Ariadne

io.github.whyy9527Published by GitHub account whyy9527v0.2.2Updated Oct 29, 2026

Deterministic local cross-service API-chain discovery for AI coding agents.

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

AI-generated overview

Local MCP server that indexes the API contract layer of microservice repos so an assistant can trace where an operation lives across services.

What you can do
  • Index the contract layer of configured repos: GraphQL mutations, REST endpoints, Kafka topics and frontend queries
  • Query cross-service call chains for an operation name, returning ranked clusters with confidence scores
  • Expand a chain and rate results to feed local feedback reranking
  • Rescan an indexed workspace when results are flagged as stale
  • Run a zero-config demo that clones a public sample stack and prints a real call chain
When to use it
Useful when an assistant works in a Spring Boot, TypeScript, FastAPI or Kafka microservice workspace and needs to find which services and contracts a given operation touches, instead of grepping across repos. Best for contract-level lookup rather than general code search.
Requirements
Runs as a local stdio process installed from PyPI (ariadne-mcp) with Python 3.10-3.12; needs MCP Python SDK v1 and PyYAML. A config file listing the repo paths to index, and a workspace where the index database is written. The demo clones a public repository into the user's cache directory.
Before you install
The index is built from your source repositories, so the database and any local feedback stay on disk and should be treated as workspace data. The demo clones a third-party repository and needs network access. Optional feedback is only sent if you choose to submit it. Re-run install or rescan after pulling new code, since results can go stale.

Installation

In SourceWeft

  1. Open Ariadne in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Ariadne

[License: MIT] [MCP] [ariadne MCP server] [Awesome MCP Servers]

Ariadne's thread — a way out of the microservice maze.

Cross-service API dependency graph for Spring Boot + TypeScript microservice stacks. MCP stdio server for AI coding assistants (Claude Code, Cursor, Windsurf), with a CLI twin. Local SQLite + TF-IDF. Zero ML dependencies.

[Ariadne demo — scan Spring PetClinic microservices and ask "owner"]

70-second deterministic terminal walkthrough. Reproduce it from docs/demo.tape.


What it does

Indexes the contract layer — GraphQL mutations, REST endpoints, Kafka topics, frontend queries. Nothing else. That's why results fit an AI context window.

Ask Claude "where does createOrder live across the stack?" and query_chains returns:

Top Cluster #1  [confidence: 0.91]  Services: gateway, orders-svc, billing-svc, web  - [web]          Frontend Mutation: createOrder  - [gateway]      GraphQL Mutation:  createOrder  - [orders-svc]   HTTP POST /orders: createOrder  - [orders-svc]   Kafka Topic:       order-created  - [billing-svc]  Kafka Listener:    order-created → chargeCustomer

The response is intentionally bounded for an AI context window. See the reproducible public-stack benchmark for measured retrieval, serialized token, and timing results against rg and grep.

Current public-stack benchmark (48 reviewed queries across Spring REST, GraphQL/TypeScript, Kafka, and FastAPI):

BackendTop-1Top-3MRRWarm queryMean output
Ariadne89.6%95.8%0.927<0.4 ms375 tokens
rg37.5%56.2%0.510~9 ms591 tokens
grep37.5%56.2%0.510~9 ms591 tokens

Full methodology and per-stack results · raw JSON evidence

This corpus is operation-name-heavy and measures deterministic contract lookup compatibility. It is not yet a natural-language relevance benchmark.

Reproduce the measurements with uv:

bash
uv run --locked --extra benchmark python benchmarks/run.py

The first run that uses tiktoken downloads its encoding data and needs network access. The benchmark also calls the system git, rg, and grep commands.

Supports: GraphQL · Spring HTTP/Kafka/RestClient · Python FastAPI · TypeScript Apollo/fetch/axios · Cube.js.


Try it in 30 seconds (zero config)

bash
pip install ariadne-mcpariadne-mcp demo

Clones spring-petclinic-microservices into ~/.cache/ariadne-mcp/demo, scans it, and prints the top cluster for owner — a real cross-service call chain. No config file, no workspace setup.

Did Ariadne find the chain you expected? Share one minute of structured feedback. Ariadne sends no usage data automatically; the form opens only when you choose to submit it.


Install on your own workspace

bash
pip install ariadne-mcpcp "$(python -c 'import ariadne_mcp, os; print(os.path.join(os.path.dirname(ariadne_mcp.__file__), "ariadne.config.example.json"))')" ariadne.config.json# edit ariadne.config.json (list the repos you want indexed)ariadne-mcp install ariadne.config.json ~/your-workspace

Restart Claude Code. install is idempotent — re-run after pulling new code, or let the assistant call rescan on a stale_warning.

Run an MCP server from a source checkout

Prepare the environment with the project's pinned uv version, then configure the MCP client to launch that environment's Python directly:

bash
cd /path/to/ariadneuv sync --locked --no-dev --inexact.venv/bin/python -m ariadne_mcp.server \  --db /path/to/workspace/.ariadne/ariadne.db \  --fb /path/to/workspace/.ariadne/feedback.db

Use an absolute interpreter path in the MCP client configuration. Run the sync step after dependency changes; it is separate from the long-running server. --inexact preserves other installed dependency groups in a development checkout. If UV_PROJECT_ENVIRONMENT selects another environment, configure that environment's Python instead of .venv/bin/python.

With the project's pinned uv version, wrapping a long-running server in uv run keeps uv's shared package-cache lock held for the server's lifetime, which prevents normal uv cache prune. A direct Python launch avoids that lock. Separate stdio client connections can run separate server processes.

After your first real query, you can optionally send closed-ended usage feedback. No source, query, or usage data is transmitted by Ariadne itself.


Config

json
{ "repos": [    { "path": "../gateway" },    { "path": "../orders-svc" },    { "path": "../web" }]}

Scanners are inferred from each repo's top-level files (pom.xml / build.gradle / package.json / SDL). See docs/CONFIG.md for the detection table and override syntax.


Reproducible public samples

Each sample pins an upstream commit, scans real service source, runs one query, and verifies manually reviewed node IDs:

ExampleContract path
spring-petclinicSpring REST gateway → service
one-platformGraphQL/TypeScript services
kafka-microservicesKafka producer → consumer
fastapi-microservicesPython FastAPI routes

Run one from a source checkout:

bash
python examples/run.py kafka-microservices

Evaluate ranking

Keep a JSONL judgment list for queries that matter to your workspace:

jsonl
{"hint":"createOrder","expected_node_ids":["gateway::gql::m::createOrder"],"k":3}{"hint":"owner","expected_node_ids":["customers::http::GET /owners/{ownerId}"],"match":"any","k":5}

Run it against a built DB:

bash
ariadne-mcp --db .ariadne/ariadne.db eval eval/queries.jsonl --top 3 --min-hit-rate 0.8

The command evaluates top-k hit rate and MRR using a stable internal candidate depth, and exits non-zero when a configured threshold fails. Add --feedback-db .ariadne/feedback.db to include local feedback reranking in the eval.


Architecture, MCP tools, scoring math, feedback boost → docs/ARCHITECTURE.md. Custom scanners (Go, Rust, anything) → docs/CUSTOM_SCANNERS.md. Maintainer adoption snapshots → docs/ADOPTION_METRICS.md.

Business acceptance and ablation

The source-backed business suite covers all nine built-in scanners and the complete MCP query/expand/rate/rescan lifecycle, including a real stdio client. It uses synthetic contracts; private workspace sources and feedback stay local.

bash
uv sync --locked --extra benchmark --group devuv run --locked --extra benchmark --group dev python -m pytest tests/ -quv run --locked --extra benchmark python benchmarks/ablation.py \  --output benchmarks/.work/ablation.json

See quality experiment results for the fixed-input baseline, component ablations, output costs and remaining limitations. The server supports Python 3.10, 3.11, and 3.12. Runtime requirements are MCP Python SDK v1 (mcp>=1,<2) and PyYAML (PyYAML>=6,<7); tiktoken is an optional benchmark dependency. uv.lock pins the maintainer and CI environment and does not constrain dependencies resolved by package users.

Source: README.md at commit 26e4599

Tools

0
SourceWeft started this package once in an isolated sandbox, without credentials, to list its tools. That failed: the server did not start.

Version history

4
  1. v0.2.2LatestOct 29, 2026
  2. v0.2.1Oct 22, 2026
  3. v0.2.0Oct 12, 2026
  4. v0.1.3Sep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts