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Simba MCP Server

Official
by getsimba-ai
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Python
Hybrid

Get Data Schema

get_data_schema
Read-onlyIdempotent

Retrieve the JSON Schema specification for Simba MMM CSV inputs, including required columns, channel naming, constraints, and supported date formats.

Instructions

Get the canonical CSV data schema for Simba MMM input files.

Returns the JSON Schema specification describing required columns (date, KPI, multiplier, hierarchy), media channel column naming conventions ({channel}_activity, {channel}_spend), constraints (min rows, max file size), and supported date formats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "get_data_schemaDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value by disclosing the tool's return contract: a JSON Schema specification listing required columns, naming conventions, constraints, and date formats. No contradiction exists, and the behavior is fully consistent with a read-only metadata query.

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: the first sentence states the action and resource, and the second sentence enumerates the return contents with no wasted words. Every sentence contributes useful information, and the structure is easy to parse quickly.

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 no-parameter, read-only schema tool with an output schema, the description is complete. It tells the agent exactly what to expect in the response, lists the schema's key contents, and needs no additional context to select or invoke the tool correctly.

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?

The tool has zero parameters, so the input schema is trivially documented at 100% coverage. There are no parameter semantics for the description to clarify, and the baseline for zero-parameter tools is 4; the description appropriately focuses on the return value instead.

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 states a specific verb and resource: 'Get the canonical CSV data schema for Simba MMM input files.' It further clarifies the exact content returned (required columns, naming conventions, constraints, date formats), leaving no ambiguity about what the tool does. It is clearly distinguishable from all siblings, none of which offer a data 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?

The description provides clear context: this is the canonical schema for Simba MMM input files, which signals when an agent should call it for constructing or validating input data. It does not explicitly name exclusions or alternatives, but no sibling tool serves the same schema-retrieval purpose, so the implicit use-case guidance is adequate.

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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