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schema

Read-onlyIdempotent

Retrieve table schemas, query templates, analysis thresholds, and critical pitfalls to prevent incorrect query results on rare-variant, gene-based association data from multiple biobanks.

Instructions

The tables, the query templates, and the traps. Read this before querying.

Returns the shipped tables with their columns and row counts, worked queries for the questions people actually ask, the analysis vocabulary (masks, MAF cutoffs, tests, significance thresholds), and a list of ways a correct-looking query gives a wrong answer on this data. That list is not boilerplate: it covers effect sizes that belong to a different test than the p-value beside them, a mask that is a calibration control rather than a biological category, ancestry strata that overlap, and p-values of exactly zero that mean the most significant result rather than a missing one.

Returns: tables, columns, recipes, vocabulary, thresholds and pitfalls.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "schema"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 19, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint, which signal safety and non-mutating behavior. The description adds significant behavioral value beyond these: it details specific pitfalls (e.g., effect sizes mismatched to p-values, overlapping ancestry strata, p-values of exactly zero) that would otherwise be opaque. It also describes the return structure comprehensively.

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

Conciseness4/5

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

The description is moderately concise but slightly verbose in the second paragraph (listing all pitfalls could be slightly tighter). However, it front-loads the critical usage instruction ('Read this before querying') and the return list. Every sentence adds value and earns its place by providing context not in structured fields.

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 has 0 parameters, no nested objects, and a likely output schema (mentioned but not provided here), the description is remarkably complete. It covers purpose, usage timing, return structure, and even specific pitfalls. The contextual signals (parameter count 0, schema coverage 100%) confirm no gaps need filling.

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 tool has 0 parameters and schema description coverage is 100% (though empty schema). The description adds no parameter details because there are none, but this is appropriate and complete for a parameterless tool. No further explanation needed.

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 returns 'tables, columns, recipes, vocabulary, thresholds and pitfalls' – a specific resource (schema/metadata) with a distinct verb 'returns'. It distinguishes itself from siblings like 'query' and 'variants' by describing this as a metadata discovery tool rather than a data retrieval tool.

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

Usage Guidelines5/5

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

The description explicitly says 'Read this before querying,' setting a clear usage context: this should be called before using sibling tools (e.g., 'query') to understand the data structure, pitfalls, and analysis vocabulary. It implies when-not-to-use by framing itself as preparatory, not transactional.

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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