feat(validator): add bare FastAgent + Pallas validator for Mnemosyne MCP

A self-contained sub-project under validator/ that wraps Mnemosyne's MCP
server in a single FastAgent. Use it to confirm — outside of Daedalus —
that Mnemosyne's MCP transport works, every tool registers, args/responses
round-trip, and an LLM can actually drive the tools.

The validator is its own Pallas-consuming project with its own pyproject
(pallas-mcp + fast-agent-mcp), agents.yaml, and fastagent.config.yaml —
matching the pattern used by Iolaus and other Pallas consumers. It does
not import Mnemosyne Python code; it only speaks MCP over HTTP.

The agent never sets workspace_id, so all calls run against the global
scope (libraries with workspace_id IS NULL). Workspace-scoped validation
will come once Daedalus's chat path is wired (Daedalus injects
workspace_id server-side, force-overwriting whatever the LLM produces).

Default model is openai.Qwen3.5-35B-A3B-UD-Q4_K_XL.gguf served by
llama.cpp at nyx.helu.ca:22079/v1. Token provisioning via
`python manage.py create_mcp_token --user <u> --name validator`.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

validator/agents/mnemosyne_validator.py

@@ -0,0 +1,56 @@
+"""
+Mnemosyne Validator Agent
+
+A bare FastAgent that wraps the Mnemosyne MCP server. Exists solely to
+exercise the Mnemosyne MCP transport, tool registration, and round-trip
+serialization — no production role.
+
+Drive it from the CLI to confirm:
+- search works against the running Mnemosyne (vector + fulltext + graph)
+- get_chunk fetches full chunk text from S3
+- list_libraries / list_collections / list_items return the expected shape
+- get_health returns ok/degraded with the right dependency breakdown
+
+When the Daedalus integration ships, the workspace_id parameter will be
+injected by Daedalus's chat path (force-overwritten before the call leaves
+Daedalus). This validator never sets it — meaning all calls go to the
+GLOBAL scope (libraries with workspace_id IS NULL).
+"""
+from fast_agent import FastAgent
+
+fast = FastAgent("Mnemosyne Validator", parse_cli_args=False)
+
+
+@fast.agent(
+    name="mnemosyne_validator",
+    instruction="""You are a validator for the Mnemosyne knowledge base. Your
+job is to exercise its MCP tools when asked, report what you saw, and surface
+errors clearly.
+
+You have direct access to Mnemosyne via these tools:
+
+- search(query, library_uid?, library_type?, collection_uid?, limit?, rerank?, include_images?, search_types?)
+    Hybrid retrieval. Returns ranked chunks with text_preview (~500 chars),
+    chunk_uid, item_uid, item_title, library_type, score, source.
+- get_chunk(chunk_uid)
+    Fetch the full text of a chunk by uid (typically obtained from search).
+- list_libraries(limit?, offset?)
+    List libraries (uid, name, library_type, description).
+- list_collections(library_uid?, limit?, offset?)
+    List collections, optionally filtered by parent library.
+- list_items(collection_uid?, library_uid?, limit?, offset?)
+    List items (documents) with chunk_count, embedding_status, etc.
+- get_health()
+    Health check: {status: ok|degraded|error, checks: {neo4j, s3, embedding}}.
+
+When the user asks "what libraries exist", call list_libraries and report.
+When they ask a research question, call search and surface chunk_uid + score
++ item_title for each candidate. If they want full text, call get_chunk.
+Show raw structured output, not flowery prose — this is a validation tool,
+not a chat assistant.
+
+If a tool errors, paste the error message verbatim.""",
+    servers=["mnemosyne"],
+)
+async def mnemosyne_validator():
+    pass