Robert Helewka
97a14fb03a
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>
57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
"""
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Mnemosyne Validator Agent
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A bare FastAgent that wraps the Mnemosyne MCP server. Exists solely to
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exercise the Mnemosyne MCP transport, tool registration, and round-trip
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serialization — no production role.
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Drive it from the CLI to confirm:
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- search works against the running Mnemosyne (vector + fulltext + graph)
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- get_chunk fetches full chunk text from S3
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- list_libraries / list_collections / list_items return the expected shape
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- get_health returns ok/degraded with the right dependency breakdown
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When the Daedalus integration ships, the workspace_id parameter will be
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injected by Daedalus's chat path (force-overwritten before the call leaves
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Daedalus). This validator never sets it — meaning all calls go to the
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GLOBAL scope (libraries with workspace_id IS NULL).
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"""
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from fast_agent import FastAgent
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fast = FastAgent("Mnemosyne Validator", parse_cli_args=False)
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@fast.agent(
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name="mnemosyne_validator",
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instruction="""You are a validator for the Mnemosyne knowledge base. Your
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job is to exercise its MCP tools when asked, report what you saw, and surface
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errors clearly.
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You have direct access to Mnemosyne via these tools:
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- search(query, library_uid?, library_type?, collection_uid?, limit?, rerank?, include_images?, search_types?)
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Hybrid retrieval. Returns ranked chunks with text_preview (~500 chars),
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chunk_uid, item_uid, item_title, library_type, score, source.
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- get_chunk(chunk_uid)
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Fetch the full text of a chunk by uid (typically obtained from search).
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- list_libraries(limit?, offset?)
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List libraries (uid, name, library_type, description).
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- list_collections(library_uid?, limit?, offset?)
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List collections, optionally filtered by parent library.
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- list_items(collection_uid?, library_uid?, limit?, offset?)
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List items (documents) with chunk_count, embedding_status, etc.
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- get_health()
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Health check: {status: ok|degraded|error, checks: {neo4j, s3, embedding}}.
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When the user asks "what libraries exist", call list_libraries and report.
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When they ask a research question, call search and surface chunk_uid + score
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+ item_title for each candidate. If they want full text, call get_chunk.
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Show raw structured output, not flowery prose — this is a validation tool,
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not a chat assistant.
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If a tool errors, paste the error message verbatim.""",
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servers=["mnemosyne"],
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)
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async def mnemosyne_validator():
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pass
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