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FCCS MCP Agentic Server

by ivossos

get_members

Retrieve dimension members from Oracle EPM Cloud FCCS to access and analyze financial consolidation data for reporting and management tasks.

Instructions

Get members of a specific dimension / Obter membros de uma dimensao especifica

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dimension_nameYesThe name of the dimension

Implementation Reference

  • The main handler function implementing get_members tool: retrieves dimension members from FCCS client with caching support.
    async def get_members(dimension_name: str) -> dict[str, Any]:
        """Get members of a specific dimension / Obter membros de uma dimensao especifica.
    
        Args:
            dimension_name: The name of the dimension.
    
        Returns:
            dict: List of dimension members.
        """
        cache = get_cache_service()
        cache_key = f"members:{dimension_name}"
        
        if cache:
            cached_data = cache.get(cache_key)
            if cached_data:
                return {"status": "success", "data": cached_data, "source": "cache"}
    
        members = await _client.get_members(_app_name, dimension_name)
        
        if cache and members:
            cache.set(cache_key, members, ttl_seconds=86400)  # Cache for 24 hours
            
        return {"status": "success", "data": members, "source": "fccs"}
  • JSON schema definition for the get_members tool input, part of TOOL_DEFINITIONS list.
        "name": "get_members",
        "description": "Get members of a specific dimension / Obter membros de uma dimensao especifica",
        "inputSchema": {
            "type": "object",
            "properties": {
                "dimension_name": {
                    "type": "string",
                    "description": "The name of the dimension",
                },
            },
            "required": ["dimension_name"],
        },
    },
  • Registration of dimension tools including get_members in the central TOOL_HANDLERS dictionary.
    "get_dimensions": dimensions.get_dimensions,
    "get_members": dimensions.get_members,
    "get_dimension_hierarchy": dimensions.get_dimension_hierarchy,
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 for behavioral disclosure. It only states the action ('get members') without describing what 'members' are (e.g., dimension items, values), the return format (e.g., list, structured data), potential errors (e.g., invalid dimension name), or system behavior (e.g., read-only, no side effects). For a tool with zero annotation coverage, this is a significant gap in transparency.

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 and front-loaded with the primary English statement, followed by a Portuguese translation. The English part is a single, clear sentence that conveys the core purpose without waste. However, the Portuguese translation adds redundancy without value for an AI agent, slightly reducing efficiency.

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 simple parameter (1 param with 100% coverage), the description is incomplete. It lacks details on what 'members' are, the return format, error handling, or behavioral traits (e.g., read-only vs. mutating). For a tool in a context with many siblings (e.g., data retrieval and processing tools), more context is needed to ensure proper 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 the parameter 'dimension_name' documented as 'The name of the dimension'. The description adds no additional parameter semantics beyond this (e.g., format examples, valid dimension types, or constraints). According to guidelines, with high schema coverage (>80%), the baseline is 3 even without param info in the description, which applies here.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Get members of a specific dimension' (verb 'get' + resource 'members' + scope 'specific dimension'). It distinguishes from siblings like 'get_dimensions' (which likely lists dimensions) and 'get_dimension_hierarchy' (which likely shows hierarchical relationships). However, it doesn't explicitly differentiate from 'smart_retrieve' tools that might also retrieve dimension data, keeping it at 4 rather than 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing dimension), exclusions (e.g., not for non-dimension data), or comparisons to siblings like 'get_dimensions' (for listing dimensions) or 'smart_retrieve' tools (for more complex queries). This lack of context leaves the agent to infer usage from the tool name alone.

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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