ├── cache/                # Cache management
│   └── utils/                # Helper utilities
├── dist/                     # Compiled JavaScript (generated)
├── package.json
└── tsconfig.json

Building

npm run build

Development Mode

npm run dev

License

MIT - See LICENSE file for full details including disclaimer.

Acknowledgments

Available Tools

2 tools
wiki_parserA

A comprehensive tool for parsing and extracting content from DeepWiki documentation pages. This tool helps you navigate and extract specific content from DeepWiki's AI-generated documentation for GitHub repositories. DeepWiki analyzes codebases and creates detailed, structured documentation making complex projects easier to understand.

When to use this tool:

  • Getting a table of contents or structure overview of a repository's documentation

  • Extracting specific chapters or sections from the documentation

  • Understanding the organization of a project's documentation

  • Pulling detailed explanations of specific components or features

  • Gathering comprehensive documentation for offline use or analysis

Key features:

  • View documentation structure with customizable depth levels

  • Extract single or multiple chapters/sections

  • Support for nested section extraction using "Chapter##Section" format

  • Flexible depth control per chapter

  • Optional file saving with customizable paths

  • Efficient caching for repeated requests

Usage examples:

  1. Get overview of documentation structure: action: "structure", depth: 2

  2. Extract a complete chapter: action: "extract", chapters: ["Introduction"]

  3. Extract specific sections from multiple chapters: action: "extract", chapters: ["Setup##Installation", "Configuration##Environment Variables", "API##Core Methods"]

  4. Extract with custom depth per chapter: action: "extract", chapters: ["API", "Examples"], chapterDepths: {"API": 4, "Examples": 2}

  5. Save documentation for offline use: action: "extract", chapters: ["Introduction", "Setup", "Configuration"], saveToFile: "save-and-show"

ParametersJSON Schema
NameRequiredDescriptionDefault
repoYesGitHub repository in 'owner/repo' format (e.g., 'facebook/react')
depthNoFor structure view, how many header levels deep to show (1-4)
actionYes'structure' to see table of contents, 'extract' to get chapter content
chaptersNoChapter names to extract, can include sections like 'Setup##Installation'
saveToFileNo'save-only' returns just file path, 'save-and-show' returns content + saves
saveLocationNoCustom file path for saving (defaults to ~/.deepwiki-mcp/output/)
chapterDepthsNoOverride depth for specific chapters (e.g., {'Introduction': 2, 'API': 3})

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full transparency burden and performs well: it discloses behaviors like optional file saving, caching for repeated requests, custom depth control, and nested section extraction. It does not cover error handling or authentication requirements, but the core behavioral traits are transparently described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear sections: overview, when-to-use, key features, and usage examples. It is front-loaded with the core purpose and every section adds value for a tool with 7 parameters, though some repetition exists across sections.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a relatively complex tool with no output schema and no annotations, the description covers actions, parameters, examples, and even file-saving behavior. It lacks explicit return-value descriptions, but given the detailed examples and 100% schema coverage, it is substantially complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds substantial semantics beyond the schema. Usage examples clarify the 'Chapter##Section' format, per-chapter depth overrides via chapterDepths, saveToFile options, and how action values map to behavior, making parameter usage significantly easier to understand.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it parses and extracts content from DeepWiki documentation pages, with specific verbs ('navigate', 'extract', 'structure') and a defined resource. It distinguishes itself from the sibling wiki_question tool by focusing on structured extraction rather than Q&A.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a clear 'When to use this tool' list with concrete scenarios like getting a table of contents, extracting chapters, and gathering offline documentation. It does not explicitly mention alternatives or when not to use it, but the usage contexts are specific and helpful.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

wiki_questionA

A powerful tool for asking questions about GitHub repositories using DeepWiki's AI analysis. This tool provides intelligent answers based on deep codebase analysis, with the ability to retrieve specific code snippets and file contents. DeepWiki's AI examines the entire repository structure, code patterns, and relationships to provide accurate, context-aware answers.

When to use this tool:

  • Understanding how a specific feature or system works in a repository

  • Getting explanations of complex code implementations

  • Finding where specific functionality is implemented

  • Understanding architectural decisions and patterns

  • Retrieving exact code snippets that answer your questions

  • Getting full file contents for detailed analysis

  • Following up on previous questions with cached results

  • Going deeper on existing queries for more detailed analysis

  • Sending follow-up questions to continue conversations

⚠️ Important: Do NOT ask for specific files in new questions (e.g., "show me auth.js"). Instead ask conceptual questions and use queryId to retrieve referenced files.

Key features:

  • Ask natural language questions about any GitHub repository

  • Retrieve exact code snippets that DeepWiki referenced in its answer

  • Get complete file contents or specific line ranges

  • Use cached results for efficient follow-up queries

  • Optional deep research mode for comprehensive analysis (3-15 minutes) - consider running in tmux for background processing

  • Go deeper functionality for existing queries to get more detailed analysis

  • Follow-up questions to continue conversations with existing queries (supports deep research)

  • Save responses to files for documentation or sharing

  • Flexible output control to show only what you need

CRITICAL: Understanding when to use queryId vs new question:

  • USE queryId when: Getting references/files from previous response, asking follow-ups about the same topic, going deeper, sending follow-up questions

  • USE new question when: Asking something completely different, no previous query exists

Usage examples:

  1. Ask a new question: question: "How does the authentication system work?", includeReferencesList: true

  2. Get specific references from previous query: queryId: "query-12345", referencesNumbers: [1, 2], includeAnswer: false

  3. Get full file content from previous query: queryId: "query-12345", contextFiles: ["src/auth/login.ts"], includeAnswer: false

  4. Get specific line ranges: queryId: "query-12345", contextFiles: ["src/index.ts"], contextRanges: {"src/index.ts": {"start": 100, "end": 150}}

  5. Deep research with file saving: question: "Explain the entire data flow architecture", useDeepResearch: true, saveToFile: "save-and-show"

  6. Go deeper on existing query: queryId: "query-12345", goDeeper: true

  7. Send follow-up question (shows only new response): queryId: "query-12345", followUpQuestion: "Can you explain how error handling works?", includeFullConversation: false

  8. Send follow-up with deep research (shows full conversation): queryId: "query-12345", followUpQuestion: "What are the security implications?", useDeepResearch: true, includeFullConversation: true

  9. Get all code snippets without the explanation: queryId: "query-12345", referencesAll: true, includeAnswer: false, includeReferencesList: false

Important notes:

  • referencesNumbers gets the EXACT snippets DeepWiki used (not full files)

  • contextFiles gets COMPLETE file contents (not just snippets)

  • Always check the queryId in responses for follow-up queries

  • Deep research mode provides more comprehensive analysis but takes significantly longer - consider using tmux for background execution

  • goDeeper creates a new query with deeper analysis on existing queries - returns a new queryId

  • followUpQuestion continues existing conversation with same queryId, supports deep research

  • includeFullConversation=false (default) shows only new response; true shows complete conversation

  • Use queryId instead of question when following up on previous responses

ParametersJSON Schema
NameRequiredDescriptionDefault
repoYesGitHub repository in 'owner/repo' format
queryIdNoID from previous response (required when using cached data)
goDeeperNoGo deeper on existing query for more detailed analysis (requires queryId)
questionNoYour question about the repository (required for NEW queries only)
contextAllNoGet complete contents of ALL available files
saveToFileNo'save-only' or 'save-and-show' to save output
contextFilesNoGet complete contents of specific files (e.g., ['src/index.ts', 'lib/utils.js'])
saveLocationNoCustom file path for saving
contextRangesNoGet specific line ranges from files (e.g., {'src/index.ts': {'start': 10, 'end': 50}})
includeAnswerNoShow the AI's text explanation (default: true)
referencesAllNoGet ALL exact code snippets DeepWiki referenced
useDeepResearchNoEnable deep analysis mode (takes 3-15 minutes, use sparingly)
followUpQuestionNoSend a follow-up question to existing query (requires queryId)
referencesNumbersNoGet specific reference snippets by number (e.g., [1, 2, 3])
includeReferencesListNoShow numbered list of referenced files (default: true)
includeFullConversationNoFor follow-ups: include full conversation history (default: false, shows only new response)

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully discloses behavioral traits. It explains that deep research takes 3-15 minutes, that referencesNumbers returns exact snippets while contextFiles returns full files, that goDeeper creates a new queryId, and that includeFullConversation controls response visibility. These details go far beyond basic operation and set clear expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear section headings, bullet points, and numbered examples. It is front-loaded with purpose and usage context. Some redundancy exists between 'Key features,' 'Usage examples,' and 'Important notes,' but each section adds value for a complex 16-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (16 parameters, nested objects, no output schema, no annotations), the description is remarkably complete. It covers all major workflows, warns about pitfalls, explains response-related parameters, and includes guidance on background execution for long operations. It leaves little ambiguity about how to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds substantial meaning beyond field descriptions. It clarifies the semantic difference between referencesNumbers and contextFiles, explains the behavior of includeFullConversation with defaults, and provides concrete examples for parameters like contextRanges, saveToFile, and followUpQuestion. This is far more than a restatement of the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'asking questions about GitHub repositories using DeepWiki's AI analysis.' It also explicitly lists capabilities like retrieving code snippets and file contents, and differentiates from the sibling tool by focusing on Q&A and deep analysis rather than parsing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'When to use this tool' scenarios, a critical distinction between using queryId vs new question, and multiple usage examples covering key parameter combinations. It also warns against asking for specific files in new questions, giving clear guidance on when not to use certain approaches.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedwiki_parser
    • First observedwiki_question

TDQS

A4.4/5.0
Disambiguation4/5

The two tools have distinct primary purposes: wiki_parser for navigation and extraction of documentation structure/content, and wiki_question for asking questions and retrieving code references. There is some overlap in that both can retrieve textual content, but the descriptions clearly differentiate the intended use cases, so an agent is unlikely to misselect.

Naming Consistency5/5

Both tools follow a consistent pattern of 'wiki_' followed by a noun (parser, question). This is a predictable and coherent naming convention that makes the tool roles clear and easy to remember.

Tool Count3/5

With only two tools, the server feels thin for the broad feature set described. Each tool is highly configurable and covers many sub-features, which partially compensates, but the count is below the typical 3-15 range and borders on insufficient for a comprehensive documentation access server.

Completeness4/5

The two tools cover the main operations for DeepWiki: extracting documentation structure and content, and asking questions with reference retrieval. Minor gaps exist, such as no explicit tool for listing available repositories or managing multiple documentation sessions, but these are likely handled at the server configuration level rather than being missing from the tool surface.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    Not graded
    maintenance
    Connects Claude Desktop to GitHub repositories, enabling users to perform git operations and GitHub API interactions through natural conversation.
    467
    -
  • F
    license
    A
    quality
    D
    maintenance
    Enables Claude to access and manage GitHub repositories dynamically at runtime, including private repos, with tools for browsing files, searching code, and viewing commits, pull requests, and issues.
    11
    1
    -
  • F
    license
    Not graded
    quality
    C
    maintenance
    Enables Claude to query GitHub repositories in plain English, fetching recent activity, release notes, issue triage, and health summaries via the GitHub API.
    -

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ai-vivid/deepwiki-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server