Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Word MCP generate a project status report with last week's metrics and next steps"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server for generating Microsoft Word documents (.docx) programmatically. Unlike typical MCP servers that act as gateways to APIs, this server acts as a Factory, converting AI-generated text and data into professional, downloadable files.
generate_report: Create complete Word documents in one shot
Markdown Support: Automatically converts basic Markdown (bold, lists) into Word formatting
Rich Elements: Supports:
Headers (Levels 1-3)
Data Tables with custom headers
Text Paragraphs
File metadata (Titles, Authors)
Local File Output: Saves files directly to your host machine
Dockerized Factory: Runs securely in a container with volume mapping
Stateless Operation: No complex databases required
Related MCP server: Word MCP Server
Install dependencies:
npm installCreate a .env file (Optional, defaults to ./output):
OUTPUT_DIR=./generated_reportsBuild and start:
npm run build
npm startCritical Note: Because this server creates files, you must mount a volume to see the output.
Build the image:
docker build -t word-mcp .Run with Volume Mapping:
docker run --rm -i \
-v $(pwd)/generated_reports:/app/output \
word-mcpTo allow the AI to save files to your Windows "Documents" folder, you must map the volume in the configuration.
Open your config file:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Add this configuration:
{
"mcpServers": {
"word-mcp": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"-v", "C:\\Users\\hp\\Documents\\mcp\\word-mcp\\generated_reports:/app/output",
"word-mcp"
]
}
}
}Note: Update the path C:\\Users\\hp... to match your actual project location.
If you prefer docker-compose, use the included configuration:
# docker-compose.yml
services:
word-mcp:
build: .
volumes:
- ./generated_reports:/app/outputThe AI can call the tool with structured data to create a formatted report.
{
"filename": "Audit_Report_2024",
"title": "Q4 Security Audit",
"sections": [
{
"heading": "Executive Summary",
"content": "The audit was completed on **January 20th**. No critical vulnerabilities were found."
},
{
"heading": "Vulnerability Matrix",
"table": {
"headers": ["Severity", "Count", "Status"],
"rows": [
["High", "0", "Pass"],
["Medium", "2", "Investigating"]
]
}
}
]
}Check Volume Mapping: Ensure your claude_desktop_config.json has the -v flag pointing to a valid folder on your host machine.
Docker Permissions: The container runs as a non-root user (appuser). Ensure your host folder allows writing (usually automatic on Windows, but requires chmod on Linux).
The server attempts to create the directory on startup. If using Docker, ensure the internal path /app/output is correctly mapped.
Currently, the Markdown parser supports bold (**text**) and basic paragraph splitting. Complex Markdown (like code blocks or nested lists) will be rendered as plain text in this version.
Run in development mode:
npm run devWatch for changes:
npm run watchGenerates a complete Word document based on a structured content payload.
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | Filename for the generated report (e.g. 'audit_report') | |
| title | Yes | Title of the document | |
| sections | Yes | List of sections to include in the report |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the tool 'Generates a complete Word document' which implies a write/create operation, but doesn't disclose behavioral traits like file storage location, permissions needed, whether it overwrites existing files, or error handling. For a document generation tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function. It's front-loaded with the core action and contains no unnecessary words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a document generation tool with no annotations, no output schema, and 3 required parameters, the description is incomplete. It doesn't explain what 'complete' means, where the document is saved, what format it returns, or any error conditions. The 100% schema coverage helps with parameters, but overall context is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 3 parameters. The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generates a complete Word document based on a structured content payload.' It specifies the verb ('Generates'), resource ('Word document'), and input type ('structured content payload'). However, without sibling tools to differentiate from, it cannot achieve a perfect 5 for sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, constraints, or typical use cases. With no sibling tools listed, there's no explicit comparison, but it still lacks basic usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0generate_reportWith only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it against. The tool's purpose is clearly defined and distinct by default.
Since there is only a single tool, naming consistency is inherently perfect—there are no other tool names to be inconsistent with. The tool name 'generate_report' follows a clear verb_noun pattern.
A single tool is too few for a server named 'Word MCP', which implies broader document manipulation capabilities beyond just report generation. This minimal toolset feels thin and under-scoped for the apparent domain.
The tool surface is severely incomplete for a Word document server; it only covers report generation, missing essential operations like document editing, formatting, saving, or loading. This will likely cause agent failures when broader document tasks are needed.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Create real Word .docx files from your AI chat: proposals, quotes, contracts, statements of work.
Use your own Word templates to convert Markdown → DOCX/PDF/HTML from any MCP-compatible AI.
Clean, repair, and convert AI-generated Markdown to HTML/PDF/DOCX/PNG; save and share documents.
Real .docx and .xlsx files from structured data, with automatic Hebrew/Arabic RTL.
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