Doclea MCP▌
by docleaai
Doclea MCP: persistent memory for AI assistants—store and retrieve architectural decisions, patterns and code insights u
Provides persistent memory for AI coding assistants, storing and retrieving architectural decisions, patterns, and solutions across sessions using semantic search, while also offering git integration for commit messages and code expertise mapping.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers maintaining consistency across long projects
- / Teams preserving architectural knowledge
- / AI assistants that need project context memory
capabilities
- / Store architectural decisions and patterns with semantic search
- / Retrieve relevant code solutions across sessions
- / Generate commit messages from code changes
- / Map code expertise and knowledge domains
- / Integrate with git repositories
- / Search stored knowledge semantically
what it does
Gives AI coding assistants persistent memory across chat sessions, storing architectural decisions, code patterns, and solutions with semantic search.
about
Doclea MCP is an official MCP server published by docleaai that provides AI assistants with tools and capabilities via the Model Context Protocol. Doclea MCP: persistent memory for AI assistants—store and retrieve architectural decisions, patterns and code insights u It is categorized under ai ml, developer tools.
how to install
You can install Doclea MCP in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Doclea MCP is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
@doclea/mcp
Local MCP server for Doclea — persistent memory for AI coding assistants.
Doclea gives your AI coding assistant (Claude Code, etc.) persistent memory across sessions. It remembers architectural decisions, patterns, solutions, and codebase context so you don't have to repeat yourself.
Features
- Persistent Memory — Store decisions, patterns, solutions, and notes that persist across sessions
- Semantic Search — Find relevant context using vector similarity search
- Git Integration — Generate commit messages, PR descriptions, and changelogs from your history
- Code Expertise Mapping — Identify code owners and suggest reviewers based on git blame analysis
- Zero-Config Mode — Works immediately with no Docker or external services required
- Auto-Detection — Automatically uses optimized Docker backends when available
Quick Start
Add to your Claude Code config (~/.claude.json or project .claude.json):
{
"mcpServers": {
"doclea": {
"command": "npx",
"args": ["@doclea/mcp"]
}
}
}
Restart Claude Code, navigate to your project, and ask:
Initialize doclea for this project
That's it! Doclea scans your codebase, git history, and documentation to bootstrap memories.
Installation Options
| Method | Command | Setup Time | Best For |
|---|---|---|---|
| Zero-Config | npx @doclea/mcp | <30 seconds | Quick start, small projects |
| Optimized | curl install.sh | 3-5 minutes | Production, large codebases |
| Manual | Clone & build | 5-10 minutes | Development, customization |
Zero-Config (Recommended)
Works immediately with no Docker required. Uses embedded sqlite-vec for vectors and Transformers.js for embeddings.
First run downloads the embedding model (~90MB) which is cached for future use.
Optimized Installation (Docker)
For larger codebases with better performance:
curl -fsSL https://raw.githubusercontent.com/docleaai/doclea-mcp/main/scripts/install.sh | bash
This script:
- Detects your OS and architecture
- Installs prerequisites (Bun, Docker if needed)
- Sets up Qdrant vector database and TEI embeddings service
- Configures Claude Code automatically
Manual Installation
git clone https://github.com/docleaai/doclea-mcp.git
cd doclea-mcp
bun install
bun run build
Add to Claude Code (~/.claude.json):
{
"mcpServers": {
"doclea": {
"command": "node",
"args": ["/absolute/path/to/doclea-mcp/dist/index.js"]
}
}
}
For detailed setup instructions, see docs/INSTALLATION.md.
Usage Examples
Store Memories
Store this as a decision: We're using PostgreSQL for ACID compliance
in financial transactions. Tag it with "database" and "infrastructure".
Search Context
Search memories for authentication patterns
Git Operations
Generate a commit message for my staged changes
Create a PR description for this branch
Generate a changelog from v1.0.0 to HEAD
Code Expertise
Who should review changes to src/auth/?
MCP Tools
Memory Tools
| Tool | Description |
|---|---|
doclea_store | Store a memory (decision, solution, pattern, architecture, note) |
doclea_search | Semantic search across memories |
doclea_get | Get memory by ID |
doclea_update | Update existing memory |
doclea_delete | Delete memory |
Git Tools
| Tool | Description |
|---|---|
doclea_commit_message | Generate conventional commit from staged changes |
doclea_pr_description | Generate PR description with context |
doclea_changelog | Generate changelog between refs |
Expertise Tools
| Tool | Description |
|---|---|
doclea_expertise | Map codebase expertise and bus factor risks |
doclea_suggest_reviewers | Suggest PR reviewers based on file ownership |
Bootstrap Tools
| Tool | Description |
|---|---|
doclea_init | Initialize project, scan git history, docs, and code |
doclea_import | Import from markdown files or ADRs |
Memory Types
| Type | Use Case |
|---|---|
decision | Architectural decisions, technology choices |
solution | Bug fixes, problem resolutions |
pattern | Code patterns, conventions |
architecture | System design notes |
note | General documentation |
Configuration
Doclea works out of the box with zero configuration. It auto-detects available backends:
- If Docker services (Qdrant/TEI) are running → uses them for better performance
- Otherwise → uses embedded sqlite-vec + Transformers.js
Custom Configuration
Create .doclea/config.json in your project root:
{
"embedding": {
"provider": "transformers",
"model": "Xenova/all-MiniLM-L6-v2"
},
"vector": {
"provider": "sqlite-vec",
"dbPath": ".doclea/vectors.db"
},
"storage": {
"dbPath": ".doclea/local.db"
}
}
Embedding Providers
| Provider | Config | Notes |
|---|---|---|
transformers | { "provider": "transformers" } | Default, no Docker |
local | { "provider": "local", "endpoint": "http://localhost:8080" } | TEI Docker |
openai | { "provider": "openai", "apiKey": "..." } | API key required |
ollama | { "provider": "ollama", "model": "nomic-embed-text" } | Local Ollama |
Vector Store Providers
| Provider | Config | Notes |
|---|---|---|
sqlite-vec | { "provider": "sqlite-vec" } | Default, no Docker |
qdrant | { "provider": "qdrant", "url": "http://localhost:6333" } | Docker service |
Architecture
┌─────────────────────────────────────────────────────────┐
│ Claude Code │
│ ↓ MCP │
├─────────────────────────────────────────────────────────┤
│ Doclea MCP Server │
│ ┌─────────┐ ┌─────────┐ ┌──────────┐ ┌───────────┐ │
│ │ Memory │ │ Git │ │Expertise │ │ Bootstrap │ │
│ │ Tools │ │ Tools │ │ Tools │ │ Tools │ │
│ └────┬────┘ └────┬────┘ └────┬─────┘ └─────┬─────┘ │
│ └───────────┴───────────┴─────────────┘ │
│ ↓ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ SQLite │ │ Vector DB │ │ Embeddings │ │
│ │ (metadata) │ │(sqlite-vec/ │ │(transformers/│ │
│ │ │ │ qdrant) │ │ TEI) │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────┘
Development
# Install dependencies
bun install
# Run in development mode (hot reload)
bun run dev
# Run tests
bun test # All tests
bun run test:unit # Unit tests only
bun run test:integration # Integration tests (requires Docker)
# Type check
bun run typecheck
# Lint
bun run lint # Check
bun run lint:fix # Auto-fix
# Build
bun run build
Troubleshooting
First startup is slow
The embedding model (~90MB) downloads on first run. Cached at:
- Linux/macOS:
~/.cache/doclea/transformers - Windows:
%LOCALAPPDATA%\doclea ransformers
macOS SQLite extension error
macOS ships with Apple's SQLite which doesn't support extensions:
brew install sqlite
The server auto-detects Homebrew SQLite.
MCP server not appearing in Claude
- Verify the path in config is absolute (manual installs)
- Check that
bun run buildcompleted successfully - Restart Claude Code completely
See docs/INSTALLATION.md for more troubleshooting.
Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
# Fork and clone
git clone https://github.com/YOUR_USERNAME/doclea-mcp.git
# Create feature branch
git checkout -b feature/amazing-feature
# Make changes, test, and lint
bun test && bun run lint
# Commit and push
git commit -m 'feat: add amazing feature'
git push origin feature/amazing-feature
Roadmap
- Cloud sync for team collaboration
- VS Code extension
- Additional embedding providers
- Memory analytics dashboard
License
<p align="center"> <a href="https://doclea.ai">Website</a> • <a href="https://github.com/docleaai/doclea-mcp/issues">Issues</a> • <a href="https://github.com/docleaai/doclea-mcp/discussions">Discussions</a> </p>
FAQ
- What is the Doclea MCP MCP server?
- Doclea MCP is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Doclea MCP?
- This profile displays 38 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.5★★★★★38 reviews- ★★★★★Pratham Ware· Dec 28, 2024
I recommend Doclea MCP for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Noah Liu· Dec 8, 2024
Strong directory entry: Doclea MCP surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Noah Chawla· Nov 27, 2024
I recommend Doclea MCP for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Anaya White· Nov 23, 2024
Doclea MCP is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Yash Thakker· Nov 19, 2024
Strong directory entry: Doclea MCP surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Henry Mensah· Oct 18, 2024
Doclea MCP reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Henry Park· Oct 14, 2024
Useful MCP listing: Doclea MCP is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Dhruvi Jain· Oct 10, 2024
Doclea MCP is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★William Bhatia· Sep 25, 2024
We wired Doclea MCP into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Piyush G· Sep 21, 2024
Doclea MCP is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
showing 1-10 of 38