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Logichive MCP Server

@Ayato-AI-for-Auto

RepositoryHomepage

Basic information

Category

Other

License

NOASSERTION

Transports

stdio

Author

Ayato-AI-for-Auto

Submitted by

Ayato

About Logichive MCP Server

🛡️ Professional AI Logic Hub: Accumulate, verify, and reuse high-quality code assets via MCP. Built for kill liberation from mundane tasks likes code copy & remenber logic.

Logichive MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "LogicHive": {
      "command": "docker",
      "args": [
        "run",
        "-d",
        "\\"
      ]
    }
  }
}

Logichive MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Logichive?

LogicHive is a high-precision knowledge extraction and logic management system that enables AI agents to accumulate, verify, and reuse code assets via the Model Context Protocol (MCP). It uses a Hybrid Deterministic Gate (Fact, Static, AI, Execution) to veto non-factual AI opinions and enforce verifiability over correctness.

How to use Logichive?

Run the LogicHive-Hub.exe (Windows) or the Docker container, then configure your MCP client (Cursor, Claude Desktop) to connect to http://localhost:10880/sse. Set at least GEMINI_API_KEY in a .env file. Use the MCP tools to search, save, retrieve, and delete logic atoms.

Key features of Logichive

  • Hybrid Knowledge Search: semantic and exact-match search for code patterns
  • Verification Quality Gate: automated testing and linting before vaulting
  • MCP Streamable HTTP (SSE) Integration: centralized server for concurrent clients
  • Project Isolation: manage logic assets across multiple namespaces
  • Hybrid Deterministic Gate with four verification stages

Use cases of Logichive

  • Accumulating verified code snippets from AI interactions for reuse
  • Reducing LLM input token costs by injecting precise logic atoms
  • Preventing technical debt through automated assertion and redundancy checks
  • Securing AI governance by filtering security vulnerabilities and isolating runtime tests
  • Preserving critical domain logic across teams to eliminate silo effects

Frequently asked questions

1

What is the Hybrid Deterministic Gate?

It is a four‑stage verification pipeline (Fact 40%, Static 30%, AI 20%, Execution 10%) that rejects logic atoms failing AST analysis, code health metrics, forensic audit, or isolated runtime validation.

2

How does LogicHive handle heavy imports like torch or sklearn?

Use lazy imports (move heavy imports inside functions) or the `mock_imports` parameter in `save_function` to bypass the 20‑second Quality Gate timeout.

3

Where is my data stored?

User data (saved logic assets) is stored locally in SQLite and ChromaDB databases on your own machine; no assets are sent to external providers unless you explicitly request context.

4

What are the system requirements for Logichive?

You need a Windows system (native EXE) or any OS with Docker/Podman, and a Gemini API key. Optionally, you can run Ollama for local LLM inference.

5

Is Logichive available under a commercial license?

Yes, LogicHive is dual‑licensed under AGPL‑3.0 and a Commercial License for proprietary use, with tiers for Indie, Startup, and Enterprise.

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