A bare FastAgent + Pallas project whose only purpose is to exercise Mnemosyne's MCP server end-to-end. Use it to confirm the transport works, every MCP tool registers, args/responses round-trip, and the local LLM can actually drive the tools.

This is not a production agent. It does not represent the long-term Daedalus integration — when Daedalus ships, it will inject workspace_id server-side. The validator never sets workspace_id, meaning all calls run against the global scope (libraries with workspace_id IS NULL).

Layout

validator/
├── pyproject.toml                 # pallas-mcp + fast-agent-mcp deps
├── agents.yaml                    # one-agent Pallas topology
├── fastagent.config.yaml          # default_model + mnemosyne MCP server
├── fastagent.secrets.yaml.example # schema for the (gitignored) secrets file
└── agents/
    └── mnemosyne_validator.py     # the FastAgent definition

All configuration lives in the FastAgent YAML files — no .env is used. The real fastagent.secrets.yaml is gitignored; fastagent.secrets.yaml.example documents its shape and is checked in.

Setup

cd validator/
python3.13 -m venv .venv
source .venv/bin/activate
pip install -e .

Copy the secrets template and fill in the bearer token (see next section):

cp fastagent.secrets.yaml.example fastagent.secrets.yaml

Provision an MCP bearer token

Mnemosyne requires a bearer token when MCP_REQUIRE_AUTH=True (the default). Generate one for your user:

cd ../mnemosyne
python manage.py create_mcp_token --user <username> --name validator

The command prints the token once — paste it into validator/fastagent.secrets.yaml under mcp.servers.mnemosyne.headers.Authorization (keep the Bearer prefix).

Start Mnemosyne's MCP server

The validator hits the Mnemosyne ASGI endpoint, so Mnemosyne's MCP server must be running. From the Mnemosyne project:

cd mnemosyne/
uvicorn mnemosyne.asgi:app --host 0.0.0.0 --port 22091 --workers 1

By default the validator points at http://localhost:22091/mcp/ (note the trailing slash — Starlette's Mount only matches paths under the mount prefix, so /mcp without the slash falls through to Django). If your Mnemosyne is on another host, override mcp.servers.mnemosyne.url in fastagent.secrets.yaml.

Run the validator

The mnemosyne-validator script is a thin alias for pallas:

# Start with the registry (Pallas mode):
mnemosyne-validator

# Or run the agent directly (no registry):
mnemosyne-validator --agent mnemosyne_validator

To chat with the agent directly without spinning up a Pallas registry, use the fast-agent CLI (provided by fast-agent-mcp):

fast-agent go --config-path fastagent.config.yaml --url http://localhost:24301/mcp mnemosyne_validator

What to test

These prompts exercise every Mnemosyne MCP tool. After each, the agent should call the named tool and surface the result.

Prompt	Tool	What to verify
"Run a health check on Mnemosyne."	`get_health`	Returns `status: ok` if Neo4j + S3 + embedding model are all reachable. `degraded` if one is down.
"List all libraries."	`list_libraries`	Returns the libraries seeded by `load_library_types`, each with `library_type` set.
"List collections in library `<uid>`."	`list_collections`	Returns collections inside the named library.
"List items in collection `<uid>`."	`list_items`	Returns items with `chunk_count` and `embedding_status`.
"Search the technical libraries for `<query>`."	`search`	Returns ranked candidates with `chunk_uid`, `score`, `text_preview`, `library_type`.
"Fetch the full text of chunk `<chunk_uid>`."	`get_chunk`	Returns the full chunk text from S3.

If a call errors, the agent surfaces it verbatim — that's the failure mode you want.

Troubleshooting

"Invalid MCP token" — token wasn't provisioned, was provisioned for a different user, or got mangled when pasted. Re-run create_mcp_token and paste again. Tokens are SHA-256 hashed at rest and can't be retrieved later.

"Couldn't connect to Mnemosyne" — the ASGI server isn't running, or it's bound to a different host/port than mcp.servers.mnemosyne.url says. Check curl http://localhost:22091/mcp/health returns {"status":"ok"}.

"No system embedding model configured" in get_health — LLMModel.get_system_embedding_model() returns nothing. Configure the embedding model via the Mnemosyne admin or manage.py before searches will work.

Search returns zero candidates with no error — Mnemosyne is reachable but has no embedded content yet. Upload an item and run embed_item, or use the Daedalus ingest endpoint, before re-testing search.