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ppm_status_report_generate

Generate a project status report with auto-populated RAG, budget, risks, and milestones, created as a draft.

Instructions

Run the Status Report wizard for a project.

The wizard auto-populates RAG, budget, risks, milestones from project data and creates a ppm.status.report in draft state.

Args: project_id: project.project id to report on. period: free-text period label (e.g. 'April 2026'); defaults to current month. commentary: optional commentary field.

Returns: dict with the created status report id and summary fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
periodNo
commentaryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the ppm_status_report_generate tool. It creates a ppm.status.report.wizard record with project_id (and optional period/commentary), calls the wizard's action_create_report method, resolves the resulting report_id (with fallback search), and returns the status report's summary fields.
    @mcp.tool()
    def ppm_status_report_generate(
        project_id: int,
        period: str | None = None,
        commentary: str | None = None,
    ) -> dict[str, Any]:
        """Run the Status Report wizard for a project.
    
        The wizard auto-populates RAG, budget, risks, milestones from project data
        and creates a `ppm.status.report` in draft state.
    
        Args:
            project_id: `project.project` id to report on.
            period: free-text period label (e.g. 'April 2026'); defaults to current month.
            commentary: optional commentary field.
    
        Returns:
            dict with the created status report id and summary fields.
        """
        vals: dict[str, Any] = {"project_id": project_id}
        if period:
            vals["period"] = period
        if commentary:
            vals["commentary"] = commentary
        wizard_id = client().execute_kw("ppm.status.report.wizard", "create", [vals])
        result = client().call_action("ppm.status.report.wizard", "action_create_report", [wizard_id])
        report_id = (result or {}).get("res_id") if isinstance(result, dict) else None
        if not report_id:
            # Fallback: most recently-created report for this project.
            rows = client().search_read(
                "ppm.status.report",
                [("project_id", "=", project_id)],
                ["id"],
                limit=1,
                order="id desc",
            )
            if not rows:
                raise RuntimeError("Wizard did not produce a status report")
            report_id = rows[0]["id"]
        return _read_state(
            "ppm.status.report",
            report_id,
            ["name", "state", "period", "project_id", "rag_overall"],
        )
  • The tool is registered via the @mcp.tool() decorator on FastMCP instance 'mcp' (line 19).
    @mcp.tool()
    def ppm_status_report_generate(
  • Helper function _read_state used by the handler to read and return fields of the created status report.
    def _read_state(model: str, rec_id: int, fields: list[str]) -> dict[str, Any]:
        rows = client().read(model, [rec_id], fields)
        if not rows:
            raise ValueError(f"{model} id={rec_id} not found")
        return rows[0]
  • Helper function that provides the OdooClient instance used by the handler to execute Odoo RPC calls.
    def client() -> OdooClient:
        global _client
        if _client is None:
            _client = OdooClient.from_env()
        return _client
Behavior3/5

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

With no annotations, the description carries full burden. It states the report creation and auto-population behavior, but does not disclose permissions, side effects, or whether it can be run multiple times.

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

Conciseness4/5

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

The description is concise, with clear Args/Returns sections, and no extraneous information. Slightly more structured formatting could be used, but it's efficient.

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 an output schema exists, the description adequately covers return values and parameter details. It could mention error scenarios, but overall it's sufficient for the complexity.

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?

Schema description coverage is 0%, but the description adds meaning for each parameter (e.g., 'project_id: project.project id to report on', 'period: free-text period label'), compensating for the missing schema descriptions.

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 runs the Status Report wizard, auto-populates RAG/budget/risks/milestones, and creates a draft report. This distinguishes it from siblings like ppm_status_report_publish or ppm_status_report_reset_draft.

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

Usage Guidelines3/5

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

The description implies usage for generating a new draft report, but does not explicitly state when to use this tool over alternatives, nor does it provide when-not-to-use guidance.

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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