Data Olympus

Data Olympus is an open-source knowledge base and Model Context Protocol (MCP) server harness created by knaisoma. Built in Python using FastMCP and SQLite, the tool is designed to manage context, documentation, and structured project knowledge for AI coding agents such as Claude Code, GitHub Copilot, and other MCP-compliant clients. In standard workflows, Data Olympus runs as a local MCP server that exposes tools and structured context to coding assistants. The underlying storage relies on SQLite with FTS5 full-text indexing for retrieval, with vector embeddings available as an optional opt-in feature. Agents query the harness to inspect health metrics (such as pending knowledge base items), fetch canonical project rules, and search project documentation directly from the terminal or IDE. What differentiates Data Olympus from generic agent harnesses is its adherence to standardized agent instruction structures (such as canonical AGENTS.md files with thin pointers for CLAUDE.md and GitHub Copilot). Instead of requiring complex external database infrastructure, it runs lightweight SQLite and FastMCP instances that integrate into Python development environments with minimal overhead. The project is available as open-source software under GitHub version control, actively maintained using modern Python tooling like uv.

Tags: ai agent, cli, developer tools, memory, OpenSource

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Key features

• FastMCP integration — Exposes knowledge base tools and context directly to agents via the Model Context Protocol. - SQLite FTS5 retrieval — Provides local full-text search indexing across repository documentation without requiring external database services. - Standardized instruction architecture — Supports canonical AGENTS.md root configurations with thin pointers for Claude and GitHub Copilot. - Optional embedding pipeline — Allows developers to toggle vector embeddings on or off according to project retrieval requirements. - CLI bundle linting — Includes CLI commands such as data-olympus lint to validate knowledge bundles and agent configuration integrity.

Use cases

Use Case: Context Provider for MCP-Enabled Coding Agents Developers run Data Olympus as a background FastMCP server so that terminal and IDE coding agents can query repository documentation, project rules, and internal standards dynamically during code generation.

Use Case: Knowledge Base Bundle Linting and Validation Teams use the data-olympus lint CLI command within local workflows or CI/CD pipelines to verify that documentation bundles and agent instruction files conform to expected schemas and reference pointers.

Use Case: Canonical Repository Agent Instructions Engineers set up Data Olympus instruction structures to maintain a single source of truth in AGENTS.md while providing compatibility pointers for Claude Code (CLAUDE.md) and GitHub Copilot (.github/copilot-instructions.md).

Who it is for

Data Olympus is designed for Python developers and engineering teams orchestrating MCP-compatible coding agents across repositories with structured documentation. It is less suitable for users looking for standalone GUI coding assistants or those seeking out-of-the-box SaaS agent platforms without MCP client setups.

Install guide

Installation Clone the repository and install it in a Python 3.13 virtual environment using uv: bash git clone https://github.com/knaisoma/data-olympus.git cd data-olympus uv venv --python 3.13 source .venv/bin/activate uv pip install -e '.[dev]'

First Run Verify that the CLI is installed and check bundle integrity with the built-in linter: bash data-olympus --help data-olympus lint example-bundle

Example Session Configure your MCP client (such as Claude Code or an MCP-aware host) to connect to the Data Olympus FastMCP server, or run the local test suite to ensure the environment is configured: bash # Run tests inside the virtual environment uv run pytest

Part of CLI Coding Agents

Categories: AI Coding Agents, Developer Tools, Memory

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