Problem
The proposal mentions MCP tools for chat and multi-agent apps. MCP is a reusable
capability that should not be duplicated across every AI extension.
CPA needs a focused MCP client extension that can be composed with FastAPI AI apps.
Solution
Create extensions/fastapi-mcp-client compatible with fastapi-backend.
Scope:
- Add MCP client configuration, for example
mcp_servers.yaml or an env-driven
equivalent.
- Add a typed tool registry/service that can be imported by chat or LangGraph code.
- Include one local/example MCP server only if it is lightweight and testable.
- Add tests with fake/local transports; no network dependency.
- Add docs:
template/docs/MCP_CLIENT_GUIDE.md
template/docs/README.md.append
.env.example.append if needed.
- Add partial
pyproject.toml dependencies.
Constraints:
- Do not assume a specific LLM orchestration framework.
- Do not hardcode filesystem or database tools enabled by default.
- Document the security model: MCP servers are external capability providers and
should be treated as trusted integrations.
- No GitHub Actions in this extension.
Acceptance criteria:
- Generated app has a deterministic test for listing/invoking a fake MCP tool.
- Docs show how to add a server without editing core chat code.
Problem
The proposal mentions MCP tools for chat and multi-agent apps. MCP is a reusable
capability that should not be duplicated across every AI extension.
CPA needs a focused MCP client extension that can be composed with FastAPI AI apps.
Solution
Create
extensions/fastapi-mcp-clientcompatible withfastapi-backend.Scope:
mcp_servers.yamlor an env-drivenequivalent.
template/docs/MCP_CLIENT_GUIDE.mdtemplate/docs/README.md.append.env.example.appendif needed.pyproject.tomldependencies.Constraints:
should be treated as trusted integrations.
Acceptance criteria: