gart/code/docs/MCP_ECOSYSTEM.md

MCP Ecosystem & Companion Tools

How FCPXML MCP fits into the broader Model Context Protocol ecosystem, and which companion servers pair well with video editing workflows.

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What Is MCP?

The Model Context Protocol is an open standard that lets AI models (like Claude) call tools exposed by local servers. Each MCP server is a specialist — it owns one domain and exposes tools for that domain. Claude Desktop (or any MCP client) can connect to multiple servers simultaneously, composing their capabilities in a single conversation.

Claude Desktop
├── fcp-mcp-server       → Final Cut Pro timeline operations
├── gitnexus             → Codebase knowledge graph + architecture analysis
├── filesystem           → General file read/write
└── your-custom-server   → Whatever you build

This is the key insight: MCP servers compose. You don't need one server that does everything. You need focused servers that each do one thing well, and an AI client that orchestrates them.

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Companion: GitNexus

What it does: GitNexus indexes a codebase into a knowledge graph — files, functions, classes, imports, dependencies — and exposes that graph via CLI, MCP tools, and a browser-based Web UI. It enables deep architectural understanding without manually tracing call chains.

Why it's relevant (relevance: 85%):

Capability	How It Helps FCPXML MCP Development
Knowledge graph indexing	Maps all 47 tool handlers, their dependencies on parser/writer/models, and cross-module call chains — useful for onboarding contributors
Architecture visualization	Web UI renders the server.py → fcpxml/*.py dispatch tree visually, showing which handlers touch which modules
Impact analysis	Before changing TimeValue arithmetic in models.py, query the graph to see every downstream consumer — prevents regressions
MCP tool exposure	Runs as a sibling MCP server alongside FCPXML MCP — Claude can query the codebase graph and manipulate timelines in the same session
CLI for CI	Index the repo in CI, query for orphan functions or circular imports as part of the lint pipeline

Example workflow — using both servers together:

User: "I want to add a new export format. Show me how the existing
       export pipeline works, then generate a template FCPXML."

Claude:
  1. [GitNexus] Query knowledge graph for export.py call chain
  2. [GitNexus] Show all functions that call writer.write_fcpxml()
  3. [FCPXML MCP] Generate a sample timeline via auto_rough_cut
  4. [FCPXML MCP] Export it via export_resolve_xml to see the pattern
  → Claude synthesizes the architecture + a working example

Setup (alongside FCPXML MCP):

{
  "mcpServers": {
    "fcpxml": {
      "command": "uv",
      "args": ["--directory", "/path/to/fcp-mcp-server", "run", "server.py"],
      "env": { "FCP_PROJECTS_DIR": "/Users/you/Movies" }
    },
    "gitnexus": {
      "command": "gitnexus",
      "args": ["mcp", "--repo", "/path/to/fcp-mcp-server"]
    }
  }
}

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Other Useful Companion Servers

Server	Domain	Pairing Use Case
filesystem	File read/write	Read raw FCPXML files, write export outputs
memory	Persistent context	Remember project preferences across sessions
fetch	HTTP requests	Pull beat analysis JSON from remote APIs
sqlite	Database queries	Track edit history, QC results over time

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Building Your Own MCP Server

If GitNexus and FCPXML MCP inspire you to build a domain-specific server, the pattern is straightforward:

1. Pick a domain — one data format, one API, one workflow
2. Define tools — each tool is a function with typed inputs and text outputs
3. Use the MCP SDK — pip install mcp, subclass Server, register handlers
4. Test locally — uv run server.py starts the server, Claude Desktop connects

See server.py in this repo for a production example of the dispatch-dict pattern with 47 tools.

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Last updated: 2026-02-25