README.md
August 18, 2026 · View on GitHub
English · 简体中文
Jacobian
Atomic mathematics for agents: discover one typed operation, run it, and compose its bounded result.
Jacobian is an MCP server that gives AI agents atomic, composable tools for
higher mathematics. It exposes two tools: math.find searches an immutable
library of typed mathematical operations, and math.run executes exactly one
of them and returns its concrete typed result. Each operation is one bounded,
exact computation: a typed request in and a typed mathematical value out. The
same mathematical library is also available through a CLI and native Python
API.
Jacobian's hypothesis is that mathematical reasoning benefits from an executable vocabulary of small, exact operations. Rather than exposing large domain solvers or precomposed workflows, Jacobian exposes mathematical primitives that agents can search for and compose into solutions beyond what any individual operation was designed to solve. The library supplies trustworthy mathematical moves; the reasoning model decides which moves to make, how to combine their results, and when to stop. Keeping the operations small and domain-owned preserves that search space instead of baking one proof strategy or workflow into the tools themselves.
Quickstart
Run the canonical Python MCP command without installing Jacobian globally:
uvx --from jacobian jacobian-mcp
Where an MCP host requires an npm command, the npm package is a deterministic carrier for that same command:
npx jacobian mcp
For a persistent installation:
python -m pip install jacobian
jacobian-mcp
That package includes Jacobian's exact maintained Python backend stack: SymPy, NetworkX, Z3, and Python-FLINT. A normal Python or npm installation therefore exposes the same built-in Python-backed operation portfolio. The tested binary-install contract is CPython 3.12 or 3.13 on glibc Linux x86-64; the release gate installs the built wheel and starts Jacobian on both Python versions. Other systems may have compatible upstream wheels, but are not part of the tested release contract yet. In particular, Alpine/musl cannot install the complete mandatory stack from PyPI.
The Python distribution contains the mathematical kernel, CLI, and MCP server.
The npm package deterministically maps its exact package version to the
corresponding uvx invocation.
Compute one bounded result
An ordinary operation returns mathematics first. For example,
matrix.determinant.compute accepts one exact rational matrix and returns its
determinant directly. Callers compose results by passing their typed values to a
subsequent operation.
Available mathematics
The built-in portfolio covers work in:
- polynomial maps and polynomial algebra;
- exact linear algebra;
- graphs, paths, colorings, and isomorphism;
- bounded SAT and SMT solving;
- finite algebra, probability, geometry, and topology; and
- Lean source elaboration.
SAT and SMT operations use the maintained Z3 Python binding directly. The
optional lean.check operation runs one bounded source snippet in the fixed
Lean service environment, using a request-scoped temporary directory and
returning typed diagnostics. Use math.find to search for an operation, browse
an unfamiliar domain, and inspect one operation before calling math.run once.
See the domain operation library for the maintained operation portfolio and backend requirements.
Status
Jacobian 0.12.0 is pre-stable. Its published package and operation contracts describe the supported surface; experimental operation contracts may change between releases.
Documentation
- Documentation home: tutorials, how-to guides, reference, and explanations
- Architecture: runtime structure and trust boundaries
- Product model: operation contracts, ownership, and project boundaries
- Tool reference: MCP resources and invocation contracts
- Backend requirements: maintained Python backends and optional Lean
- Remote deployment: HTTP deployment and authentication
Contributing
Jacobian uses Python 3.12, uv, and a small Makefile:
make setup
make test-math
make check
Read CONTRIBUTING.md before changing code. It documents focused test commands, verification rules, documentation placement, and pull-request expectations.