Skip to main content
Glama

ppm_export_resources

Export resource allocations to XLSX. Filter by project, employee, department, date range, or include cancelled allocations.

Instructions

Export resource allocations to XLSX.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNo
employee_idNo
department_idNo
date_fromNo
date_toNo
include_cancelledNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the ppm_export_resources tool. It builds a dict of filter values from optional parameters (project_id, employee_id, department_id, date_from, date_to, include_cancelled) and delegates to _run_export_wizard('ppm.resource.export.wizard', vals) to create a wizard record and trigger the export action.
    @mcp.tool()
    def ppm_export_resources(
        project_id: int | None = None,
        employee_id: int | None = None,
        department_id: int | None = None,
        date_from: str | None = None,
        date_to: str | None = None,
        include_cancelled: bool = False,
    ) -> dict[str, Any]:
        """Export resource allocations to XLSX."""
        vals: dict[str, Any] = {"include_cancelled": include_cancelled}
        if project_id is not None:
            vals["project_id"] = project_id
        if employee_id is not None:
            vals["employee_id"] = employee_id
        if department_id is not None:
            vals["department_id"] = department_id
        if date_from:
            vals["date_from"] = date_from
        if date_to:
            vals["date_to"] = date_to
        return _run_export_wizard("ppm.resource.export.wizard", vals)
  • The @mcp.tool() decorator on line 421 registers the ppm_export_resources function as an MCP tool with the FastMCP instance.
    @mcp.tool()
  • The _run_export_wizard helper function that ppm_export_resources delegates to. It creates a wizard record via execute_kw, calls the action_export button method on it, and returns the download URL action from the response.
    def _run_export_wizard(
        model: str,
        values: dict[str, Any],
    ) -> dict[str, Any]:
        wizard_id = client().execute_kw(model, "create", [values])
        action = client().call_action(model, "action_export", [wizard_id])
        # action is `ir.actions.act_url` with /web/content/{attachment_id}?download=true
        return {"wizard": model, "action": action}
  • The function signature defines the input schema: optional project_id (int), employee_id (int), department_id (int), date_from/date_to (str), and include_cancelled (bool, default False). The return type is dict[str, Any] with keys 'wizard' and 'action'.
    def ppm_export_resources(
        project_id: int | None = None,
        employee_id: int | None = None,
        department_id: int | None = None,
        date_from: str | None = None,
        date_to: str | None = None,
        include_cancelled: bool = False,
    ) -> dict[str, Any]:
Behavior2/5

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

No annotations are provided, so the description must cover behavioral traits. It only says 'export', implying a read-only operation, but omits details like permissions, side effects, or outcome (e.g., file download). The description is insufficiently transparent.

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 a single sentence, making it concise and front-loaded. However, it could be slightly expanded without losing conciseness to include parameter hints or context.

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

Completeness2/5

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

With six optional parameters and no explanations in the schema or description, the tool is under-documented. The output schema exists but is not referenced. The description fails to provide a complete understanding of how to use the tool effectively.

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

Parameters1/5

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

Schema description coverage is 0%, yet the description adds no information about the six parameters (e.g., filters, format). It does not clarify parameter usage or semantics beyond the schema's empty titles.

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 the verb 'export' and the resource 'resource allocations' with output format 'XLSX'. It distinguishes from sibling tools like ppm_export_budget and ppm_export_risks by specifying the content type.

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 when resource allocation export is needed, but provides no explicit guidance on when to use vs alternatives or any prerequisites. There is no contrast with other export tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wethti/qod-ppm-odoo-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server