amdb

amdb

Turn your codebase into AI context — entirely on your machine. Single-binary MCP server with AST parsing, call graph, and local embeddings.

Category
Visit Server

README

amdb

<p align="left"> <img src="amdb.png" alt="amdb logo" width="60"> </p>

amdb demo

Rust Version

amdb turns your codebase into AI context — entirely on your machine.

amdb is a zero-runtime, single-binary code context MCP server with combined graph + vector retrieval. No code leaves the machine and no Node/Python runtime is required. Built for air-gapped environments, CI containers, and regulated industries where cloud-based codebase indexing is prohibited.

Install

cargo install amdb

Or download a static binary for Linux/macOS from the Releases page — no toolchain required.

Quickstart

amdb init .    # index the repo: AST parse + local embeddings, incremental
amdb serve     # expose the index as an MCP server over stdio

Done. Prefer a file instead of a server? amdb generate --focus "auth" writes a targeted context file to .amdb/.

Connect your editor

VSCode / Cursor — add .vscode/mcp.json to your project:

{
  "servers": {
    "amdb": {
      "command": "amdb",
      "args": ["serve"]
    }
  }
}

Claude Code:

claude mcp add amdb -- amdb serve

The server exposes three tools, all reading from the pre-built local index:

Tool What it returns
amdb_get_context Full project overview: files, symbols, and the mermaid dependency graph
amdb_focus Context narrowed to a query via name match + semantic vector search, expanded by depth dependency hops
amdb_get_symbol Every definition of a symbol name as JSON: file, kind, line, signature, callers, and callees — each callee carries its resolved file and a resolution value (same-file, global-unique, or unresolved)

If no index exists the tools respond with an error asking you to run amdb init — the server never indexes on its own.

Demo

Real session, 1.0 seconds end-to-end (scripts/demo.sh):

$ amdb init .
 INFO Initializing amdb in: .
 INFO Scanning files in ....
 INFO Files: 35 unchanged, 0 changed, 0 added, 0 removed
 INFO Indexing 0 files using 12 threads...
 INFO Embedding calls: 0
 INFO Project indexed successfully at .

$ amdb serve
  MCP client calls amdb_get_symbol with {"name": "cosine_similarity"}

cosine_similarity — src/core/vector_store.rs:196
  signature:  fn cosine_similarity(a: &[f32], b: &[f32]) -> f64
  visibility: private
  called by:  search (src/core/vector_store.rs)
  calls:      iter, map, sqrt, sum, zip

Answer came from the local index. No network. No code left the machine.

To record the cast on a host with asciinema: asciinema rec -c "AMDB_BIN=./target/release/amdb ./scripts/demo.sh" demo.cast, then agg demo.cast demo.gif.

Benchmarks

Measured by benchmark.py against amdb's own source tree (31 files, 21,887 raw tokens). Full methodology and caveats in benchmark.md.

Metric Score Meaning
Precision targeting 100% (28/28 indexed files) Query = exact file stem; the file's own section comes back. A retrieval-plumbing test, not a semantic-search-quality test
Global efficiency 91.5% reduction Focus output tokens vs. a full-repo dump
Noise reduction 81.7% compression Interface tokens vs. raw tokens, top-5 largest files
Graph presence 100% (28/28) Output contains real --> dependency edges

3 of 31 files are module-declaration files with no extractable symbols; they are not in the index and are excluded from the denominator, not silently counted.

Language support

Symbols and the call graph are extracted for all 16 grammars, but is_public and signature enrichment is AST-accurate for only three languages. The rest fall back to is_public = true and no signature — honest table below, so you know what you get:

Language Extensions Symbols + call graph is_public / signature
Rust .rs ✅ AST-accurate
Python .py ✅ AST-accurate
TypeScript .ts, .tsx ✅ AST-accurate
JavaScript .js, .jsx, .mjs fallback (true / none)
C .c, .h fallback (true / none)
C++ .cpp, .hpp, .cc, .cxx fallback (true / none)
C# .cs fallback (true / none)
Go .go fallback (true / none)
Java .java fallback (true / none)
Ruby .rb fallback (true / none)
PHP .php fallback (true / none)
HTML .html, .htm fallback (true / none)
CSS .css fallback (true / none)
JSON .json fallback (true / none)
Bash .sh, .bash fallback (true / none)

How it works

amdb init parses every source file with Tree-sitter, extracts symbols and call edges, and embeds each symbol with a local fastembed model — content-hashed, so unchanged files are skipped entirely on re-runs. Everything lands in two SQLite files: a symbol/relationship store and a vector store. Retrieval combines exact name matching, cosine similarity over the vectors, and call-graph expansion, served over MCP stdio or written to a Markdown context file.

Comparison

Same fixture repo (amdb's own source), same five questions ("where is symbol X defined, and who calls it?"), all numbers actually measured by benchmark.py. We did not run competitor indexing tools, so none appear here; the baselines are a raw full-repo dump and a scripted grep-then-read-matched-files agent protocol.

Strategy Avg tokens to model Avg tool calls
Raw full-repo dump 21,887 1
grep + read matched files 4,180 2.4
amdb (--focus, depth 1) 3,972 1

On a 31-file repo, grep is genuinely competitive on tokens — amdb's edge at this scale is one structured call instead of 2–4, with signatures, visibility, and resolver-accurate caller/callee attribution instead of raw text. The token gap widens with repo size: the dump grows linearly, grep grows with match noise, amdb's focus output grows with the size of the relevant interface.

More

Daemon modeamdb daemon watches the project and incrementally re-indexes on save, keeping the MCP answers fresh.

Focus depthamdb generate --focus <query> --depth N expands context N call-graph hops from the matched files (default 1).

Configuration — optional amdb.toml in the project root:

db_path = ".database"
ignore_patterns = ["target", ".git", "node_modules", ".amdb", ".fastembed_cache", "__pycache__", ".database"]

AMDB_DB_PATH overrides db_path. Add .database/ and .amdb/ to your .gitignore.

Verbose-v / --verbose on any command for debug logs.

Docker — the repo Dockerfile builds a slim image whose entrypoint is amdb serve, so the container speaks MCP over stdio immediately:

docker build -t amdb .
docker run --rm -v "$PWD:/workspace" --entrypoint amdb amdb init .
docker run -i --rm -v "$PWD:/workspace" amdb

The published ghcr.io/betaer-08/amdb:1.0.0 image predates the serve entrypoint — it runs bare amdb, so pass the subcommand explicitly: docker run -i --rm -v "$PWD:/workspace" -w /workspace ghcr.io/betaer-08/amdb:1.0.0 serve. Images published from the next tag serve by default.

Stability

amdb follows semantic versioning. 1.0.0 freezes the contract below; anything listed as covered changes only in a 2.0 release, and contract tests in tests/contract_test.rs fail loudly if it drifts.

Covered by the 1.0 promise:

  • CLI — subcommands init, daemon, generate, serve; flags --focus/-f, --depth/-d, --verbose/-v; the optional path argument to init and daemon. Exit codes: 0 on success, 1 on unrecoverable error.
  • MCP tools — exactly amdb_get_context, amdb_focus, amdb_get_symbol with their current input parameters. amdb_get_symbol responses keep every current field with its current type: file, name, kind, line, signature, is_public, callers[] (name, file), callees[] (name, file, resolutionsame-file | global-unique | unresolved). New fields and new resolution values may be added in minor releases; existing ones are never renamed, removed, or retyped.
  • Configamdb.toml keys db_path and ignore_patterns, and the AMDB_DB_PATH environment override. Unknown keys are ignored.
  • Database upgrades — the index schema is versioned via PRAGMA user_version. Any database written by amdb ≥ 0.6 opens without error and migrates automatically; the next amdb init rebuilds whatever the migration invalidated. Deleting .database/ is a last-resort fallback, never a required upgrade step.
  • Generated Markdown anchors — two things in generate output are stable for scripts: each indexed file gets a heading line of exactly ### <relative/path> (forward slashes, relative to the project root), and the dependency graph is a single fenced ```mermaid block containing graph TD; with --> edge lines.

Not covered (may change in any release):

  • Every other detail of the Markdown layout: bullet and signature formatting, section ordering, mermaid node-id sanitization, header text.
  • Log and progress text on stdout/stderr.
  • The SQLite table layout and the vector-store file format (only automatic migration is promised, not the bytes).
  • The benchmark harness (benchmark.py) and its output format.
  • Internal Rust APIs — amdb is a binary crate; depending on its modules as a library is unsupported.

License

MIT. Bug reports and inquiries: try.betaer@gmail.com

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured