pinion-mcp 0.3.1

AI-Powered Characterization Test Generator MCP — lock legacy Python behavior into pytest so you can refactor safely.

Pinion

Lock legacy code behavior into pytest — so you can finally refactor it.

Pinion is an AI-powered characterization-test generator that reads a Python function or class method, synthesizes representative inputs, captures the function's actual behavior in a sandbox, and emits a self-contained pytest file that locks that behavior in. It runs as a CLI and as a stdio Model Context Protocol (MCP) server, so it works inside Claude Code, Claude Desktop, Cursor, Cline, Codex CLI, Gemini CLI, Zed, revfactory/harness, and any other MCP-aware client.

🇰🇷 한국어 README는 여기로 → 🇰🇷 한국어 사용자 매뉴얼은 여기로 →

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Why Pinion

Legacy modernization has a chicken-and-egg problem. To refactor safely you need tests. To write tests you need to understand the code. To understand the code you need to refactor it. Most teams stall here for years.

Existing tools have not closed this gap:

• ApprovalTests / pinning-test libraries require a human to choose the inputs.
• Hypothesis / property-based testing requires a human to write strategies.
• EvoSuite is Java-only and search-based.
• Vendor AI assistants can suggest tests in chat, but they don't run, validate coverage, or capture real behavior.

Pinion treats input selection as a reasoning task and gives it to an LLM — then validates the result with deterministic tools (AST analysis, sandboxed execution, coverage.py) before emitting a regular pytest file you can read, edit, and commit.

The AI component is essential, not decorative: removing it leaves you with a sandbox that has nothing to run.

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Quickstart

Install

pip install pinion-mcp

Pick a provider

Pinion supports five LLM backends — Anthropic Claude, OpenAI ChatGPT, Google Gemini, local Ollama, or an internal enterprise gateway. Pick whichever you already have or grab the free Gemini tier:

# (a) Anthropic Claude — default
export ANTHROPIC_API_KEY="sk-ant-..."

# (b) OpenAI / ChatGPT
export PINION_LLM_PROVIDER=openai
export OPENAI_API_KEY="sk-..."

# (c) Google Gemini (free tier — https://aistudio.google.com/apikey)
export PINION_LLM_PROVIDER=gemini
export GEMINI_API_KEY="AIza..."

Generate tests for a function (v1)

pinion characterize ./legacy/order_service.py \
  --function calculate_total \
  --out tests/test_order_service_pinned.py

Drop --function to characterise every pure top-level function in the module.

Generate tests for a class method (v2.0)

pinion characterize ./legacy/cart.py \
  --class Cart --method total \
  --out tests/test_cart_total_pinned.py

Drop --method to characterise every public method on the class. Pinion automatically figures out how to construct the instance and which helper methods (add_item, apply_discount, …) to call first to put the instance into a meaningful state. Plain classes, @dataclass, and pydantic.BaseModel all work.

Use Pinion as an MCP server

claude mcp add pinion -- pinion-mcp serve

Then, in any MCP-aware client:

"Use pinion to characterise legacy/order_service.py::calculate_total and write the tests to tests/test_order_service_pinned.py."

Pinion exposes four MCP tools:

• characterize_function(file_path, function_name, …) — v1
• characterize_method(file_path, class_name, method_name, …) — v2.0
• characterize_module(file_path, …)
• health_check(probe=False)

The next section lists every MCP client we've registered Pinion with.

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MCP Clients

MCP is an open protocol. Pinion is not Claude-only — anything that speaks stdio MCP can mount it.

Client	How to register Pinion
Claude Code (CLI)	claude mcp add pinion -- pinion-mcp serve
Claude Desktop	~/Library/Application Support/Claude/claude_desktop_config.json → "mcpServers": {"pinion": {"command": "pinion-mcp", "args": ["serve"]}}
Cursor	.cursor/mcp.json (same mcpServers shape)
Cline (VS Code)	Extension settings → MCP Servers → pinion-mcp serve
Continue.dev (VS Code / JetBrains)	~/.continue/config.json → mcpServers
Codex CLI (OpenAI)	~/.codex/config.toml → [mcp_servers.pinion]
Gemini CLI (Google)	~/.gemini/settings.json → mcpServers
Zed Editor	settings.json → context_servers
revfactory/harness	harness.yaml → mcp_servers:
Custom client	Anthropic's mcp SDK (Python or TypeScript) — call pinion-mcp serve over stdio

Same payload shape, different config file locations.

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How it works

  +-----------+     +------------+     +-----------+     +------------+     +----------+
  | analyzer  | --> | synthesizer| --> |  sandbox  | --> | coverage   | --> | emitter  |
  | (AST)     |     | (LLM)      |     | (subproc  |     | (line+arc) |     | (pytest) |
  | profile   |     | inputs     |     |  + rlimit)|     | gate       |     | code     |
  +-----------+     +------------+     +-----------+     +------------+     +----------+
        deterministic         LLM               deterministic              deterministic

         If coverage < threshold, the synthesizer is invoked again with
         the missing branches as additional context. Up to 3 rounds.

1. Profile. Static AST analysis pulls the signature, type hints, docstring, branch structure, and external calls. For class methods (v2.0) it also produces a ClassProfile with the constructor signature and instance attributes.
2. Synthesize. The profile (not the source) goes to the LLM together with the missing-branch hints. The LLM returns a JSON list of input cases — for methods, each case includes a setup block describing how to construct the instance and which helper methods to invoke first. The output is validated against a Pydantic schema before it is trusted.
3. Capture. Each input is executed in a fresh subprocess with CPU, memory, file-descriptor, environment, and network limits in place. Return values, exceptions, and stdout/stderr tails are captured. v2.0.1 attributes exceptions to the right phase (construction / post-init / target-method).
4. Validate. coverage.py measures line and branch coverage. If we are below threshold (default 0.8), the synthesizer is asked for more cases targeting the missing branches.
5. Emit. A clean, reviewable pytest file is produced — for methods, with @pytest.fixture per unique setup hash so cases that share a setup also share a fixture.

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Capabilities and limitations

Pinion ships honest. It refuses, never silently degrades.

What works today (v1 + v2.0)

• ✅ Top-level pure functions
• ✅ Class methods on plain classes, @dataclass, and pydantic.BaseModel
• ✅ Five LLM providers via env-var-only switching
• ✅ Provider-and-model-aware retry on truncated JSON
• ✅ @pytest.fixture sharing for class methods
• ✅ macOS and Linux

What v1/v2.0 deliberately refuse

• Pure functions only by default. Functions touching the filesystem, network, databases, or subprocess are refused unless --allow-impure is set, in which case there is no correctness guarantee.
• No abstract base classes, metaclass-heavy classes, or __init_subclass__ users. v2.0 refuses these because the construction path is not safe to drive automatically.
• JSON-friendly arguments only. Constructors and method calls take JSON-serialisable values. User-defined-class arguments are properly supported once v2.2 (mock adapters) ships.
• Process-level sandbox, not a security boundary. Run Pinion only on code you have read, on disposable workstations or CI runners. The sandbox protects you from runaway loops and accidental I/O, not from a determined adversary.
• No async functions yet. v2.1 adds those.
• Windows is best-effort. No resource.setrlimit.

These boundaries are explicit in docs/SPEC.md §10 and in the code paths themselves.

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LLM Providers

Provider	PINION_LLM_PROVIDER	Default model	Notes
Anthropic Claude (default)	anthropic	claude-sonnet-4-5	ANTHROPIC_API_KEY required
OpenAI / ChatGPT	openai	gpt-4o-mini	OPENAI_API_KEY required
Google Gemini	gemini	gemini-2.5-flash	GEMINI_API_KEY (or GOOGLE_API_KEY). Free tier at aistudio.google.com/apikey
Local Ollama	ollama	qwen2.5-coder	PINION_OLLAMA_URL (default http://localhost:11434)
Internal Enterprise Gateway	enterprise-gateway	(set explicitly)	OpenAI-compatible endpoint, see below

Override the default model any time with PINION_LLM_MODEL=<model-name>.

Internal Enterprise Gateway

The enterprise-gateway slot is wired but inactive by default. To use a private internal LLM gateway (assuming OpenAI-compatible API), set:

export PINION_LLM_PROVIDER=enterprise-gateway
export PINION_LLM_MODEL=<gateway-model-name>
export PINION_GATEWAY_URL=https://internal-llm.example.com/v1
export PINION_GATEWAY_API_KEY=<token>

No code change required. pinion-mcp exposes a health_check(probe=true) tool to verify connectivity. If your internal gateway is not OpenAI-compatible, add a thin adapter — the abstraction lives in pinion/providers.py.

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Configuration

All configuration is via environment variables. See docs/SPEC.md §8 for the complete list. Key ones:

PINION_LLM_PROVIDER=anthropic            # anthropic | openai | gemini | ollama | enterprise-gateway
PINION_LLM_MODEL=claude-sonnet-4-5       # provider-specific
PINION_DEFAULT_THRESHOLD=0.8             # coverage gate
PINION_MAX_ROUNDS=3                      # max LLM re-synthesis rounds
PINION_SANDBOX_TIMEOUT=5.0               # seconds per case
PINION_SANDBOX_MEMORY_MB=256             # RLIMIT_AS per case

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Dogfooding

We point Pinion at Pinion. The full report — including two real limitations the run surfaced and the fix we shipped because of them — lives at examples/dogfooding/README.md.

Run	Mode	Target	Outcome
1	v1 (function)	pinion.providers.resolve_litellm_model	Tests passed, but exposed the JSON-only input contract limitation when the function takes a typed-class argument (motivates v2.2)
2	v2 (method)	examples.demo_legacy_class.Cart.total	100% coverage in 1 LLM round; initially 6/8 emitted tests passed — exposed a v2.0 setup-vs-method exception attribution bug we then fixed in v2.0.1 (now 8/8)

The dogfooding run also drove one user-visible default change: DEFAULT_MAX_TOKENS was raised from 4096 to 8192 after Gemini truncated long routing-function responses.

The point of dogfooding is not "the tool worked perfectly." It is "the tool worked, and here is exactly where it does not." Both runs reproduce on the Gemini free tier at $0 total.

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Roadmap

Shipped

• ✅ v1 — top-level pure functions, five LLM providers, MCP server, CLI, demo, full test suite (80 tests)
• ✅ v2.0 — class methods on plain classes / @dataclass / pydantic models; per-setup @pytest.fixture sharing; new characterize_method MCP tool (110 tests total)
• ✅ v2.0.1 — setup-phase vs method-phase exception attribution fix (112 tests total)

Next (designed in docs/V2_ROADMAP.md)

• v2.1 — async functions (async def) with isolated event loops
• v2.2 — user-supplied mock adapters (replay / stub / route) for I/O-heavy functions
• v2.3 — pinion diff orig.py --against new.py golden-master diff mode for refactor reviews
• v2.4 — source-hash cache so unchanged code skips LLM re-synthesis

Further out (v3)

• TypeScript via tree-sitter (vitest emitter)
• Java + JUnit emitter
• Property-based test synthesis (Hypothesis strategies)
• VS Code extension

The full roadmap, with design notes and DoDs, is in docs/V2_ROADMAP.md.

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Contributing

Pinion is Apache 2.0 licensed and welcomes contributions. The design contract is frozen in docs/SPEC.md; please read it before opening a PR that changes interfaces. For bug fixes and additional fixtures, just open an issue or PR.

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License

Apache License 2.0. See LICENSE.