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rnaseq-mcp

rnaseq-mcp

by sbarbi-gh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

read_table

Read the first rows of a CSV or TSV file to preview column names, sample aliases, and group labels as a markdown table, aiding in data inspection before analysis.

Instructions

Read the first n_rows of a CSV/TSV file from /data and return it as a markdown table. Useful for inspecting column names, sample aliases, and group labels before writing analysis code.

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": {
      "filename": "<filename>"
    },
    "name": "read_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
filenameYes
n_rowsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It states the tool is read-only, operates on files in /data, and returns a markdown table. It does not cover error cases or permissions, but for a simple read tool this is adequate.

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?

Two sentences with no wasted words. The purpose is front-loaded and every sentence adds value.

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 tool's simplicity, the description covers the key aspects: what it reads, from where, and for what purpose. The existence of an output schema further reduces the need to describe return values.

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 schema has 0% description coverage, but the description adds meaning by specifying that the file is a CSV/TSV from /data and that n_rows refers to 'first n_rows'. This compensates well for the lack of schema descriptions.

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 reads the first n_rows of a CSV/TSV file from /data and returns a markdown table. The verb 'Read' and resource 'CSV/TSV file' are specific and distinct from sibling tools like list_data_files or execute_python.

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 explicit usage context: 'Useful for inspecting column names, sample aliases, and group labels before writing analysis code.' However, it does not explicitly mention when not to use it or suggest alternatives, though the sibling tools implicitly provide options.

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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