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gnomad-genetics-mcp-server

by io.github.cyanheads
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gnomad-genetics-mcp-server: dataframe describe

gnomad_dataframe_describe
Read-onlyIdempotent

List the tables staged on a canvas and their columns (name and type) so you can write correct SQL for gnomad_dataframe_query. Use the canvas_id returned by gnomad_list_gene_variants or gnomad_search_clinvar. Returns one entry per table with its row count and column schema.

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": {
      "canvas_id": "<canvas_id>"
    },
    "name": "gnomad_dataframe_describe"
  }
}

Input Schema

NameRequiredDescriptionDefault
canvas_idYesCanvas ID returned by a prior staging call (gnomad_list_gene_variants or gnomad_search_clinvar).

Output Schema

NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
tablesNoTables staged on the canvas.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / canvas_id / minLength
      Removed value: -1
    • addedInput schema / properties / canvas_id / pattern
      Added value: +"^[A-Za-z0-9_-]{10}$"
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover readOnlyHint and idempotentHint, so the safety profile is known. The description adds value by disclosing the return shape—'one entry per table with its row count and column schema'—and by clarifying the canvas-staging context.

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?

Three short, purposeful sentences. The first sentence front-loads the action and purpose, the second gives the prerequisite, and the third describes the output. 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?

For a single-parameter, read-only tool with an output schema available, the description is complete: it explains what the tool returns, why it is needed for SQL generation, and how to obtain the required canvas_id. Nothing essential is missing.

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 coverage is 100% and the single parameter canvas_id is already described as the ID returned by prior staging calls. The description repeats this provenance without adding extra syntax or format details, so the schema carries the semantic weight.

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: 'List the tables staged on a canvas and their columns.' It also ties this to the downstream action of writing correct SQL for gnomad_dataframe_query, which distinguishes it from the query and staging siblings.

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 usage context: call it after gnomad_list_gene_variants or gnomad_search_clinvar, and use the returned canvas_id before issuing gnomad_dataframe_query. It does not explicitly state when not to use it, but the intended workflow is unambiguous.

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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