Skip to main content
Glama
enesjakozh
Sunwood-ai-labs

DocuMind MCP Server

by Sunwood-ai-labs
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

🌐 DocuMind MCP Server

"Where Documentation Meets Digital Intelligence"

A next-generation Model Context Protocol (MCP) server that revolutionizes documentation quality analysis through advanced neural processing.

⚡ Core Systems

  • 🧠 Neural Documentation Analysis: Advanced algorithms for comprehensive README evaluation

  • 🔮 Holographic Header Scanning: Cutting-edge SVG analysis for visual elements

  • 🌍 Multi-dimensional Language Support: Cross-linguistic documentation verification

  • 💫 Quantum Suggestion Engine: AI-powered improvement recommendations

Related MCP server: Lucidity MCP

🚀 System Boot Sequence

System Requirements

  • Node.js 18+

  • npm || yarn

Initialize Core

npm install

Compile Matrix

npm run build

Neural Development Link

Establish real-time neural connection:

npm run watch

🛸 Operation Protocol

System Configuration

Integrate with Claude Desktop mainframe:

Windows Terminal:

// %APPDATA%/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "documind-mcp-server": {
      "command": "/path/to/documind-mcp-server/build/index.js"
    }
  }
}

Neural Interface Commands

evaluate_readme

Initiates quantum analysis of documentation structure.

Parameters:

  • projectPath: Neural pathway to target directory

Example Request:

{
  name: "evaluate_readme",
  arguments: {
    projectPath: "/path/to/project"
  }
}

Example Response:

{
  content: [
    {
      type: "text",
      text: JSON.stringify({
        filePath: "/path/to/project/README.md",
        hasHeaderImage: true,
        headerImageQuality: {
          hasGradient: true,
          hasAnimation: true,
          // ... other quality metrics
        },
        score: 95,
        suggestions: [
          "Consider adding language badges",
          // ... other suggestions
        ]
      })
    }
  ]
}

🔮 Development Matrix

Debug Protocol

Access the neural network through MCP Inspector:

npm run inspector

Troubleshooting Guide

Common Issues and Solutions

  1. Header Image Not Detected

    • Ensure SVG file is placed in the assets/ directory

    • Validate SVG file contains proper XML structure

    • Check file permissions

  2. Language Badges Not Recognized

    • Verify badges use shields.io format

    • Check HTML structure follows recommended pattern

    • Ensure proper center alignment

  3. Build Errors

    • Clear node_modules and reinstall dependencies

    • Ensure TypeScript version matches project requirements

    • Check for syntax errors in modified files

  4. MCP Connection Issues

    • Verify stdio transport configuration

    • Check Claude Desktop configuration

    • Ensure proper file paths in config

Performance Optimization

  1. SVG Analysis

    • Minimize SVG complexity for faster parsing

    • Use efficient gradients and animations

    • Optimize file size while maintaining quality

  2. README Scanning

    • Structure content for optimal parsing

    • Use recommended markdown patterns

    • Follow badge placement guidelines

🔬 API Documentation

Core Classes

ReadmeService

Primary service for README analysis and evaluation.

class ReadmeService {
  // Analyzes all README files in a project
  async evaluateAllReadmes(projectPath: string): Promise<ReadmeEvaluation[]>
  
  // Evaluates a single README file
  private async evaluateReadme(dirPath: string, readmePath: string): Promise<ReadmeEvaluation>
  
  // Evaluates language badge configuration
  private evaluateLanguageBadges(content: string): BadgeEvaluation
}

SVGService

Specialized service for SVG header image analysis.

class SVGService {
  // Evaluates SVG header image quality
  public evaluateHeaderImageQuality(imgSrc: string, content: string): HeaderImageQuality
  
  // Checks for project-specific elements in SVG
  private checkProjectSpecificImage(svgContent: string, readmeContent: string): boolean
}

Core Interfaces

interface ReadmeEvaluation {
  filePath: string;
  hasHeaderImage: boolean;
  headerImageQuality: HeaderImageQuality;
  isCentered: {
    headerImage: boolean;
    title: boolean;
    badges: boolean;
  };
  hasBadges: {
    english: boolean;
    japanese: boolean;
    isCentered: boolean;
    hasCorrectFormat: boolean;
  };
  score: number;
  suggestions: string[];
}

interface HeaderImageQuality {
  hasGradient: boolean;
  hasAnimation: boolean;
  hasRoundedCorners: boolean;
  hasEnglishText: boolean;
  isProjectSpecific: boolean;
}

Error Handling

The server implements comprehensive error handling:

try {
  const evaluations = await readmeService.evaluateAllReadmes(projectPath);
  // Process results
} catch (error) {
  const errorMessage = error instanceof Error ? error.message : String(error);
  return {
    content: [{
      type: 'text',
      text: `Evaluation error: ${errorMessage}`
    }],
    isError: true
  };
}

⚡ License

Operating under MIT Protocol.

Available Tools

1 tool
evaluate_readmeC

リポジトリ内の全てのREADMEファイルの構成を評価し、改善点を提案します

ParametersJSON Schema
NameRequiredDescriptionDefault
projectPathYesプロジェクトのルートディレクトリパス

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions evaluation and proposal of improvements but does not specify how the evaluation is performed (e.g., criteria, depth), what format the proposals take, whether it modifies files or only reports, or any constraints like rate limits or permissions needed. This leaves significant gaps in understanding the tool's behavior.

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 a single, efficient sentence in Japanese that directly states the tool's function without unnecessary words. It is front-loaded with the core action and outcome, making it easy to parse. This minimal structure earns a top score for conciseness.

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

Completeness2/5

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

Given the tool's complexity (evaluating and proposing improvements for README files) and the absence of annotations and output schema, the description is insufficient. It lacks details on evaluation criteria, output format, behavioral traits, and usage context. Without this information, an AI agent would struggle to understand the full scope and limitations of the tool.

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?

The input schema has 100% description coverage, with the single parameter 'projectPath' clearly documented as 'プロジェクトのルートディレクトリパス' (project root directory path). The description does not add any additional meaning or context beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'evaluate the structure of all README files in a repository and propose improvements.' It specifies the verb ('evaluate'), resource ('README files'), and outcome ('propose improvements'), making the intent unambiguous. However, since there are no sibling tools, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.

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

Usage Guidelines2/5

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, prerequisites, or exclusions. It simply states what the tool does without context for its application. This lack of usage instructions limits its effectiveness for an AI agent in selecting the right tool for a scenario.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedevaluate_readme
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, clear purpose of evaluating README files in repositories.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'evaluate_readme' follows a clear verb_noun pattern.

Tool Count2/5

One tool is too few for a server named 'DocuMind MCP Server', which suggests a broader documentation-related purpose. This feels thin and limited in scope, lacking coverage for other documentation tasks.

Completeness1/5

The server is severely incomplete for its implied domain of documentation management. It only handles README evaluation, missing obvious gaps like creating, updating, or analyzing other documentation types, or managing documentation workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

Appeared in Searches

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Sunwood-ai-labs/documind-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server