open-index/open-npm huggingface dataset:
npm package: @mseep/cosense-mcp-server
Description: A MCP server for cosense
Latest version: 0.2.1

# Cosense MCP Server

A MCP server for [Cosense](https://cosen.se).

## Tools

The following tools are available for interacting with Cosense pages:

- `get_page`: Retrieves a page with the specified title
- `list_pages`: Lists available pages in the resources
- `search_pages`: Searches for pages containing the specified query string
- `insert_lines`: Inserts text after a specified line in a page

## MCP Client Configuration

The following environment variables are required:

- `COSENSE_PROJECT_NAME`: Project name
- `COSENSE_SID`: Session ID for authentication
  - Required for writing to pages and reading private pages
  - Handle with care as it contains sensitive information
  - For more details, see [scrapboxlab/connect.sid](https://scrapbox.io/scrapboxlab/connect.sid)

### Run from npm registry

#### JSR registry configuration

This package depends on [@cosense/std](https://jsr.io/@cosense/std) and [@cosense/types](https://jsr.io/@cosense/types) which are hosted on JSR. Before using npx, you need to configure the JSR registry globally:

For Linux/macOS:

```bash
echo "@jsr:registry=https://npm.jsr.io" >> ~/.npmrc
```

For Windows (PowerShell):

```powershell
echo "@jsr:registry=https://npm.jsr.io" >> $env:USERPROFILE\.npmrc
```

Or if you prefer not to modify global settings, run from source instead (see the section below)

#### Client json configuration

After configuring JSR registry, configure your MCP client:

```json
{
  "mcpServers": {
    "cosense-mcp-server": {
      "command": "npx",
      "args": ["-y", "@yosider/cosense-mcp-server"],
      "env": {
        "COSENSE_PROJECT_NAME": "your_project_name",
        "COSENSE_SID": "your_sid"
      }
    }
  }
}
```

### Run from source

#### Clone and build

```bash
git clone https://github.com/yosider/cosense-mcp-server.git
cd cosense-mcp-server
npm install
npm run build
```

#### Client json configuration

```json
{
  "mcpServers": {
    "cosense-mcp-server": {
      "command": "npx",
      "args": ["-y", "/path/to/cosense-mcp-server"],
      "env": {
        "COSENSE_PROJECT_NAME": "your_project_name",
        "COSENSE_SID": "your_sid"
      }
    }
  }
}
```

### Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is available as a package script:

```bash
npm run inspect
```

The Inspector will provide a URL to access debugging tools in your browser.

## Acknowledgments

This project is forked from [funwarioisii/cosense-mcp-server](https://github.com/funwarioisii/cosense-mcp-server).
npm package: @mseep/datagov-mcp-server
Description: An MCP server for accessing data from Data.gov, providing tools and resources for interacting with government datasets.
Author: Joao Bondan
Latest version: 0.1.0

# Data.gov MCP Server

An MCP server for accessing data from Data.gov, providing tools and resources for interacting with government datasets.

<a href="https://glama.ai/mcp/servers/qi5i2d6sen">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/qi5i2d6sen/badge" alt="Data.gov Server MCP server" />
</a>

## Installation

1.  **Install the package globally:**

    ```bash
    npm install -g @melaodoidao/datagov-mcp-server
    ```

2. **Configure the MCP Server:**

   - Add the following entry to your `cline_mcp_settings.json` file (usually located in `~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/` on macOS):

     ```json
      {
        "mcpServers": {
          "datagov": {
            "command": "datagov-mcp-server",
            "args": [],
            "env": {}
          }
        }
      }
     ```
    - If you are using the Claude Desktop app, add the entry to `~/Library/Application Support/Claude/claude_desktop_config.json` instead.

## Usage

This server provides the following tools:

*   `package_search`: Search for packages (datasets) on Data.gov.
*   `package_show`: Get details for a specific package (dataset).
*   `group_list`: List groups on Data.gov.
*   `tag_list`: List tags on Data.gov.

It also provides the following resource template:

*   `datagov://resource/{url}`: Access a Data.gov resource by its URL.

You can use these tools and resources with Cline by specifying the server name (`datagov-mcp-server`) and the tool/resource name.

## Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

## License

MIT License
npm package: @mseep/dbt-semantic-layer-mcp-server
Description: MCP server for using the DBT Semantic Layer API
Author: TommyBez
Latest version: 0.1.3

# dbt Semantic Layer MCP Server

[![smithery badge](https://smithery.ai/badge/@TommyBez/dbt-semantic-layer-mcp)](https://smithery.ai/server/@TommyBez/dbt-semantic-layer-mcp)

A Model-Connector-Presenter (MCP) server for seamlessly querying the dbt Semantic Layer through Claude Desktop and other compatible AI assistants.

## What is the dbt Semantic Layer?

The [dbt Semantic Layer](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl) is a powerful feature that allows you to define metrics once in your dbt project and reuse them consistently across your entire data stack. It provides:

- A single source of truth for business metrics
- Consistent metric definitions across all data tools
- Simplified access to complex metrics for all team members

## About This Project

This MCP server acts as a bridge between AI assistants (like Claude) and the dbt Semantic Layer, enabling you to:

- Query metrics directly through natural language conversations
- Explore available metrics and their definitions
- Analyze data with dimensional breakdowns and filters
- Visualize results within your AI assistant interface

## Features

- 🔍 **Metric Discovery**: Browse and search available metrics in your dbt Semantic Layer
- 📊 **Query Creation**: Generate and execute semantic queries through natural language
- 🧮 **Data Analysis**: Filter, group, and order metrics for deeper insights
- 📈 **Result Visualization**: Display query results in an easy-to-understand format

## Prerequisites

- A dbt Cloud account with Semantic Layer enabled
- API access to your dbt Cloud instance
- Node.js (v14 or later)

## Installation

### Via Smithery (Recommended)

The easiest way to install is via [Smithery](https://smithery.ai/server/@TommyBez/dbt-semantic-layer-mcp):

```bash
npx -y @smithery/cli install @TommyBez/dbt-semantic-layer-mcp --client claude
```

## Usage

Once installed and configured, you can interact with the dbt Semantic Layer directly from Claude Desktop:

1. Ask about available metrics: "What metrics are available in my dbt Semantic Layer?"
2. Query specific metrics: "Show me monthly revenue for the last quarter grouped by product category"
3. Analyze trends: "What's the week-over-week growth in user signups?"

## Troubleshooting

If you encounter issues:

- Verify your API credentials are correct
- Ensure your dbt Cloud project has Semantic Layer enabled
- Check that your metrics are properly defined in your dbt project

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

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

## Acknowledgments

- [dbt Labs](https://www.getdbt.com/) for creating the dbt Semantic Layer
- [Smithery](https://smithery.ai/) for the MCP deployment platform
- [LiteMCP](https://github.com/wong2/litemcp) for the MCP development package
npm package: @mseep/deepclaude-mcp
Description: DeepClaude MCP Server - Combining DeepSeek and Claude for better results
Author: 你的名字
Latest version: 1.0.1

# DeepClaude MCP (Model Control Panel)

[English](./README.md) | [中文](./README_CN.md)

## Overview
DeepClaude MCP (Model Control Panel) is a powerful server application that combines the capabilities of DeepSeek and Claude AI models to deliver enhanced results. This integration allows for more accurate and comprehensive AI responses by leveraging the strengths of both models.

## Features
- Seamless integration of DeepSeek and Claude AI models
- RESTful API endpoints for easy integration
- Configurable model parameters
- Robust error handling with Winston logger
- Cross-origin resource sharing (CORS) support
- TypeScript implementation for type safety

## Prerequisites
- Node.js (v14 or higher)
- npm or yarn package manager
- API keys for both DeepSeek and Claude services

## Installation
1. Clone the repository:
```bash
git clone https://github.com/Mrdapeng/DeepClaudeMcp.git
cd DeepClaudeMcp
```

2. Install dependencies:
```bash
yarn install
# or
npm install
```

3. Configure environment variables:
```bash
cp .env.example .env
# Edit .env file with your API keys and configuration
```

## Development
- Start development server with hot-reload:
```bash
yarn dev
# or
npm run dev
```

- Build the project:
```bash
yarn build
# or
npm run build
```

- Start production server:
```bash
yarn start
# or
npm start
```

## Project Structure
```
src/
├── config/     # Configuration files
├── services/   # Core services and API clients
├── types/      # TypeScript type definitions
└── index.ts    # Application entry point
```

## Dependencies
- Express.js - Web framework
- Axios - HTTP client
- Winston - Logging library
- CORS - Cross-origin resource sharing
- dotenv - Environment configuration
- TypeScript - Programming language
- Jest - Testing framework

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