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Simba MCP Server

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by getsimba-ai
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Python
Hybrid

Get Upload

get_upload
Read-onlyIdempotent

Retrieve a dataset's details and column schema by file ID, providing metadata like row count, column count, and column dtypes to build model arguments without re-reading the CSV.

Instructions

Get one uploaded dataset's details, including its column schema.

Returns id, filename, original_filename, source_type, mime_type, file_size, row_count, column_count, columns ([{name, dtype}, ...] — use these to build create_model's channel/control column arguments without re-reading the CSV), and created_at.

Args: file_id: The upload's id, from upload_data's response or list_uploads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_idYes

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_uploadDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds useful non-obvious context: it returns a structured column schema and that this avoids re-reading the CSV for model building. This goes beyond what annotations alone provide without contradicting them.

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 front-loaded with the purpose, followed by a compact return list and a clear Args section. Despite listing many fields, every sentence earns its place because it helps the agent know exactly what will be returned and how to use it. No filler or redundancy.

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 single parameter, the safe-read annotations, and the rich output schema, the description is complete for tool selection and invocation. It explains the parameter source, the return contents, and the downstream use case, leaving no practical gap for an agent to call this tool correctly.

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

Parameters5/5

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

The input schema provides only 'integer' and 'File Id' with 0% description coverage, so the description must carry the semantics. It does: 'The upload's id, from upload_data's response or list_uploads.' This fully explains the parameter's meaning and provenance, which is exactly what an agent needs.

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 begins with a specific verb and resource: 'Get one uploaded dataset's details, including its column schema.' This clearly distinguishes it from list_uploads (which lists uploads) and upload_data (which creates an upload). The resource and output are unambiguous.

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 gives clear context on when this tool is useful: it explains that the returned columns can be used to build create_model's channel/control arguments without re-reading the CSV. It also specifies where to obtain file_id (from upload_data or list_uploads). It stops short of naming alternatives or explicit when-not-to-use cases, so it does not earn a 5.

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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