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grp-docx-mcp

grp-docx-mcp

by Arvindh95Censof
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

validate_template_data

Validate that data covers all template fields, returning missing and extra keys.

Instructions

Validate data dict covers all template fields. Returns missing and extra keys.

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": {
      "data": "<data>"
    },
    "name": "validate_template_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.4

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the tool validates coverage and returns missing/extra keys. No side effects are mentioned, but for a read-only validation tool, this is sufficient. It does not contradict annotations (none provided).

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 (12 words) with no wasted text. Information is front-loaded and every word earns its place.

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 tool with an output schema (implied by 'returns missing and extra keys'), the description is complete. It covers purpose, behavior, and output without unnecessary detail.

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?

Schema has 0% description coverage with only a generic 'data' object. The description adds meaning by calling it a 'data dict' and explaining the operation (validating against template fields). This clarifies the parameter's purpose beyond the schema alone.

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 'Validate data dict covers all template fields' clearly states the action (validate) and the resource (data dict vs template fields). It distinguishes from sibling validation tools like validate_endnotes by focusing on template field coverage.

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

Usage Guidelines3/5

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

The description implies usage when checking if data covers template fields, but does not explicitly state when to use this tool over alternatives or provide prerequisites. Context is adequate but not explicit.

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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