Skip to main content
Glama
Sign In
fes-mcp

fes-mcp

by hnegi01
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

datamodel.describe_datamodel

datamodel_describe_datamodel
Read-only

Retrieves a data model's structure as a flat, row-based format with one row per table. Resolves the model by name and returns dataset, connection, and table context for DataFrame or CSV export.

Instructions

Retrieve data model structure in a flat, row-based format. Resolves the data model by name and flattens its datasets and tables into one row per table, suitable for DataFrame or CSV export. Returns: list[dict[str, Any]] List of row dicts, each representing a single table with model, dataset, connection, and table context. Returns an empty list if the data model is not found.

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": {
      "datamodel_name": "<datamodel_name>"
    },
    "name": "datamodel_describe_datamodel"
  }
}

Input Schema

NameRequiredDescriptionDefault
datamodel_nameYesName (title) of the data model to describe.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses meaningful behavior: it resolves the data model by name, flattens datasets and tables, returns a list of row dicts, and returns an empty list when the model is not found. No contradiction with annotations exists.

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?

The description is compact and front-loaded with the primary purpose, then immediately explains the return format and missing-model behavior. Every sentence earns its place, and the return type note compensates for the lack of an output schema.

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

Completeness5/5

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

For a single-parameter, read-only tool with no output schema, the description fully covers what an agent needs: what the tool returns, the row structure, the missing-model behavior, and the use case. Nothing essential is missing.

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?

The schema already documents the only parameter with 100% coverage ('Name (title) of the data model to describe'). The description lightly reinforces the meaning by saying it 'resolves the data model by name' but adds no new depth beyond the schema.

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 uses a specific verb ('Retrieve') with a concrete resource ('data model structure') and clearly distinguishes its output format ('flat, row-based format', 'one row per table') from sibling tools like datamodel_get_model_schema. The mention of DataFrame/CSV suitability further clarifies its unique role.

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 about when to use this tool: when a flat, row-oriented representation of a data model is needed, especially for DataFrame or CSV export. It does not explicitly name alternatives or exclusions, but the format-oriented context is sufficient for most selection scenarios.

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

Deploy Server

Other Tools