# Mnemosyne Validator

A bare [FastAgent](https://github.com/evalstate/fast-agent) + [Pallas](https://git.helu.ca/r/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

```bash
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):

```bash
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:

```bash
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:

```bash
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`. 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`:

```bash
# 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`):

```bash
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.
