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FCCS MCP Agentic Server

by ivossos

generate_intercompany_matching_report

Generate intercompany matching reports in Oracle EPM Cloud FCCS to identify and resolve discrepancies between related entities during financial consolidation.

Instructions

Generate intercompany matching report / Gerar relatorio de correspondencia ICP

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parametersNoReport parameters (scenario, year, period, etc.)

Implementation Reference

  • The main async handler function that executes the tool logic by delegating to the FCCS client to generate the intercompany matching report.
    async def generate_intercompany_matching_report(
        parameters: Optional[dict[str, Any]] = None
    ) -> dict[str, Any]:
        """Generate intercompany matching report / Gerar relatorio de correspondencia ICP.
    
        Args:
            parameters: Report parameters (scenario, year, period, etc.).
    
        Returns:
            dict: Job submission result.
        """
        result = await _client.generate_intercompany_matching_report(_app_name, parameters)
        return {"status": "success", "data": result}
  • The tool schema definition including name, description, and input schema for parameters.
    {
        "name": "generate_intercompany_matching_report",
        "description": "Generate intercompany matching report / Gerar relatorio de correspondencia ICP",
        "inputSchema": {
            "type": "object",
            "properties": {
                "parameters": {
                    "type": "object",
                    "description": "Report parameters (scenario, year, period, etc.)",
                },
            },
        },
    },
  • Registration of the tool handler in the agent's TOOL_HANDLERS dictionary.
    "generate_intercompany_matching_report": consolidation.generate_intercompany_matching_report,
  • Supporting client method that performs the actual HTTP POST to FCCS jobs endpoint with jobType INTERCOMPANYMATCHING.
    async def generate_intercompany_matching_report(
        self,
        app_name: str,
        parameters: Optional[dict[str, Any]] = None
    ) -> dict[str, Any]:
        """Generate intercompany matching report / Gerar relatorio ICP."""
        if self.config.fccs_mock_mode:
            return {"jobId": "501", "status": "Submitted", "jobType": "IntercompanyMatching"}
    
        payload = {"jobType": "INTERCOMPANYMATCHING", **(parameters or {})}
        response = await self._client.post(
            f"/{app_name}/jobs{self._get_query_params()}",
            json=payload
        )
        response.raise_for_status()
        return response.json()
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions 'generate' but doesn't disclose behavioral traits like whether this is a read-only operation, if it triggers background jobs, what permissions are needed, or how results are delivered. For a tool with no annotations, this leaves critical gaps in understanding its behavior.

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 two language versions (English and Portuguese), which is efficient but slightly redundant. It front-loads the purpose clearly, though the bilingual format adds minimal value without extra context. Overall, it's well-structured with no wasted words.

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?

Given no annotations, no output schema, and a single nested parameter object, the description is incomplete. It doesn't explain what the report contains, how it's generated (e.g., async job), or what the user should expect. For a tool with potential complexity (implied by 'parameters' object), more context is needed to guide effective use.

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

Parameters3/5

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

Schema description coverage is 100%, with one parameter ('parameters') documented as 'Report parameters (scenario, year, period, etc.)'. The description adds no additional meaning beyond this, such as explaining the structure or required fields of the parameters object. Baseline 3 is appropriate since the schema handles the documentation.

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

Purpose3/5

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

The description states the tool generates an 'intercompany matching report' (verb+resource), which is clear but basic. It doesn't differentiate from sibling tools like 'generate_report', 'generate_consolidation_process_report', or 'generate_report_script', leaving ambiguity about when to use this specific tool versus other report generators.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. With multiple report-related siblings (e.g., 'generate_report', 'generate_consolidation_process_report'), the description lacks context about specific use cases, prerequisites, or exclusions, offering minimal help for selection.

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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