lean-lsp-mcp

lean-lsp-mcp

Lean Theorem Prover MCP

MCP server that allows agentic interaction with the Lean theorem prover via the Language Server Protocol using leanclient. This server provides a range of tools for LLM agents to understand, analyze and interact with Lean projects.

Currently beta testing: Please help us by submitting bug reports and feature requests!

Key Features

• Rich Lean Interaction: Access diagnostics, goal states, term information, hover documentation and more.
• External Search Tools: Use leansearch, loogle, and lean_state_search to find relevant theorems and definitions.
• Easy Setup: Simple configuration for various clients, including VSCode, Cursor and Claude Code.

Setup

Overview

1. Install uv, a Python package manager.
2. Make sure your Lean project builds quickly by running lake build manually.
3. Configure your IDE/Setup

1. Install uv

Install uv for your system.

E.g. on Linux/MacOS:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Run lake build

lean-lsp-mcp will run lake build in the project root to use the language server (for most tools). Some clients (e.g. Cursor) might timeout during this process. Therefore, it is recommended to run lake build manually before starting the MCP. This ensures a faster build time and avoids timeouts.

E.g. on Linux/MacOS:

cd /path/to/lean/project
lake build

Note: Your build does not necessarily need to be successful, some errors or warnings (e.g. declaration uses 'sorry') are OK.

3. a) VSCode Setup

VSCode and VSCode Insiders are supporting MCPs in agent mode. For VSCode you might have to enable Chat > Agent: Enable in the settings.

1. One-click config setup:

OR manually add config to settings.json (global):

{
    "mcp": {
        "servers": {
            "lean-lsp": {
                "command": "uvx",
                "args": ["lean-lsp-mcp"],
                // OPTIONAL: Setting this env variable is not required.
                // It is recommended to try without it first and only set it if you run into issues.
                "env": {
                    "LEAN_PROJECT_PATH": "/path/to/lean/project"
                }
            }
        }
    }
}

2. Click "Start" above server config, open a Lean file, change to agent mode in the chat and run e.g. "auto proof" to get started:

3. b) Cursor Setup

1. Open MCP Settings (File > Preferences > Cursor Settings > MCP)

2. "+ Add a new global MCP Server" > ("Create File")

3. Paste the server config into mcp.json file:

{
    "mcpServers": {
        "lean-lsp": {
            "command": "uvx",
            "args": ["lean-lsp-mcp"],
            // OPTIONAL: Setting this env variable is not required.
            // It is recommended to try without it first and only set it if you run into issues.
            "env": {
                "LEAN_PROJECT_PATH": "/path/to/lean/project"
            }
        }
    }
}

3. c) Claude Code

Run one of these commands in the root directory of your Lean project (where lakefile.toml is located):

# Local-scoped MCP server
claude mcp add lean-lsp uvx lean-lsp-mcp

# OR project-scoped MCP server (creates or updates a .mcp.json file in the current directory)
claude mcp add lean-lsp -s project uvx lean-lsp-mcp

# OR If you run into issues with the project path (e.g. the language server directory cannot be found), you can also set it manually e.g.
claude mcp add lean-lsp uvx lean-lsp-mcp -e LEAN_PROJECT_PATH=$PWD

You can find more details about MCP server configuration for Claude Code here.

Other Setups

Other setups, such as Claude Desktop, OpenAI Agent SDK, Windsurf or Goose should work with similar configs.

Tools

Tools are currently the only way to interact with the MCP server.

File interactions (LSP)

lean_file_contents

Get the contents of a Lean file, optionally with line number annotations.

lean_diagnostic_messages

Get all diagnostic messages for a Lean file. This includes infos, warnings and errors.

lean_goal

Get the proof goal at a specific location (line or line & column) in a Lean file.

lean_term_goal

Get the term goal at a specific position (line & column) in a Lean file.

lean_hover_info

Retrieve hover information (documentation) for symbols, terms, and expressions in a Lean file (at a specific line & column).

lean_declaration_file

Get the file contents where a symbol or term is declared.

lean_completions

Code auto-completion: Find available identifiers or import suggestions at a specific position (line & column) in a Lean file.

lean_run_code

Run/compile an independent Lean code snippet/file and return the result or error message.

lean_multi_attempt

Attempt multiple lean code snippets on a line and return goal state and diagnostics for each snippet. This tool is useful to screen different proof attempts before using the most promising one.

External Search Tools

Currently all external tools are rate limited to 3 requests per 30 seconds. This will change based on provider feedback.

lean_leansearch

Search for theorems in Mathlib using leansearch.net (natural language search).

Github Repository | Arxiv Paper

• Supports natural language, mixed queries, concepts, identifiers, and Lean terms.
• Example: bijective map from injective, n + 1 <= m if n < m, Cauchy Schwarz, List.sum, {f : A → B} (hf : Injective f) : ∃ h, Bijective h

  {
    "module_name": "Mathlib.Logic.Function.Basic",
    "kind": "theorem",
    "name": "Function.Bijective.injective",
    "signature": " {f : α → β} (hf : Bijective f) : Injective f",
    "type": "∀ {α : Sort u_1} {β : Sort u_2} {f : α → β}, Function.Bijective f → Function.Injective f",
    "value": ":= hf.1",
    "informal_name": "Bijectivity Implies Injectivity",
    "informal_description": "For any function $f \\colon \\alpha \\to \\beta$, if $f$ is bijective, then $f$ is injective."
  },
  ...

lean_loogle

Search for Lean definitions and theorems using loogle.lean-lang.org.

Github Repository

• Supports queries by constant, lemma name, subexpression, type, or conclusion.
• Example: Real.sin, "differ", _ * (_ ^ _), (?a -> ?b) -> List ?a -> List ?b, |- tsum _ = _ * tsum _

[
  {
    "type": " (x : ℝ) : ℝ",
    "name": "Real.sin",
    "module": "Mathlib.Data.Complex.Trigonometric"
  },
  ...
]

lean_state_search

Search for applicable theorems for the current proof goal using premise-search.com.

Github Repository | Arxiv Paper

A self-hosted version is available and encouraged. You can set an environment variable LEAN_STATE_SEARCH_URL (see Setup 3. for an example) to point to your self-hosted instance. It defaults to https://premise-search.com.

Uses the first goal at a given line and column. Returns a list of relevant theorems.

[
  {
    "name": "Nat.mul_zero",
    "formal_type": "∀ (n : Nat), n * 0 = 0",
    "module": "Init.Data.Nat.Basic"
  },
  ...
]

lean_hammer_premise

Search for relevant premises based on the current proof state using the Lean Hammer Premise Search.

Github Repository | Arxiv Paper

A self-hosted version is available and encouraged. You can set an environment variable LEAN_HAMMER_URL (see Setup 3. for an example) to point to your self-hosted instance. It defaults to http://leanpremise.net.

Uses the first goal at a given line and column. Returns a list of relevant premises (theorems) that can be used to prove the goal.

Note: We use a simplified version, LeanHammer might have better premise search results.

[
  "MulOpposite.unop_injective",
  "MulOpposite.op_injective",
  "WellFoundedLT.induction",
  ...
]

Project-level tools

lean_build

Rebuild the Lean project and restart the Lean LSP server.

Disabling Tools

Many clients allow the user to disable specific tools manually (e.g. lean_build).

VSCode: Click on the Wrench/Screwdriver icon in the chat.

Cursor: In "Cursor Settings" > "MCP" click on the name of a tool to disable it (strikethrough).

Example Uses

Here are a few example prompts and interactions to try. All examples use VSCode (Agent Mode) and Gemini 2.5 Pro (Preview).

Use tools to assist with a proof

After installing the MCP, tools are automatically available to the agent. E.g. Open a Lean file with a sorry and run the following prompt: "Solve this sorry" The agent should use various tools such as lean_goal to understand and create a proof. You can also ask the agent to use tools explicitly, e.g. "Help me write this proof using tools." or "Use tools to analyze the goal and hover information, then write a proof."

Analyze a theorem

Open Algebra/Lie/Abelian.lean. Example prompt:

"Analyze commutative_ring_iff_abelian_lie_ring thoroughly using various tools such as goal, term goal, hover info. Explain the key proof steps in english.".

Design proof approaches

Open an incomplete proof such as putnam 1964 b2. Example prompt:

"First analyze the problem statement by checking the goal, hover info and looking up key declarations. Next use up to three queries to leansearch to design three different approaches to solve this problem. Very concisely present each approach and its key challenge."

Notes on MCP Security

There are many valid security concerns with the Model Context Protocol (MCP) in general!

This MCP server is meant as a research tool and is currently in beta. While it does not handle any sensitive data such as passwords or API keys, it still includes various security risks:

• Access to your local file system.
• No input or output validation.

Please be aware of these risks. Feel free to audit the code and report security issues!

For more information, you can use Awesome MCP Security as a starting point.

MCP Inspector

npx @modelcontextprotocol/inspector uvx --with-editable path/to/lean-lsp-mcp python -m lean_lsp_mcp.server

Related Projects

• LeanTool
• LeanExplore MCP

License & Citation

MIT licensed. See LICENSE for more information.

Citing this repository is highly appreciated but not required by the license.

@software{lean-lsp-mcp,
  author = {Oliver Dressler},
  title = {{Lean LSP MCP: Tools for agentic interaction with the Lean theorem prover}},
  url = {https://github.com/oOo0oOo/lean-lsp-mcp},
  month = {3},
  year = {2025}
}