Production-grade, token-optimized FastMCP SQLite Server with sub-millisecond O(1) schema discovery, opcode execution watchdog, and fuzzy self-healing.
View on GitHub ↗Homepage ↗A High-Performance, Token-Optimized SQLite Model Context Protocol (MCP) Server Built for AI Coding Agents. Zero Native Build Toolchain · Sub-15ms Cold Start · Prompt Cache Prefix Stability (97%) · VDBE Opcode Watchdog · Fast Non-Blocking Schema Discovery
Design Principles • Quickstart • 🤖 Agent Prompt • Client Matrix • Tools Reference • Benchmarks & Tokenomics • Architecture • When NOT to Use
Many SQLite Model Context Protocol (MCP) servers in the ecosystem rely on Node.js native addons (better-sqlite3) or unbounded serialization formats, introducing distinct operational challenges in autonomous AI agent environments:
1. Native Build Toolchain Overhead: Relying on node-gyp or platform-specific C++ build toolchains (such as MSVC on Windows) introduces installation friction in minimal container environments, restricted CI/CD runners, and locked-down developer workstations. 2. Context Token Inefficiency: Formatting query results as verbose JSON object arrays repeats schema keys for every record, consuming 2.2x to 3.7x more context tokens than compact tabular representations. 3. Prompt Cache Invalidation: Injecting dynamic execution timestamps or metrics into response headers alters the message prefix, preventing KV-cache reuse on Claude, GPT-4o, and Gemini architectures. 4. Unbounded Query Execution: Executing full SELECT COUNT() table scans on multi-gigabyte databases creates prolonged disk read locks, while unconstrained recursive Common Table Expressions (WITH RECURSIVE) or Cartesian joins can stall agent stdio subprocesses.
From the project README.
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