Skip to main content
Glama
Sign In
fes-mcp

fes-mcp

by hnegi01
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

access_management.get_datamodel_columns

access_management_get_datamodel_columns
Read-only

Retrieve all columns from a DataModel by name, resolving its ID and scanning datasets and tables to return column details for access management.

Instructions

Retrieve columns from a DataModel by collecting them from its datasets and tables. Resolves the DataModel ID by title, then walks its datasets and tables to gather every column. Returns: list[dict[str, Any]] A list of dictionaries, each containing datamodel_id, datamodel_name, table, and column. An empty list is returned if the DataModel cannot be found or has no columns.

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": "access_management_get_datamodel_columns"
  }
}

Input Schema

NameRequiredDescriptionDefault
datamodel_nameYesThe name of the DataModel from which to extract columns.

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?

Annotations already establish that this is a read-only, non-destructive operation. The description adds meaningful behavioral details beyond those annotations: it resolves the DataModel by title, walks datasets and tables, and returns an empty list if the DataModel is not found or has no columns. This gives the agent useful expectations about edge cases.

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 three sentences with no wasted words: purpose, resolution/collection process, and return format/edge behavior. Each sentence contributes necessary information and the most important action is front-loaded.

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?

Given the simple single-parameter input and the presence of read-only annotations, the description is complete. It explains the return value shape and the empty-list behavior, which is especially important because no output schema is provided.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds extra semantic value by clarifying that datamodel_name is resolved as a title to an ID, which is critical for correct invocation. It also reinforces that an unknown title yields an empty list rather than an error.

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 columns') and resource ('DataModel'), and distinguishes itself by explaining it collects columns from datasets and tables after resolving the DataModel ID by title. This clearly differentiates it from sibling tools like datamodel_get_table_schema, which target a single table schema.

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 clearly implies when to use the tool: when you have a DataModel title and need all its columns aggregated from datasets and tables. However, it does not explicitly mention alternatives or state when not to use this tool, leaving some inference required.

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