Skip to main content
Glama
sindhug

MCP DataFrame QA

by sindhug

execute_analysis_plan

Execute validated analysis plans on local dataframes to answer natural language questions, ensuring safe read-only execution.

Instructions

Execute a validated, read-only dataframe analysis plan.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
planYes
dataset_idNodefault

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

The description claims the tool is read-only but provides no other behavioral details. With no annotations, the description should disclose potential side effects, authorization needs, or error handling, but it fails to do so.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (one sentence) and front-loaded with purpose, but it sacrifices too much essential information. While efficient, it fails to fully earn its place by omitting critical details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of a nested object parameter and an output schema, the description is severely incomplete. It does not explain what constitutes a validated plan, what the output looks like, or how to obtain a plan. Sibling tools are listed but not contextualized.

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

Parameters1/5

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

The input schema has 0% description coverage, and the description adds no information about the parameters (plan or dataset_id). The agent cannot understand what valid input looks like or how to construct the plan.

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 the tool executes a validated, read-only dataframe analysis plan. It uses a specific verb and resource, and the read-only constraint distinguishes it from sibling tools like preview_dataframe and query_dataframe.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus its siblings, nor are prerequisites (e.g., having a validated plan) mentioned. The description lacks context for appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sindhug/mcp-dataframe-qa'

If you have feedback or need assistance with the MCP directory API, please join our Discord server