Climate MCP Server

get_schema_contract_summary

Retrieve a machine-readable JSON schema contract with layers, keys, grain, and cross-layer rules for formal governance in cross-dataset analysis.

Instructions

Machine-readable JSON schema contract for all datasets (layers, keys, grain, cross-layer rules).

Use for cross-dataset analysis when you need formal governance rules. Avoid as the first call for simple single-table questions when column names are already provided to the model — the payload is large.

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": "get_schema_contract_summary"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

A4.5/5.0

Behavior3/5

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

No annotations are provided, so the description must carry the burden. It discloses that the payload is large, implying performance considerations. However, it does not explicitly state whether the operation is read-only, requires authentication, or other behavioral traits. More detail on safety or side effects would improve transparency.

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 sentences, each with clear purpose: first defines what the tool returns, second gives when-to-use, third gives when-to-avoid. No wasted words, and essential information is front-loaded.

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 zero parameters and an existing output schema, the description covers the tool's purpose, usage context, and a behavioral caveat (large payload). It is complete enough for an agent to decide when to invoke this tool appropriately.

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?

There are zero parameters, and the schema coverage is 100% (trivially). According to guidelines, no parameters yields a baseline of 4. The description does not need to add parameter information because there are none.

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 a machine-readable JSON schema contract for all datasets, specifying layers, keys, grain, and cross-layer rules. It distinguishes itself from single-table queries and implies it is a summary across datasets, differentiating it from the sibling 'get_schema_contract'.

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?

Explicitly says when to use ('cross-dataset analysis, formal governance rules') and when to avoid ('first call for simple single-table questions when column names are already provided'). This provides clear guidance on appropriate context and alternatives.

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