#Connect fpf-memory MCP

Use this page when you want ChatGPT, Claude, an editor, or a coding CLI to retrieve bounded FPF context through the hosted MCP server.

#Hosted endpoint

https://mcp.fpf.sh/api/mcp/fpf_memory/mcp

This endpoint is the direct Vercel-hosted MCP origin over streamable HTTP. It exposes the public fpf-memory tools for catalog browsing, search, compact answers, exact generated doc reads, and index health.

Public tools:

• browse_fpf_catalog
• search_fpf
• ask_fpf
• query_fpf_spec
• read_fpf_doc
• get_fpf_index_status

#Test first

After adding the server, ask your client to call get_fpf_index_status. Then run a compact route query:

Use only fpf_memory. Call query_fpf_spec with question: "Project kickoff: align a project information system with roles and adoption next steps" and mode "compact". Return the route ID, ordered IDs, acceptance check, and next move.

A good response should include route:project-alignment in ids, then bounded next steps rather than a full FPF paste.

#ChatGPT

Use this path for ChatGPT custom apps/connectors.

1. Open ChatGPT settings.
2. Go to Apps & Connectors.
3. Enable developer mode under Advanced settings if your plan or workspace requires it.
4. Create a new custom app or connector.
5. Set the connector URL to the hosted endpoint above.
6. Create the connector, confirm the advertised tools, then open a new chat and add the connector from the composer tools menu.

Reference: OpenAI Apps SDK - connect from ChatGPT.

#Claude

Use this path for Claude chat, Claude Desktop, or Cowork custom connectors.

1. Open Claude settings.
2. Go to Customize > Connectors.
3. Click the + button next to Connectors.
4. Choose Add custom connector.
5. Enter fpf-memory as the name and the hosted endpoint as the URL.
6. Add it, then connect it in the same way as other Claude connectors.

Reference: Claude Help - custom connectors.

#VS Code

Use the MCP: Add Server command, or add a workspace config at .vscode/mcp.json:

{
  "servers": {
    "fpf_memory": {
      "type": "http",
      "url": "https://mcp.fpf.sh/api/mcp/fpf_memory/mcp"
    }
  }
}

Restart or start the server from the MCP code lens, then confirm trust when VS Code asks.

Reference: VS Code - add and manage MCP servers.

#Zed

Open the Agent Panel settings with agent: open settings, then add a custom server:

{
  "context_servers": {
    "fpf-memory": {
      "url": "https://mcp.fpf.sh/api/mcp/fpf_memory/mcp"
    }
  }
}

The indicator next to the server should turn green when the server is active.

Reference: Zed - Model Context Protocol.

#Codex CLI

Add the remote streamable HTTP server:

codex mcp add fpf_memory --url https://mcp.fpf.sh/api/mcp/fpf_memory/mcp

Equivalent ~/.codex/config.toml entry:

[mcp_servers.fpf_memory]
url = "https://mcp.fpf.sh/api/mcp/fpf_memory/mcp"

Reference: Codex configuration reference.

#Claude Code

Add the remote HTTP server:

claude mcp add --transport http fpf_memory https://mcp.fpf.sh/api/mcp/fpf_memory/mcp

Check it inside Claude Code with /mcp.

Reference: Claude Code - connect to tools via MCP.

#Pi

Pi supports MCP through extensions. Install an MCP extension, then add a streamable HTTP server config.

pi install npm:pi-mcp-extension

Global config at ~/.pi/agent/mcp.json or project config at .pi/mcp.json:

{
  "mcpServers": {
    "fpf_memory": {
      "transport": "streamable-http",
      "url": "https://mcp.fpf.sh/api/mcp/fpf_memory/mcp",
      "lifecycle": "eager"
    }
  }
}

Start Pi and use /mcp to check status.

Reference: Pi MCP extension.

#Good first prompt

Use only fpf_memory. First call get_fpf_index_status. If the index is available, find the smallest FPF route for this work: <describe work>. Return Context | Route ID | Ordered IDs | Friction avoided | Acceptance check | Next move.

Keep route answers compact. Read exact generated docs only when wording matters, and do not paste the full FPF into the chat.