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emovi-mcp

emovi-mcp

by Lalitronico
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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variable_detail

Retrieve the label, value labels, dataset, and section for any variable in the ESRU-EMOVI 2023 survey.

Instructions

Get detailed information about a specific variable.

Args: variable: The variable name (e.g., 'educ', 'ingc_pc', 'sexo').

Returns the variable label, value labels, dataset, and section.

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": {
      "variable": "<variable>"
    },
    "name": "variable_detail"
  }
}

Input Schema

NameRequiredDescriptionDefault
variableYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It does not mention that the tool is read-only, safe, or any side effects. The description only lists what is returned, omitting critical transparency details.

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 extremely concise with two sentences plus a parameter doc string. The main purpose is stated upfront, with no extraneous information.

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?

Given the existence of an output schema, the description adequately summarizes return fields (label, value labels, dataset, section). However, it lacks information on error handling or behavior when a variable is not found, which would enhance completeness.

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 0%, so the description must compensate. It explains the variable parameter with examples and implies string input. This adds significant meaning beyond the schema, though it could specify case sensitivity or exact matching.

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 it provides detailed information about a specific variable, using a verb ('get') and resource ('variable'). It gives examples of variable names, distinguishing it from siblings like list_variables which only lists names.

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 implies use when needing details on a single variable but does not explicitly state when to use this tool over alternatives like list_variables. No 'when not to use' or exclusion criteria are provided.

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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