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fluig-mcp

fluig-mcp

by alucardigo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

fluig_dataset_structure

Returns a dataset's columns and types without executing it, so you can inspect the schema before running the dataset.

Instructions

Retorna a ESTRUTURA de um dataset (colunas + tipos) SEM executá-lo — útil para descobrir o schema antes de montar um fluig_dataset_run. Rota descoberta por eng. reversa do app oficial e confirmada ao vivo.

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": {
      "datasetId": "<datasetId>",
      "env": "<env>"
    },
    "name": "fluig_dataset_structure"
  }
}

Input Schema

NameRequiredDescriptionDefault
envYesOBRIGATÓRIO. Ambiente alvo: "teste" (homologação, o host de FLUIG_HOST) ou "prod" (PRODUÇÃO REAL, o host de FLUIG_HOST_PROD). Não há default: a escolha é sempre explícita, para que ninguém leia homologação achando que é produção nem escreva em produção achando que é teste.
datasetIdYesid do dataset (ex: colleague, ds_generic_rm_sql)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.3.0
    • changedInput schema / properties / datasetId / description
      Previous value: -"Dataset id."New value: +"id do dataset (ex: colleague, ds_generic_rm_sql)"
    • addedInput schema / properties / env
      Added value: +{
+  "description": "OBRIGATÓRIO. Ambiente alvo: \"teste\" (homologação, o host de FLUIG_HOST) ou \"prod\" (PRODUÇÃO REAL, o host de FLUIG_HOST_PROD). Não há default: a escolha é sempre explícita, para que ninguém leia homologação achando que é produção nem escreva em produção achando que é teste.",
+  "enum": [
+    "teste",
+    "prod"
+  ],
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "datasetId"
-]New value: +[
+  "datasetId",
+  "env"
+]
  2. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the single most important behavioral trait: the dataset is not executed, only its structure (columns + types) is returned. It also adds provenance ('rota descoberta por eng. reversa ... confirmada ao vivo'), which builds trust in the route. It omits error behavior for unknown datasetIds and permission requirements, keeping it below a 5.

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?

Two dense sentences with the core purpose and the non-execution guarantee front-loaded. The trailing provenance clause is mildly useful for trust but not strictly needed for invocation, slightly diluting an otherwise tight definition.

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?

For a two-parameter read tool with no output schema, the description says enough: it identifies the return content (columns + types) and the safety profile (no execution). Error handling and pagination/size limits on large datasets are the only meaningful gaps.

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% and the env parameter already carries a lengthy explanation of teste vs prod, so the schema does the heavy lifting. The description adds nothing about datasetId or env semantics beyond what is structured, so the baseline of 3 applies.

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?

States a specific verb and resource ('Retorna a ESTRUTURA de um dataset (colunas + tipos)') and immediately differentiates from its closest sibling by emphasizing 'SEM executá-lo' versus fluig_dataset_run. An agent can distinguish it from the run/get/list siblings without opening a schema.

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?

Gives explicit timing guidance: use it to discover the schema before assembling a fluig_dataset_run. It does not, however, distinguish itself from fluig_dataset_get or fluig_dataset_list, which could plausibly return overlapping metadata, so the routing is not fully airtight.

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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