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millimap-mcp

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by milliomics

list_analysis_cards

Retrieve a summary list of analysis result cards from the MilliMap workspace sidebar. Get an overview of available cards without loading full details.

Instructions

List the analysis result cards visible in MilliMap's workspace sidebar.

Same data as the millimap://analysis_cards resource — returns an array of summaries. Use get_analysis_card to load the full payload for a specific card.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The `list_analysis_cards()` function is decorated with @mcp.tool() — it returns a JSON-formatted list of analysis result cards from the MilliMap session snapshot by reading the "analysis_cards" key.
    @mcp.tool()
    def list_analysis_cards() -> str:
        """List the analysis result cards visible in MilliMap's workspace sidebar.
    
        Same data as the ``millimap://analysis_cards`` resource — returns an
        array of summaries. Use ``get_analysis_card`` to load the full payload
        for a specific card.
        """
        return _fmt_json(_load_snapshot().get("analysis_cards", []))
  • The `analysis_cards_resource()` function provides the same data as a resource (millimap://analysis_cards), using the same _load_snapshot().get('analysis_cards', []) pattern.
    @mcp.resource("millimap://analysis_cards")
    def analysis_cards_resource() -> str:
        """Analysis result cards shown in MilliMap's workspace sidebar.
    
        Each entry is a summary — title, type, method, timestamp, dataset,
        and dataframe shape if applicable. Use the ``get_analysis_card`` tool
        with the card's ``id`` to read its full payload (including the
        underlying DataFrame).
        """
        return _fmt_json(_load_snapshot().get("analysis_cards", []))
  • The `_load_snapshot()` helper reads and returns the full session JSON from ~/.millimap/mcp_session.json, handling missing files and parse errors.
    def _load_snapshot() -> dict[str, Any]:
        if not SNAPSHOT_PATH.exists():
            return {
                "error": "no_snapshot",
                "message": (
                    f"No MilliMap snapshot found at {SNAPSHOT_PATH}. "
                    "Is MilliMap running with a dataset loaded?"
                ),
            }
        try:
            with SNAPSHOT_PATH.open("r") as f:
                return json.load(f)
        except Exception as exc:
            return {"error": "read_failed", "message": str(exc)}
  • The `_fmt_json()` helper serializes Python objects to indented JSON strings.
    def _fmt_json(payload: Any) -> str:
        return json.dumps(payload, indent=2, default=str)
  • The `mcp` FastMCP instance used for the @mcp.tool() decorator that registers `list_analysis_cards`.
    mcp = FastMCP("millimap")
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that it returns an array of summaries, but does not mention any side effects, ordering, pagination, or other behavioral characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words, front-loading the main purpose and then providing a sibling reference.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and no parameters, the description is mostly complete. It could optionally mention if there is any limit or pagination, but not necessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameters and schema description coverage is 100%. With no params, the description does not need to add parameter info, and the baseline is 4 per rubric.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists analysis result cards visible in MilliMap's workspace sidebar, and distinguishes from the sibling tool get_analysis_card by noting that the latter loads the full payload for a specific card.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It mentions that the data is the same as the millimap://analysis_cards resource and suggests using get_analysis_card for full details, providing context on when to use this tool versus alternatives, though it does not explicitly list scenarios to avoid.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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