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ghidra-api-mcp

by Taardisaa
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ghidra-api-mcp

A MCP tool that helps LLMs write correct Ghidra scripts by providing API call-flows extracted from Ghidra's own source code.

Problem Statement

LLMs frequently get Ghidra API call sequences wrong. Decompiling a function isn't a single API call; it requires constructing a DecompInterface, calling openProgram(), obtaining a Function, invoking decompileFunction(), checking decompileCompleted(), and calling dispose(). Miss any step and the script silently fails.

This tool automatically extracts these workflow patterns from Ghidra's own source code, indexes them via chromadb, and serves them via MCP so any agent can query them.

MCP Tools

Tool

Purpose

Input

initialize_index

Build the RAG database (run once before first use)

Optional path to local Ghidra source

get_index_info

Show Ghidra version, build timestamp, and record counts

—

clear_index

Delete the index (use before a clean rebuild)

—

get_workflows

Find API call sequences for a task

Natural-language task description

get_api_doc

Look up a class or method (fuzzy match)

Class/method name or keyword

list_related_apis

Find co-occurring APIs

Class name

Example

initialize_index()                                    # first-time setup; clones Ghidra automatically
initialize_index("/path/to/ghidra")                   # or point at a local Ghidra source tree

get_workflows("decompile a function to C code")

Returns:

Workflow: decompileFunction
Source: Ghidra/Features/Decompiler/src/test/...

1. new DecompInterface()
2. ifc.openProgram(...)       [uses ifc from step 1]
3. program.getListing().getFunctionAt(...)
4. ifc.decompileFunction(...) [uses func from step 3]
5. res.decompileCompleted()
6. res.getDecompiledFunction().getC()
7. ifc.dispose()

Setup

Quick start (from PyPI)

1. Add the server to Claude Code:

claude mcp add ghidra-api-mcp -- uvx ghidra-api-mcp

Or for Claude Desktop, add to your config file (~/.config/Claude/claude_desktop_config.json on Linux, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "ghidra-api-mcp": {
      "command": "uvx",
      "args": ["ghidra-api-mcp"]
    }
  }
}

2. Build the index on first use:

Call initialize_index from Claude — it will clone Ghidra automatically and build the RAG database (takes 10–30 minutes). Subsequent sessions reuse the built index.

From source

git clone https://github.com/Taardisaa/ghidra-api-mcp.git
cd ghidra-api-mcp
python3 -m venv .venv
.venv/bin/pip install -e ".[dev]"

Add to Claude Code:

claude mcp add ghidra-api-mcp -- uv run --directory /path/to/ghidra-api-mcp ghidra-api-mcp

Or build the index offline first (CLI):

# Auto-clone Ghidra
ghidra-api-mcp-admin build-index

# Or point at a local Ghidra source tree
ghidra-api-mcp-admin build-index --ghidra-path /path/to/ghidra

Inspect / test without MCP:

ghidra-api-mcp-admin inspect info                               # get_index_info
ghidra-api-mcp-admin inspect workflows "decompile a function"   # get_workflows
ghidra-api-mcp-admin inspect api-doc DecompInterface            # get_api_doc
ghidra-api-mcp-admin inspect related DecompInterface            # list_related_apis

Clear the index:

ghidra-api-mcp-admin clear-index

How It Works

[1. Collect]  Enumerate Java files from Ghidra source (tests, examples, main code)
      ↓
[2. Parse]    tree-sitter Java → AST
      ↓
[3. Extract]  Identify ghidra.* API calls per function
              Track variable assignments to build data-flow edges
              Build call-chain graphs: call_A --output_feeds--> call_B
      ↓
[4. Index]    Store call chains + source snippets in ChromaDB
              Embed with semantic vectors for natural-language search
      ↓
[5. Serve]    MCP server retrieves relevant workflows at query time

Data sources are ranked by trust: Ghidra's own tests and examples surface first, main source code second.

Development

# Run tests
.venv/bin/pytest -v

# Lint
.venv/bin/ruff check src/ tests/

Note

Warnings when indexing chromadb: The following error may appear during indexing:

[W:onnxruntime:Default, device_discovery.cc:164 DiscoverDevicesForPlatform] GPU device discovery failed: device_discovery.cc:89 ReadFileContents Failed to open file: "/sys/class/drm/card0/device/vendor"

This is expected. It will fallback to CPU embedding if GPU is unavailable.

License

MIT

This server cannot be installed

A
license - permissive license
-
quality - not tested
B
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
1Releases (12mo)

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