Climate MCP Server

missing_report

Audit data completeness by grouping rows and reporting missing-value counts across specified columns to identify problematic records before analysis.

Instructions

    For any dataset table, groups rows by 'group_by' and reports missing-value
    counts across the specified 'columns'.
    Use before analysis to audit data completeness and find problematic records.
    Example: missing_report('fund.csv', 'fund_type', ['pledge', 'deposit'])
    (filename aliases map to database-backed tables by default.)

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>",
      "group_by": "<group_by>",
      "columns": "<columns>"
    },
    "name": "missing_report"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`columns`	Yes
`filename`	Yes
`group_by`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It does not state whether the tool is read-only, whether it modifies data, or any performance or side-effect implications. This lack of transparency is a significant gap for a tool that likely scans entire tables.

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 concise with three sentences and an example. Important information is front-loaded. It is not overly verbose, though the example could be slightly more polished.

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

Completeness4/5

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

Given that an output schema exists, the description does not need to explain return values. It covers the tool's purpose, usage context, and mentions filename aliases. It is fairly complete for a reporting tool, though it could be improved with a brief note on expected output structure.

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 0%, but the description explains the roles of 'filename', 'group_by', and 'columns' through text and an example. However, the optional 'limit' parameter is not mentioned. The description adds meaning but falls short of full coverage.

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 groups rows by a column and reports missing-value counts for specified columns. It uses specific verbs and identifies the resource (dataset table). It distinguishes itself from siblings like sector_summary or fund_summary by focusing on data completeness auditing.

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 explicitly recommends using the tool 'before analysis to audit data completeness and find problematic records,' providing a clear use context. It does not mention alternatives or when not to use, but the context is adequate for typical usage.

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