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mcp-sac-tools

mcp-sac-tools

by pmankineni
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

sac_get_model_metadata

Retrieve model metadata including dimensions, measures, and data types by supplying a model ID.

Instructions

Get the metadata/schema of a model — dimensions, measures, and data types. Use a model ID from sac_list_models.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "modelId": "<modelId>"
    },
    "name": "sac_get_model_metadata"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelIdYesThe model/provider ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It correctly describes the return content but does not explicitly state it is a read-only operation. The behavioral traits are adequately conveyed for a simple GET-like tool.

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?

Two concise sentences front-load the purpose and output, then provide a usage tip. Every word earns its place, with no redundancy or fluff.

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?

The description explains the return value (dimensions, measures, data types) and input source, but lacks specification of output format (e.g., JSON structure). For a tool with no output schema, this is fairly complete.

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

Parameters5/5

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

The schema provides a basic description for 'modelId', but the description adds meaningful context ('Use a model ID from sac_list_models'), improving parameter understanding beyond the schema alone.

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 'Get' and the resource 'metadata/schema of a model', specifying the returned components (dimensions, measures, data types). It distinctly identifies the tool's purpose among siblings, which lack direct model metadata retrieval.

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?

The description provides clear context by instructing to use a model ID from 'sac_list_models', but does not explicitly distinguish this tool from siblings like 'sac_read_fact_data' or mention when not to use it. The prerequisite is helpful but exclusion criteria are absent.

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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