# 🦆 MCP Gosling - Document Processor for Goose
[](https://badge.fury.io/py/mcp-gosling)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
> **Advanced document processing extension for Goose AI with enterprise-grade offline fallback**
A powerful [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that provides advanced document processing capabilities for [Goose](https://block.github.io/goose/). Process PDFs, DOCX, PPTX, images, and HTML documents with high fidelity using [IBM's Docling library](https://github.com/DS4SD/docling), with intelligent fallback to offline processing for network-restricted environments.
## ✨ **Key Features**
🔧 **Enterprise-Ready**: SSL certificate fixes for corporate networks
📄 **Multi-Format**: PDF, DOCX, PPTX, images, HTML, and more
🌐 **Offline Capable**: Graceful fallback to PyPDF2 when Hugging Face is blocked
⚡ **High Performance**: Optimized for production workloads
🛡️ **Robust**: Comprehensive error handling and validation
🎯 **AI-Optimized**: Clean Markdown output perfect for AI analysis
## 🚀 **Quick Start**
### Installation Options
#### Option 1: Standard Installation (Recommended)
```bash
pip install mcp-gosling
```
#### Option 2: Using uvx (Modern Tooling)
```bash
# Run directly without installation
uvx mcp-gosling
# Or using uv tool run (identical behavior)
uv tool run mcp-gosling
```
### Configuration for Goose
#### With Standard Installation:
```json
{
"mcpServers": {
"gosling": {
"command": "mcp-gosling",
"args": []
}
}
}
```
#### With uvx:
```json
{
"mcpServers": {
"gosling": {
"command": "uvx",
"args": ["mcp-gosling"]
}
}
}
```
### Usage
```bash
# Process your AWS certification PDF
goose "Process my AWS certification document at /path/to/cert.pdf"
# Batch process multiple documents
goose "Process all PDFs in my documents folder and summarize them"
# Extract metadata only
goose "What are the metadata details of this document?"
```
## 📋 **Available Tools**
### `process_document`
Process a single document and return clean Markdown content.
**Parameters:**
- `file_path` (string): Path to the document file
- `output_format` (string): "markdown", "json", or "text" (default: "markdown")
- `extract_images` (boolean): Whether to extract and describe images (default: false)
- `extract_tables` (boolean): Whether to extract table structure (default: true)
### `batch_process_documents`
Process multiple documents in batch with optional file output.
**Parameters:**
- `file_paths` (array): List of document file paths (max 20 files)
- `output_format` (string): Output format for all documents (default: "markdown")
- `output_directory` (string): Directory to save files (empty = return content)
### `extract_document_metadata`
Extract detailed metadata and structure information from a document.
**Parameters:**
- `file_path` (string): Path to the document file
## 🔧 **Advanced Features**
### Corporate Network Support
- ✅ SSL certificate fixes for enterprise environments
- ✅ Automatic fallback when Hugging Face Hub is blocked
- ✅ Works behind corporate firewalls and proxies
### Intelligent Processing
- **Primary**: IBM Docling for high-fidelity extraction with OCR and table recognition
- **Fallback**: PyPDF2 for reliable offline PDF processing
- **Formats**: PDF, DOCX, PPTX, PNG, JPG, HTML, TXT, MD, JSON
### Performance & Reliability
- File size limits (50MB for full processing, 5MB for metadata)
- Batch processing (up to 20 files)
- Comprehensive error handling
- Memory-efficient processing
## 🎯 **Use Cases**
- **📑 Document Analysis**: Extract and analyze content from reports, papers, contracts
- **🏢 Enterprise**: Process documents in network-restricted corporate environments
- **🔍 Research**: Batch process academic papers and research documents
- **📊 Data Extraction**: Convert documents to structured data for AI analysis
- **📝 Content Migration**: Bulk convert document formats with preserved structure
## 🛠 **Technical Details**
**Built With:**
- [IBM Docling](https://github.com/DS4SD/docling) - Enterprise-grade document processing
- [PyPDF2](https://pypdf2.readthedocs.io/) - Reliable offline PDF processing
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - Model Context Protocol
**Requirements:**
- Python 3.9+
- Works on macOS, Linux, Windows
- Optional: GPU acceleration for enhanced performance
## 🚀 **Installation Options**
### For Goose Users (Recommended)
1. **Install via pip:**
```bash
pip install mcp-gosling
```
2. **Configure in Goose:**
Add the MCP server to your Goose configuration
3. **Start using:**
```bash
goose "Process this document for me"
```
### For MCP Development
1. **Clone and install:**
```bash
git clone https://github.com/masanderso/goose-docling.git
cd goose-docling
pip install -e .
```
2. **Test with MCP Inspector:**
```bash
mcp dev src/mcp_docling/server.py
```
## 🔍 **Example Outputs**
### Document Processing
```markdown
# Document: AWS Certified Solutions Architect - Associate.pdf
**Source:** /path/to/document.pdf
**Format:** .pdf
**Pages:** 2
---
## Page 1
AWS Certified Solutions Architect - Associate
Notice of Exam Results
Candidate: Matthew Sanderson Exam Date: 12/3/2024
Candidate Score: 779 Pass/Fail: PASS
...
```
### Metadata Extraction
```json
{
"file_info": {
"name": "document.pdf",
"size_mb": 0.03,
"format": ".pdf"
},
"document_structure": {
"page_count": 2,
"has_tables": true,
"has_figures": false
}
}
```
## 🤝 **Contributing**
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request
## 📄 **License**
MIT License - see [LICENSE](LICENSE) file for details.
## 🔗 **Links**
- [Goose AI Assistant](https://block.github.io/goose/)
- [Model Context Protocol](https://modelcontextprotocol.io/)
- [IBM Docling Library](https://github.com/DS4SD/docling)
- [Issue Tracker](https://github.com/masanderso/goose-docling/issues)
## 🏷️ **Tags**
`goose-extension` `document-processing` `mcp-server` `docling` `pdf-processing` `enterprise-ready` `offline-capable` `ai-tools`
## 🚀 Features
- **Multi-format Support**: PDF, DOCX, PPTX, images (PNG, JPG), HTML, and more
- **Intelligent Processing**: OCR, table extraction, and structure preservation
- **Flexible Output**: Markdown, JSON, or plain text formats
- **Batch Processing**: Handle multiple documents efficiently
- **Metadata Extraction**: Detailed document structure and properties
- **Production Ready**: Robust error handling and file size limits
## 📋 Tools Available
This MCP server exposes three main tools:
### 1. `process_document`
Process a single document and return the converted content.
**Parameters:**
- `file_path` (string): Path to the document file
- `output_format` (string): "markdown", "json", or "text" (default: "markdown")
- `extract_images` (boolean): Whether to extract and describe images (default: false)
- `extract_tables` (boolean): Whether to extract table structure (default: true)
**Example:**
```
process_document("report.pdf", "markdown", true, true)
```
### 2. `batch_process_documents`
Process multiple documents in batch with optional file output.
**Parameters:**
- `file_paths` (array): List of document file paths (max 20 files)
- `output_format` (string): Output format for all documents (default: "markdown")
- `output_directory` (string): Directory to save files (empty = return content)
**Example:**
```
batch_process_documents(["doc1.pdf", "doc2.docx"], "markdown", "/output")
```
### 3. `extract_document_metadata`
Extract detailed metadata and structure information from a document.
**Parameters:**
- `file_path` (string): Path to the document file
**Example:**
```
extract_document_metadata("report.pdf")
```
## 🛠 Installation
### For Goose Users
#### Option 1: Standard Installation
1. Install the MCP server:
```bash
pip install mcp-gosling
```
2. Add to your Goose configuration:
```json
{
"mcpServers": {
"gosling": {
"command": "mcp-gosling",
"args": []
}
}
}
```
#### Option 2: Using uvx (Modern)
1. Ensure uv is installed:
```bash
pip install uv
```
2. Add to your Goose configuration:
```json
{
"mcpServers": {
"gosling": {
"command": "uvx",
"args": ["mcp-gosling"]
}
}
}
```
### For MCP Development
1. Clone and install:
```bash
git clone https://github.com/masanderso/mcp-gosling.git
cd mcp-gosling
pip install -e .
```
2. Test with MCP Inspector:
```bash
mcp dev src/mcp_docling/server.py
```
## 🔧 Configuration
The server automatically configures Docling with optimal settings:
- OCR enabled for scanned documents
- Table structure extraction with cell matching
- Support for all major document formats
- 50MB file size limit for safety
## 🎯 Use Cases
- **Research**: Extract content from academic papers and reports
- **Business**: Process contracts, invoices, and presentations
- **Data Extraction**: Convert documents to structured data
- **Content Migration**: Bulk convert document formats
- **Analysis**: Extract metadata and document structure
## 🏗 Architecture
This server follows the MCP (Model Context Protocol) specification:
- **Tools**: Document processing functions exposed to AI assistants
- **STDIO Transport**: Communication via standard input/output
- **Error Handling**: Proper MCP error responses
- **Type Safety**: Full type annotations and validation
## 🤝 Integration Examples
### With Goose
```
"Process the quarterly report in /documents/q4-report.pdf and summarize the key findings"
```
### With other MCP clients
```python
# Call the process_document tool
result = await client.call_tool("process_document", {
"file_path": "/path/to/document.pdf",
"output_format": "markdown"
})
```
## 📊 Performance
- **Speed**: Optimized for production workloads
- **Memory**: Efficient processing of large documents
- **Reliability**: Robust error handling and validation
- **Scalability**: Suitable for batch processing workflows
## 🐛 Troubleshooting
Common issues and solutions:
- **File not found**: Ensure file paths are absolute and accessible
- **Large files**: Files over 50MB are rejected for safety
- **Format errors**: Check that file format is supported
- **Memory issues**: Process large batches in smaller chunks
## 📄 License
MIT License - see [LICENSE](LICENSE) file for details.
## 🤝 Contributing
Contributions welcome! Please read our contributing guidelines and submit pull requests.
## 🔗 Links
- [Model Context Protocol](https://modelcontextprotocol.io/)
- [IBM Docling Library](https://github.com/DS4SD/docling)
- [Goose AI Assistant](https://block.github.io/goose/)
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
Raw data
{
"_id": null,
"home_page": null,
"name": "mcp-gosling",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "ai, docling, document-processing, goose, mcp, model-context-protocol, pdf, server, text-extraction",
"author": null,
"author_email": "Michael Anderson <masanderso@users.noreply.github.com>",
"download_url": "https://files.pythonhosted.org/packages/7c/92/56579a8d071af21548040fb24c661f8a0fb368b62e2394b9166205b2518a/mcp_gosling-0.1.0.tar.gz",
"platform": null,
"description": "# \ud83e\udd86 MCP Gosling - Document Processor for Goose\n\n[](https://badge.fury.io/py/mcp-gosling)\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n\n> **Advanced document processing extension for Goose AI with enterprise-grade offline fallback**\n\nA powerful [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that provides advanced document processing capabilities for [Goose](https://block.github.io/goose/). Process PDFs, DOCX, PPTX, images, and HTML documents with high fidelity using [IBM's Docling library](https://github.com/DS4SD/docling), with intelligent fallback to offline processing for network-restricted environments.\n\n## \u2728 **Key Features**\n\n\ud83d\udd27 **Enterprise-Ready**: SSL certificate fixes for corporate networks \n\ud83d\udcc4 **Multi-Format**: PDF, DOCX, PPTX, images, HTML, and more \n\ud83c\udf10 **Offline Capable**: Graceful fallback to PyPDF2 when Hugging Face is blocked \n\u26a1 **High Performance**: Optimized for production workloads \n\ud83d\udee1\ufe0f **Robust**: Comprehensive error handling and validation \n\ud83c\udfaf **AI-Optimized**: Clean Markdown output perfect for AI analysis \n\n## \ud83d\ude80 **Quick Start**\n\n### Installation Options\n\n#### Option 1: Standard Installation (Recommended)\n```bash\npip install mcp-gosling\n```\n\n#### Option 2: Using uvx (Modern Tooling)\n```bash\n# Run directly without installation\nuvx mcp-gosling\n\n# Or using uv tool run (identical behavior)\nuv tool run mcp-gosling\n```\n\n### Configuration for Goose\n\n#### With Standard Installation:\n```json\n{\n \"mcpServers\": {\n \"gosling\": {\n \"command\": \"mcp-gosling\",\n \"args\": []\n }\n }\n}\n```\n\n#### With uvx:\n```json\n{\n \"mcpServers\": {\n \"gosling\": {\n \"command\": \"uvx\",\n \"args\": [\"mcp-gosling\"]\n }\n }\n}\n```\n\n### Usage\n\n```bash\n# Process your AWS certification PDF\ngoose \"Process my AWS certification document at /path/to/cert.pdf\"\n\n# Batch process multiple documents\ngoose \"Process all PDFs in my documents folder and summarize them\"\n\n# Extract metadata only\ngoose \"What are the metadata details of this document?\"\n```\n\n## \ud83d\udccb **Available Tools**\n\n### `process_document`\nProcess a single document and return clean Markdown content.\n\n**Parameters:**\n- `file_path` (string): Path to the document file\n- `output_format` (string): \"markdown\", \"json\", or \"text\" (default: \"markdown\")\n- `extract_images` (boolean): Whether to extract and describe images (default: false)\n- `extract_tables` (boolean): Whether to extract table structure (default: true)\n\n### `batch_process_documents`\nProcess multiple documents in batch with optional file output.\n\n**Parameters:**\n- `file_paths` (array): List of document file paths (max 20 files)\n- `output_format` (string): Output format for all documents (default: \"markdown\")\n- `output_directory` (string): Directory to save files (empty = return content)\n\n### `extract_document_metadata`\nExtract detailed metadata and structure information from a document.\n\n**Parameters:**\n- `file_path` (string): Path to the document file\n\n## \ud83d\udd27 **Advanced Features**\n\n### Corporate Network Support\n- \u2705 SSL certificate fixes for enterprise environments\n- \u2705 Automatic fallback when Hugging Face Hub is blocked\n- \u2705 Works behind corporate firewalls and proxies\n\n### Intelligent Processing\n- **Primary**: IBM Docling for high-fidelity extraction with OCR and table recognition\n- **Fallback**: PyPDF2 for reliable offline PDF processing\n- **Formats**: PDF, DOCX, PPTX, PNG, JPG, HTML, TXT, MD, JSON\n\n### Performance & Reliability\n- File size limits (50MB for full processing, 5MB for metadata)\n- Batch processing (up to 20 files)\n- Comprehensive error handling\n- Memory-efficient processing\n\n## \ud83c\udfaf **Use Cases**\n\n- **\ud83d\udcd1 Document Analysis**: Extract and analyze content from reports, papers, contracts\n- **\ud83c\udfe2 Enterprise**: Process documents in network-restricted corporate environments \n- **\ud83d\udd0d Research**: Batch process academic papers and research documents\n- **\ud83d\udcca Data Extraction**: Convert documents to structured data for AI analysis\n- **\ud83d\udcdd Content Migration**: Bulk convert document formats with preserved structure\n\n## \ud83d\udee0 **Technical Details**\n\n**Built With:**\n- [IBM Docling](https://github.com/DS4SD/docling) - Enterprise-grade document processing\n- [PyPDF2](https://pypdf2.readthedocs.io/) - Reliable offline PDF processing\n- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - Model Context Protocol\n\n**Requirements:**\n- Python 3.9+\n- Works on macOS, Linux, Windows\n- Optional: GPU acceleration for enhanced performance\n\n## \ud83d\ude80 **Installation Options**\n\n### For Goose Users (Recommended)\n\n1. **Install via pip:**\n ```bash\n pip install mcp-gosling\n ```\n\n2. **Configure in Goose:**\n Add the MCP server to your Goose configuration\n\n3. **Start using:**\n ```bash\n goose \"Process this document for me\"\n ```\n\n### For MCP Development\n\n1. **Clone and install:**\n ```bash\n git clone https://github.com/masanderso/goose-docling.git\n cd goose-docling\n pip install -e .\n ```\n\n2. **Test with MCP Inspector:**\n ```bash\n mcp dev src/mcp_docling/server.py\n ```\n\n## \ud83d\udd0d **Example Outputs**\n\n### Document Processing\n```markdown\n# Document: AWS Certified Solutions Architect - Associate.pdf\n**Source:** /path/to/document.pdf\n**Format:** .pdf\n**Pages:** 2\n\n---\n\n## Page 1\n\nAWS Certified Solutions Architect - Associate\nNotice of Exam Results\nCandidate: Matthew Sanderson Exam Date: 12/3/2024\nCandidate Score: 779 Pass/Fail: PASS\n...\n```\n\n### Metadata Extraction\n```json\n{\n \"file_info\": {\n \"name\": \"document.pdf\",\n \"size_mb\": 0.03,\n \"format\": \".pdf\"\n },\n \"document_structure\": {\n \"page_count\": 2,\n \"has_tables\": true,\n \"has_figures\": false\n }\n}\n```\n\n## \ud83e\udd1d **Contributing**\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n1. Fork the repository\n2. Create a feature branch\n3. Make your changes\n4. Add tests if applicable\n5. Submit a pull request\n\n## \ud83d\udcc4 **License**\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## \ud83d\udd17 **Links**\n\n- [Goose AI Assistant](https://block.github.io/goose/)\n- [Model Context Protocol](https://modelcontextprotocol.io/)\n- [IBM Docling Library](https://github.com/DS4SD/docling)\n- [Issue Tracker](https://github.com/masanderso/goose-docling/issues)\n\n## \ud83c\udff7\ufe0f **Tags**\n\n`goose-extension` `document-processing` `mcp-server` `docling` `pdf-processing` `enterprise-ready` `offline-capable` `ai-tools`\n\n## \ud83d\ude80 Features\n\n- **Multi-format Support**: PDF, DOCX, PPTX, images (PNG, JPG), HTML, and more\n- **Intelligent Processing**: OCR, table extraction, and structure preservation\n- **Flexible Output**: Markdown, JSON, or plain text formats\n- **Batch Processing**: Handle multiple documents efficiently\n- **Metadata Extraction**: Detailed document structure and properties\n- **Production Ready**: Robust error handling and file size limits\n\n## \ud83d\udccb Tools Available\n\nThis MCP server exposes three main tools:\n\n### 1. `process_document`\nProcess a single document and return the converted content.\n\n**Parameters:**\n- `file_path` (string): Path to the document file\n- `output_format` (string): \"markdown\", \"json\", or \"text\" (default: \"markdown\")\n- `extract_images` (boolean): Whether to extract and describe images (default: false)\n- `extract_tables` (boolean): Whether to extract table structure (default: true)\n\n**Example:**\n```\nprocess_document(\"report.pdf\", \"markdown\", true, true)\n```\n\n### 2. `batch_process_documents`\nProcess multiple documents in batch with optional file output.\n\n**Parameters:**\n- `file_paths` (array): List of document file paths (max 20 files)\n- `output_format` (string): Output format for all documents (default: \"markdown\")\n- `output_directory` (string): Directory to save files (empty = return content)\n\n**Example:**\n```\nbatch_process_documents([\"doc1.pdf\", \"doc2.docx\"], \"markdown\", \"/output\")\n```\n\n### 3. `extract_document_metadata`\nExtract detailed metadata and structure information from a document.\n\n**Parameters:**\n- `file_path` (string): Path to the document file\n\n**Example:**\n```\nextract_document_metadata(\"report.pdf\")\n```\n\n## \ud83d\udee0 Installation\n\n### For Goose Users\n\n#### Option 1: Standard Installation\n1. Install the MCP server:\n```bash\npip install mcp-gosling\n```\n\n2. Add to your Goose configuration:\n```json\n{\n \"mcpServers\": {\n \"gosling\": {\n \"command\": \"mcp-gosling\",\n \"args\": []\n }\n }\n}\n```\n\n#### Option 2: Using uvx (Modern)\n1. Ensure uv is installed:\n```bash\npip install uv\n```\n\n2. Add to your Goose configuration:\n```json\n{\n \"mcpServers\": {\n \"gosling\": {\n \"command\": \"uvx\",\n \"args\": [\"mcp-gosling\"]\n }\n }\n}\n```\n\n### For MCP Development\n\n1. Clone and install:\n```bash\ngit clone https://github.com/masanderso/mcp-gosling.git\ncd mcp-gosling\npip install -e .\n```\n\n2. Test with MCP Inspector:\n```bash\nmcp dev src/mcp_docling/server.py\n```\n\n## \ud83d\udd27 Configuration\n\nThe server automatically configures Docling with optimal settings:\n- OCR enabled for scanned documents\n- Table structure extraction with cell matching\n- Support for all major document formats\n- 50MB file size limit for safety\n\n## \ud83c\udfaf Use Cases\n\n- **Research**: Extract content from academic papers and reports\n- **Business**: Process contracts, invoices, and presentations \n- **Data Extraction**: Convert documents to structured data\n- **Content Migration**: Bulk convert document formats\n- **Analysis**: Extract metadata and document structure\n\n## \ud83c\udfd7 Architecture\n\nThis server follows the MCP (Model Context Protocol) specification:\n- **Tools**: Document processing functions exposed to AI assistants\n- **STDIO Transport**: Communication via standard input/output\n- **Error Handling**: Proper MCP error responses\n- **Type Safety**: Full type annotations and validation\n\n## \ud83e\udd1d Integration Examples\n\n### With Goose\n```\n\"Process the quarterly report in /documents/q4-report.pdf and summarize the key findings\"\n```\n\n### With other MCP clients\n```python\n# Call the process_document tool\nresult = await client.call_tool(\"process_document\", {\n \"file_path\": \"/path/to/document.pdf\",\n \"output_format\": \"markdown\"\n})\n```\n\n## \ud83d\udcca Performance\n\n- **Speed**: Optimized for production workloads\n- **Memory**: Efficient processing of large documents\n- **Reliability**: Robust error handling and validation\n- **Scalability**: Suitable for batch processing workflows\n\n## \ud83d\udc1b Troubleshooting\n\nCommon issues and solutions:\n\n- **File not found**: Ensure file paths are absolute and accessible\n- **Large files**: Files over 50MB are rejected for safety\n- **Format errors**: Check that file format is supported\n- **Memory issues**: Process large batches in smaller chunks\n\n## \ud83d\udcc4 License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## \ud83e\udd1d Contributing\n\nContributions welcome! Please read our contributing guidelines and submit pull requests.\n\n## \ud83d\udd17 Links\n\n- [Model Context Protocol](https://modelcontextprotocol.io/)\n- [IBM Docling Library](https://github.com/DS4SD/docling)\n- [Goose AI Assistant](https://block.github.io/goose/)\n- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)\n",
"bugtrack_url": null,
"license": null,
"summary": "MCP Gosling - Advanced document processing server for Goose AI using IBM's Docling library",
"version": "0.1.0",
"project_urls": {
"Homepage": "https://github.com/masanderso/goose-docling",
"Issues": "https://github.com/masanderso/goose-docling/issues",
"Repository": "https://github.com/masanderso/goose-docling"
},
"split_keywords": [
"ai",
" docling",
" document-processing",
" goose",
" mcp",
" model-context-protocol",
" pdf",
" server",
" text-extraction"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "a0a68c57b35b9a61d594fb79d8eefc350724d1ce631ed4618bb583f2814b752e",
"md5": "6c63ad9202db68d0228e2f294e3dddac",
"sha256": "85af761fdd47d19e591cbb04dd1217ec663911157717a4cdf979d3e22afa4b69"
},
"downloads": -1,
"filename": "mcp_gosling-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6c63ad9202db68d0228e2f294e3dddac",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 9680,
"upload_time": "2025-08-25T02:12:30",
"upload_time_iso_8601": "2025-08-25T02:12:30.725601Z",
"url": "https://files.pythonhosted.org/packages/a0/a6/8c57b35b9a61d594fb79d8eefc350724d1ce631ed4618bb583f2814b752e/mcp_gosling-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "7c9256579a8d071af21548040fb24c661f8a0fb368b62e2394b9166205b2518a",
"md5": "038724748987a2f3f348fc432d7545f9",
"sha256": "e10387db6c0bf40417811dbd2de29dcdbbc947b1320eccb169201e8661dbd8b1"
},
"downloads": -1,
"filename": "mcp_gosling-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "038724748987a2f3f348fc432d7545f9",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 13004,
"upload_time": "2025-08-25T02:12:32",
"upload_time_iso_8601": "2025-08-25T02:12:32.009400Z",
"url": "https://files.pythonhosted.org/packages/7c/92/56579a8d071af21548040fb24c661f8a0fb368b62e2394b9166205b2518a/mcp_gosling-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-25 02:12:32",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "masanderso",
"github_project": "goose-docling",
"github_not_found": true,
"lcname": "mcp-gosling"
}
JSON
