# RDL MCP Server [Health: Active]
**Category:** 💻 Developer Tools
**Repository:** https://github.com/bethmaloney/rdl-mcp
**GitHub Stars:** 11
**Views:** 0
**Installs:** 0
**Upvotes:** 0
**Directory Page:** https://allmcps.com/mcp/rdl-mcp-server
## Description
Edit SSRS reports using AI - simple tools to read and modify RDL files
## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `uvx` (confidence: high):
```json
"mcpServers": {
"rdl-mcp-server": {
"command": "uvx",
"args": ["rdl-mcp"]
}
}
```
## Documentation & README
# RDL MCP Server
mcp-name: io.github.bethmaloney/rdl-mcp
[](https://pypi.org/project/rdl-mcp/)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://modelcontextprotocol.io)
Edit SSRS reports using AI assistants instead of wrestling with 2000+ lines of XML. This [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server gives Claude, Copilot, and other AI tools simple commands to read and modify RDL files.
## What It Does
**Read reports:**
- `describe_rdl_report` - Get report structure overview
- `get_rdl_datasets` - View datasets, fields, and stored procedures (supports field limiting and filtering)
- `get_rdl_parameters` - List all report parameters
- `get_rdl_columns` - See column headers, widths, and bindings
**Modify reports:**
- `update_column_header` / `update_column_width` - Change columns
- `add_column` / `remove_column` - Add or remove columns
- `update_column_format` - Change number/date formatting
- `update_stored_procedure` - Swap stored procedures
- `add_dataset_field` / `remove_dataset_field` - Manage dataset fields
- `add_parameter` / `update_parameter` - Manage parameters
- `validate_rdl` - Validate XML after changes
**Why it's better than editing XML:**
- AI sees clean JSON instead of verbose XML namespaces
- One-line commands instead of error-prone string manipulation
- Automatic validation catches errors before they break reports
- No dependencies - just Python 3.8+ standard library
## Installation
**Requirements:**
- Python 3.8 or higher
- [uv](https://docs.astral.sh/uv/) (Python package manager and tool runner)
**Installing uv:**
- **macOS/Linux:** `curl -LsSf https://astral.sh/uv/install.sh | sh`
- **Windows:** `powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"`
- **Alternative (all platforms):** `pip install uv` or see [installation docs](https://docs.astral.sh/uv/getting-started/installation/)
**Note:** `uvx` (included with `uv`) automatically handles the Python environment and dependencies. No manual Python package installation needed!
### Quick Start
Claude Desktop
Edit config file:
- **macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json`
- **Windows:** `%APPDATA%\Claude\claude_desktop_config.json`
- **Linux:** `~/.config/Claude/claude_desktop_config.json`
```json
{
"mcpServers": {
"rdl-mcp": {
"command": "uvx",
"args": ["rdl-mcp"]
}
}
}
```
GitHub Copilot (VSCode)
Add to VSCode settings (`.vscode/mcp.json` in your workspace or user settings):
```json
{
"servers": {
"rdlMcp": {
"type": "stdio",
"command": "uvx",
"args": ["rdl-mcp"]
}
}
}
```
**Note:** Requires VSCode with Copilot Chat extension installed.
**After installation:** Restart your AI assistant and try: `"Describe the structure of my report.rdl file"`
Optional: Enable debug logging
Set environment variables:
- `RDL_MCP_LOG_LEVEL`: `DEBUG`, `INFO`, `WARNING`, or `ERROR`
- `RDL_MCP_LOG_FILE`: Path to log file
## Usage
Just ask your AI assistant in natural language:
- "What datasets does this report use?"
- "Make the Account Number column 2 inches wide"
- "Format the Amount column as currency with 2 decimals"
- "Add a new Amount column that shows the sum in the footer"
- "Add a Status column but leave the footer blank"
- "Update the main dataset to use the V2 stored procedure and add the TaxAmount field"
- "Remove the obsolete Status column"
- "Add a Year parameter to filter the report"
The AI assistant will use the appropriate MCP tools automatically.
## Example: Editing vs. XML
**Without MCP** (manually editing XML):
```xml
Old Header
```
**With MCP** (one command):
```python
update_column_header(filepath="report.rdl",
old_header="Old Header",
new_header="New Header")
```
## API Reference
View all available tools
### Reading Tools
- **`describe_rdl_report(filepath)`** - Report structure summary
- **`get_rdl_datasets(filepath, field_limit?, field_pattern?)`** - Datasets with fields and stored procedures
- `field_limit`: 0 = counts only (default), -1 = all fields, N = limit to N fields
- `field_pattern`: Optional regex to filter field names
- **`get_rdl_parameters(filepath)`** - All parameters with configurations
- **`get_rdl_columns(filepath)`** - Column headers, widths, bindings
### Editing Tools
- **`update_column_header(filepath, old_header, new_header)`** - Change column text
- **`update_column_width(filepath, column_index, new_width)`** - Modify width (e.g. "2.5in")
- **`update_column_format(filepath, column_index, format_string)`** - Change format (e.g. "#,0.00", "dd/MM/yyyy", "C2")
- **`add_column(filepath, column_index, header_text, field_binding, width?, format_string?, footer_expression?)`** - Add column
- `footer_expression`: Optional expression for footer/total row - e.g. "=Sum(Fields!Amount.Value)", "=Count(Fields!ID.Value)", "Total:", or leave empty
- **`remove_column(filepath, column_index)`** - Remove column
- **`update_stored_procedure(filepath, dataset_name, new_sproc)`** - Change dataset sproc
- **`add_dataset_field(filepath, dataset_name, field_name, data_field, type_name)`** - Add field to dataset
- **`remove_dataset_field(filepath, dataset_name, field_name)`** - Remove field from dataset
- **`add_parameter(filepath, name, data_type, prompt)`** - Add new parameter
- **`update_parameter(filepath, name, prompt?, default_value?)`** - Update parameter
- **`validate_rdl(filepath)`** - Validate XML structure
All tools return `{success: bool, message?: string, error?: string}` or structured data.
## Limitations & Roadmap
**Current limitations:**
- Tablix (table) controls only - no Matrix or Chart support yet
- Works best with standard report layouts
- Some complex RDL features may still need manual XML editing
**Planned features:**
- Column reordering, grouping, and sorting configuration
- Expression builder helpers
- Dataset field management
## Troubleshooting
**Server not appearing?**
- Check absolute path in config is correct
- Verify Python 3.8+: `python3 --version`
- Restart your MCP client
**Permission errors?**
- Make script executable: `chmod +x rdl_mcp_server.py`
- Check RDL file read/write permissions
## Releasing a New Version
This server is published to [PyPI](https://pypi.org/project/rdl-mcp/) and the [MCP Registry](https://registry.modelcontextprotocol.io/). To release a new version:
1. **Update version numbers** in both files:
`pyproject.toml`:
```toml
version = "0.2.0"
```
`server.json`:
```json
{
"version": "0.2.0",
"packages": [
{
"version": "0.2.0"
}
]
}
```
2. **Commit your changes**:
```bash
git add .
git commit -m "Release v0.2.0: Add feature description"
```
3. **Create and push a git tag**:
```bash
git tag v0.2.0
git push origin main --tags
```
4. **Automated publishing**: The GitHub Actions workflows automatically:
- Build and publish to PyPI (users can install via `uvx rdl-mcp`)
- Validate `server.json` against the MCP schema
- Publish to the MCP Registry (server appears in registry search)
- Update downstream registries (like GitHub's MCP marketplace)
## Contributing
PRs welcome! Priority areas:
- Better column detection for complex layouts
- More editing operations (reordering, grouping, etc.)
Requirements: Python standard library only
1. Fork repo
2. Create feature branch
3. Make changes + tests
4. Submit PR
## License
MIT License - see [LICENSE](https://github.com/bethmaloney/rdl-mcp/blob/HEAD/LICENSE) file for details.
This means you're free to use, modify, and distribute this software for any purpose, commercial or non-commercial.