MCP Advisor

★ 89

from istarwyh

A discovery and recommendation service for exploring MCP servers using natural language queries.

🔥🔥🔥✓ VerifiedFreeAdvanced setup

#automation #analytics #database #git #memory

MCP Advisor

English | 简体中文

Introduction

MCP Advisor is a discovery and recommendation service that helps AI assistants explore Model Context Protocol (MCP) servers using natural language queries. It makes it easier for users to find and leverage MCP tools suitable for specific tasks.

User Stories

Discover & Recommend MCP Servers
- As an AI agent developer, I want to quickly find the right MCP servers for a specific task using natural-language queries.
- Example prompt: "Find MCP servers for insurance risk analysis"
Install & Configure MCP Servers
- As a regular user who discovers a useful MCP server, I want to install and start using it as quickly as possible.
- Example prompt: "Install this MCP: https://github.com/Deepractice/PromptX"

Demo

https://github.com/user-attachments/assets/7a536315-e316-4978-8e5a-e8f417169eb1

Usage

Once configured, the Nacos provider will be automatically enabled and used when searching for MCP servers. You can query it using natural language, for example:

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Find MCP servers for insurance risk analysis

Or more specifically:

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Search for MCP servers with natural language processing capabilities

Documentation Navigation

Quick Start Guide - Installation, configuration, and basic usage
Technical Reference - Advanced features and search providers
Contributing Guide - Development setup and contribution guidelines
Architecture Documentation - System architecture details
Troubleshooting - Common issues and solutions
Roadmap - Future development plans

Developer Guide

Architecture Overview

MCP Advisor adopts a modular architecture with clean separation of concerns and functional programming principles. The codebase has been recently refactored (2025) to improve maintainability and scalability:

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graph TD
    Client["Client Application"] --> |"MCP Protocol"| Transport["Transport Layer"]
    
    subgraph "MCP Advisor Server"
        Transport --> |"Request"| SearchService["Search Service"]
        SearchService --> |"Query"| Providers["Search Providers"]
        
        subgraph "Search Providers"
            Providers --> MeilisearchProvider["Meilisearch Provider"]
            Providers --> GetMcpProvider["GetMCP Provider"]
            Providers --> CompassProvider["Compass Provider"]
            Providers --> NacosProvider["Nacos Provider"]
            Providers --> OfflineProvider["Offline Provider"]
        end
        
        OfflineProvider --> |"Hybrid Search"| HybridSearch["Hybrid Search Engine"]
        HybridSearch --> TextMatching["Text Matching"]
        HybridSearch --> VectorSearch["Vector Search"]
        
        SearchService --> |"Merge & Filter"| ResultProcessor["Result Processor"]
        
        SearchService --> Logger["Logging System"]
    end

Project Structure

The codebase follows clean architecture principles with organized directory structure:

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src/
├── services/
│   ├── core/                    # Core business logic
│   │   ├── installation/        # Installation guide services
│   │   ├── search/             # Search providers
│   │   └── server/             # MCP server implementation
│   ├── providers/              # External service providers
│   │   ├── meilisearch/        # Meilisearch integration
│   │   ├── nacos/              # Nacos service discovery
│   │   ├── oceanbase/          # OceanBase vector database
│   │   └── offline/            # Offline search engine
│   ├── common/                 # Shared utilities
│   │   ├── api/                # API clients
│   │   ├── cache/              # Caching mechanisms
│   │   └── vector/             # Vector operations
│   └── interfaces/             # Type definitions
├── types/                      # TypeScript type definitions
├── utils/                      # Utility functions
└── tests/                      # Test suites
    ├── unit/                   # Unit tests
    ├── integration/            # Integration tests
    └── e2e/                    # End-to-end tests

Core Components

Search Service Layer
- Unified search interface and provider aggregation
- Support for multiple search providers executing in parallel
- Configurable search options (limit, minSimilarity)
Search Providers
- Meilisearch Provider: Vector search using Meilisearch
- GetMCP Provider: API search from the GetMCP registry
- Compass Provider: API search from the Compass registry
- Nacos Provider: Service discovery integration
- Offline Provider: Hybrid search combining text and vectors
Hybrid Search Strategy
- Intelligent combination of text matching and vector search
- Configurable weight balancing
- Smart adaptive filtering mechanisms
Transport Layer
- Stdio (CLI default)
- SSE (Web integration)
- REST API endpoints

For more detailed architecture documentation, see ARCHITECTURE.md.

Contribution Guidelines

We welcome contributions to MCP Advisor!

Search Providers

MCP Advisor supports multiple search providers that can be used simultaneously:

Compass Search Provider: Retrieves MCP server information using the Compass API
GetMCP Search Provider: Uses the GetMCP API and vector search for semantic matching
Meilisearch Search Provider: Uses Meilisearch for fast, fault-tolerant text search

For detailed information about search providers, see Technical Reference.

Roadmap

MCP Advisor is evolving from a simple recommendation system to an intelligent agent orchestration platform. Our vision is to create a system that not only recommends the right MCP servers but also learns from interactions and helps agents dynamically plan and execute complex tasks.

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gantt
    title MCP Advisor Evolution Roadmap
    dateFormat  YYYY-MM-DD
    axisFormat  %Y-%m
    
    section Foundation
    Enhanced Search & Recommendation ✓       :done, 2025-01-01, 90d
    Hybrid Search Engine ✓                   :done, 2025-01-01, 90d
    Provider Priority System ✓               :done, 2025-04-01, 60d
    
    section Intelligence Layer
    Feedback Collection System               :active, 2025-04-01, 90d
    Agent Interaction Analytics             :2025-07-01, 120d
    Usage Pattern Recognition               :2025-07-01, 90d
    
    section Learning Systems
    Reinforcement Learning Framework         :2025-10-01, 180d
    Contextual Bandit Implementation         :2025-10-01, 120d
    Multi-Agent Reward Modeling             :2026-01-01, 90d
    
    section Advanced Features
    Task Decomposition Engine               :2026-01-01, 120d
    Dynamic Planning System                 :2026-04-01, 150d
    Adaptive MCP Orchestration              :2026-04-01, 120d
    
    section Ecosystem
    Developer SDK & API                     :2026-07-01, 90d
    Custom MCP Training Tools               :2026-07-01, 120d
    Enterprise Integration Framework        :2026-10-01, 150d

Major Development Phases

Recommendation Capability Optimization (2025 Q2-Q3)
- Accept user feedback
- Refine recommendation effectiveness
- Introduce more indices

For a detailed roadmap, see ROADMAP.md.

To Implement the above features, we need to:

Support Full-Text Index Search
Utilize Professional Rerank Module like https://github.com/PrithivirajDamodaran/FlashRank or Qwen Rerank Model
Support Cline marketplace: https://api.cline.bot/v1/mcp/marketplace

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copy & paste — that's it

npx -y @smithery/cli install @istarwyh/mcpadvisor --client claude

Quick Start

Installation

The fastest way is to integrate MCP Advisor through MCP configuration:

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{
  "mcpServers": {
    "mcpadvisor": {
      "command": "npx",
      "args": ["-y", "@xiaohui-wang/mcpadvisor"]
    }
  }
}

Add this configuration to your AI assistant's MCP settings file:

MacOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %AppData%\Claude\claude_desktop_config.json

Installing via Smithery

To install Advisor for Claude Desktop automatically via Smithery:

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npx -y @smithery/cli install @istarwyh/mcpadvisor --client claude

For more installation methods and detailed configuration, see the Quick Start Guide.

Optional: Local Meilisearch (improves recommendations)

To boost recommendation quality, you can run a local Meilisearch instance:

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pnpm meilisearch:start

This starts Meilisearch at http://localhost:7700, bootstraps the mcp_servers index from local data, and persists environment variables to ~/.meilisearch/env. Load them in your current shell with:

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source ~/.meilisearch/env

Or enable it automatically with a single flag when launching MCPAdvisor (no manual env needed):

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{
  "mcpServers": {
    "mcpadvisor": {
      "command": "npx",
      "args": ["-y", "@xiaohui-wang/mcpadvisor", "--local-meilisearch"]
    }
  }
}

Developer Quick Start

Development Environment Setup

Clone the repository
Install dependencies:
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```
pnpm install
```
Build the project:
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```
pnpm run build
```
Configure environment variables (see Quick Start Guide)

Testing

MCP Advisor includes comprehensive testing suites to ensure code quality and functionality. For detailed testing information including unit tests, integration tests, end-to-end testing, and manual testing procedures, see the Technical Reference.

Testing

Run comprehensive tests:

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# Run all tests
pnpm run check && pnpm run test && pnpm run test:e2e

# Automated E2E testing script
./scripts/run-e2e-test.sh

For detailed testing information, see Technical Reference.

Library Usage

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import { SearchService } from '@xiaohui-wang/mcpadvisor';

// Initialize search service
const searchService = new SearchService();

// Search for MCP servers
const results = await searchService.search('vector database integration');
console.log(results);

Transport Options

MCP Advisor supports multiple transport methods:

Stdio Transport (default) - Suitable for command-line tools
SSE Transport - Suitable for web integration
REST Transport - Provides REST API endpoints

For more development details, see Contributing Guide.

Usage Examples

Example Queries

Here are some example queries you can use with MCP Advisor:

Copy & paste — that's it

"Find MCP servers for natural language processing"
"Document summarization MCP servers"

Example Response

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[
  {
    "title": "NLP Toolkit",
    "description": "Comprehensive natural language processing toolkit with sentiment analysis, entity recognition, and text summarization capabilities.",
    "github_url": "https://github.com/example/nlp-toolkit",
    "similarity": 0.92
  },
  {
    "title": "Text Processor",
    "description": "Efficient text processing MCP server with multi-language support.",
    "github_url": "https://github.com/example/text-processor",
    "similarity": 0.85
  }
]

For more examples and advanced usage, see Technical Reference.

MCP Advisor

Introduction

User Stories

Demo

Usage

Documentation Navigation

Developer Guide

Architecture Overview

Project Structure

Core Components

Contribution Guidelines

Search Providers

Roadmap

Major Development Phases

License

Quick Start

Installation

Installing via Smithery

Optional: Local Meilisearch (improves recommendations)

Developer Quick Start

Development Environment Setup

Testing

Testing

Library Usage

Transport Options

Usage Examples

Example Queries

Example Response

Troubleshooting

Common Issues