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grp-docx-mcp

grp-docx-mcp

by Arvindh95Censof
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_statistics

Retrieve comprehensive statistics for the current open document, including word, character, paragraph, table, image, and section counts.

Instructions

Return document statistics for the open document.

Returns a dict with keys: word_count, character_count, paragraph_count, table_count, image_count, section_count.

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_statistics"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.4

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return format and keys, but omits potential performance implications or limitations (e.g., behavior with large documents). Adequate but not rich.

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?

The description is extremely concise: two sentences, the first explaining purpose and the second listing the output keys. No wasted words, front-loaded with the action.

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 zero-parameter, read-only tool with an implicit output schema provided in the description, it is complete. The agent has all necessary information to invoke and interpret the result.

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?

With zero parameters and 100% schema coverage, the description does not need to add parameter details. The baseline of 4 applies as no compensation required.

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 uses a specific verb ('Return') and resource ('document statistics'), and lists the exact keys in the returned dict, making the purpose crystal clear. It distinguishes itself from siblings like 'get_word_count' by being comprehensive.

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 states when to use (to get statistics) but does not explicitly mention alternatives or when not to use it. Given many sibling tools, implicit guidance could be better, but the use case is clear.

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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