semvec/pypi-packages huggingface dataset:
iflow-mcp_mcp-llms-txt 0.2.0
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Requires Python: >=3.11

# mcp-llms-txt

[![smithery badge](https://smithery.ai/badge/@SecretiveShell/MCP-llms-txt)](https://smithery.ai/server/@SecretiveShell/MCP-llms-txt)

MCP server for [Awesome-llms-txt](https://github.com/SecretiveShell/Awesome-llms-txt). Add documentation directly into your conversation via mcp resources.

<a href="https://glama.ai/mcp/servers/kqwhhpe8l7"><img width="380" height="200" src="https://glama.ai/mcp/servers/kqwhhpe8l7/badge" alt="MCP-llms-txt MCP server" /></a>

## Installation

View a setup guide + example usage on [pulsemcp.com](https://www.pulsemcp.com/use-cases/utilize-llm-txt-files/secretiveshell-claude-llmstxt)

### Installing via Smithery

To install MCP Server for Awesome-llms-txt for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@SecretiveShell/MCP-llms-txt):

```bash
npx -y @smithery/cli install @SecretiveShell/MCP-llms-txt --client claude
```

### Manual Installation
Setup your claude config like this:

```json
{
    "mcpServers": {
        "mcp-llms-txt": {
            "command": "uvx",
            "args": ["mcp-llms-txt"],
            "env": {
                "PYTHONUTF8": "1"
            }
        }
    }
}
```

## testing

Use [mcp-cli](https://github.com/wong2/mcp-cli) to test the server:

```bash
npx -y "@wong2/mcp-cli" -c config.json
```

The config file is already setup and does not need to be changed since there are no api keys or secrets.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License.
baytos-claro 0.0.1
Python SDK for Claro by Bayt OS
Requires Python: >=3.8
Keywords: ai, llm, prompts, sdk, baytos, claro

# Claro Python SDK

Official Python SDK for **Claro**, part of [Bayt OS](https://baytos.ai) - The Collaborative Intelligence platform.

## 📚 Documentation

**Complete documentation is available at [docs.baytos.ai](https://docs.baytos.ai)**

## Quick Install

```bash
pip install baytos-claro
```

## Quick Example

```python
from baytos.claro import BaytClient
import os

# Initialize client
client = BaytClient(api_key=os.getenv("BAYT_API_KEY"))

# Get a prompt
prompt = client.get_prompt("@workspace/my-prompt:v1")

# Use it with your LLM
print(prompt.generator)
```

## Get Started

1. **[Get your API key](https://claro.baytos.ai)** from Claro
2. **[Read the quickstart](https://docs.baytos.ai/quickstart)** guide
3. **[Explore examples](https://docs.baytos.ai/examples/basic-usage)**

## Documentation Links

- **[Installation Guide](https://docs.baytos.ai/sdk/python/installation)** - Setup and requirements
- **[Quickstart](https://docs.baytos.ai/sdk/python/quickstart)** - Your first API call
- **[Working with Prompts](https://docs.baytos.ai/sdk/python/prompts)** - Core concepts
- **[API Reference](https://docs.baytos.ai/sdk/python/api-reference)** - Complete SDK reference
- **[Examples](https://docs.baytos.ai/examples/basic-usage)** - Production-ready code
- **[Error Handling](https://docs.baytos.ai/sdk/python/error-handling)** - Robust patterns
- **[Advanced Features](https://docs.baytos.ai/sdk/python/advanced)** - Performance optimization

## Support

- **Documentation:** [docs.baytos.ai](https://docs.baytos.ai)
- **Email:** [support@baytos.ai](mailto:support@baytos.ai)
- **Platform:** [claro.baytos.ai](https://claro.baytos.ai)

## License

Copyright © 2025 Bayt, Inc. All rights reserved.
iflow-mcp_mcp-vcd 0.1.1
A model context protocol (MCP) server for value change dump (VCD) waveforms.
Requires Python: >=3.13

# mcp-vcd

A model context protocol (MCP) server for value change dump (VCD) waveforms.

https://github.com/user-attachments/assets/9d1a6a64-de22-4b5a-a597-685c663c9c79

<a href="https://glama.ai/mcp/servers/kdvs90ijbl"><img width="380" height="200" src="https://glama.ai/mcp/servers/kdvs90ijbl/badge" alt="mcp-vcd MCP server" /></a>

# Tools

- `get-signal`: Provide all changes of the specified signal name to the model's context. This is useful for large waveform files with many signals where you cannot fit the entire VCD file into the model's context window.
  - Can optionally specify a start and end timestamp range for the model to look at.

# Installation

`uv pip install --system mcp-vcd`

And add the following to your `claude_desktop_config.json`:

```json
"mcpServers": {
  "mcp-vcd": {
    "command": "uv",
    "args": [
      "run",
      "mcp-vcd"
    ]
  }
}
```
See [Anthropic's MCP documentation](https://modelcontextprotocol.io/quickstart/user) for more info.