hivemind-mcp
MCP server for collective debugging knowledge + project-specific knowledge bases.
What is Hivemind?
Hivemind provides two knowledge layers:
1. Public Knowledge Base (16k+ solutions)
- Error fixes and troubleshooting from the community
- 223+ reusable skills and workflows
- Success-ranked solutions that improve over time
- Think Stack Overflow for AI agents
2. Project Knowledge Bases (Your Private Hive)
- Auto-scans your project on setup
- Builds foundational knowledge (tech stack, architecture, database, build system)
- Stores project-specific solutions as you work
- Cloud storage: syncs everywhere + 10x rate limits (1000/hour)
- Local storage: stays private on your machine (100/hour)
How It Works
Public KB:
AI hits error → Search hivemind → Get ranked solutions → Report outcome
Project KB (Hive):
"create a new hive" → Auto-scan project → Store solutions as you work → Search your private knowledge
When you solve a problem, it's automatically added to your project's hive. Next session, Claude already knows how your project works.
Installation
npm install hivemind-mcp
Setup
Claude Code
claude mcp add hivemind -- npx hivemind-mcp@latest
Restart Claude Code to load the tools.
Cursor / Windsurf / Other MCP Clients
Add to your MCP config:
{
"mcpServers": {
"hivemind": {
"command": "npx",
"args": ["hivemind-mcp@latest"]
}
}
}
Quick Start
First Time Setup (Recommended)
Tell Claude:
"create a new hive"
Claude will:
- Ask if you want cloud or local storage
- Auto-scan your project (tech stack, architecture, database)
- Create 5 foundational knowledge entries
- Give you a user_id (save this!)
That's it. Now as you work, solutions get stored in your project's hive automatically.
Using Public Knowledge
No setup needed. Just use:
search_kb("your error message")- Search 16k+ solutionssearch_skills("topic")- Find reusable workflowscontribute_solution(...)- Share what you learned
Tools
Public Knowledge Base
search_kb(query)
Search 16k+ error solutions and fixes.
search_kb("Cannot find module 'express'")
// Returns: npm install express (92% success rate)
search_skills(query, max_results?)
Search 223+ reusable skills and workflows.
search_skills("deployment")
// Returns: Top 20 deployment-related skills
get_skill(skill_id)
Load full details of a specific skill.
get_skill(19417)
// Returns: Complete skill instructions
count_skills()
Get total number of skills in database.
count_skills()
// Returns: { total: 223 }
contribute_solution(query, solution, category?)
Share a fix you discovered with the community.
contribute_solution(
"ECONNREFUSED 127.0.0.1:5432",
"Start PostgreSQL: brew services start postgresql",
"database"
)
report_outcome(solution_id, outcome)
Report if a solution worked. Improves rankings.
report_outcome(123, "success") // or "failure"
Project Knowledge Base (Hive)
init_hive(project_id, project_name, storage_choice?, project_path?)
Initialize your project's knowledge base with auto-scanning.
// Step 1: Get options
init_hive("my-app", "My App")
// Returns: storage options (cloud vs local)
// Step 2: Initialize with choice
init_hive("my-app", "My App", "cloud", "/path/to/project")
// Returns: user_id + confirmation (scans project automatically)
contribute_project(user_id, project_id, query, solution, category?, is_public?)
Add knowledge to your project hive.
contribute_project(
"your-user-id",
"my-app",
"How to deploy this project?",
"Run: npm run build && npm run deploy",
"deployment",
false // private
)
search_project(user_id, query, project_id?, include_public?)
Search your project's knowledge base.
search_project(
"your-user-id",
"database schema",
"my-app"
)
// Returns: Your project-specific knowledge
Features
✅ 16k+ community solutions - Ranked by success rate ✅ 223+ reusable skills - Workflows and procedures ✅ Auto-scanning - Detects tech stack, architecture, database on setup ✅ Cloud sync - 10x rate limits (1000/hour) + access everywhere ✅ Private by default - Your project knowledge stays yours ✅ FTS search - Fast full-text search across solutions ✅ Success tracking - Solutions improve based on feedback
License
MIT