Cochrane Meta-Analysis MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Cochrane Meta-Analysis MCP Serverperform a random-effects meta-analysis on the 10 RCTs data"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Cochrane Meta-Analysis MCP Server
An MCP (Model Context Protocol) server that provides AI-assisted meta-analysis workflows following Cochrane methodological standards.
Features
RevMan Import: Parse RevMan 5 (.rm5 XML) and Cochrane CSV exports
Data Validation: Comprehensive validation against Cochrane standards
Meta-Analysis: R-based statistical analysis using metafor/meta packages
Forest Plots: Publication-ready visualizations
Publication Bias: Funnel plots, Egger's test, trim-and-fill
Reporting: Automated Cochrane-style HTML/PDF reports
Related MCP server: Medical Research MCP Suite
Installation
npm install
npm run buildPrerequisites
Node.js 18+
R 4.0+ with packages:
metafor
meta
ggplot2
jsonlite
Install R packages:
install.packages(c("metafor", "meta", "ggplot2", "jsonlite"))Configuration
Add to Claude Desktop config (~/.config/claude/claude_desktop_config.json):
{
"mcpServers": {
"cochrane-meta": {
"command": "node",
"args": ["/Users/YOUR_USERNAME/Documents/cochrane-meta-mcp/dist/index.js"]
}
}
}Available Tools
1. import_revman_data
Import and parse RevMan 5 files or Cochrane CSV exports.
{
"file_path": "/path/to/review.rm5",
"format": "rm5"
}2. validate_cochrane_data
Validate study data against Cochrane standards.
{
"data": { ... },
"validation_level": "comprehensive"
}3. perform_meta_analysis
Execute meta-analysis following Cochrane Handbook Chapter 10.
{
"data": { ... },
"effect_measure": "OR",
"model": "random",
"heterogeneity_test": true
}4. generate_forest_plot
Create publication-ready forest plot.
{
"analysis_results": { ... },
"plot_style": "classic",
"output_path": "/path/to/forest_plot.png"
}5. assess_publication_bias
Perform publication bias assessment.
{
"analysis_results": { ... },
"methods": ["funnel_plot", "egger_test", "trim_fill"],
"output_path": "/path/to/funnel_plot.png"
}6. generate_cochrane_report
Generate comprehensive Cochrane-style report.
{
"analysis_results": { ... },
"format": "html",
"output_path": "/path/to/report.html"
}Example Usage with Claude
User: I have a Cochrane CSV file with 10 RCTs comparing intervention A vs control.
Can you help me run a meta-analysis?
Claude: I'll help you analyze this. First, let me import the data using the import_revman_data tool...
[Uses import_revman_data]
Claude: Great! I've imported 10 studies. Let me validate the data...
[Uses validate_cochrane_data]
Claude: Validation passed with 2 warnings about small sample sizes.
Now I'll perform the meta-analysis using a random-effects model
with odds ratios as the effect measure...
[Uses perform_meta_analysis]
Claude: The pooled OR is 0.75 (95% CI: 0.62-0.91), p=0.003, with moderate
heterogeneity (I²=45%). Let me generate a forest plot...
[Uses generate_forest_plot]
Claude: Forest plot created. Now checking for publication bias...
[Uses assess_publication_bias]
Claude: Egger's test p=0.42, no evidence of publication bias.
Generating final report...
[Uses generate_cochrane_report]
Claude: Complete! Your meta-analysis report is ready at report.htmlCSV Format
For CSV imports, use this format:
study_id,authors,year,title,intervention,comparison,outcome,events_treatment,n_treatment,events_control,n_control
Study1,Smith 2020,2020,RCT of intervention,Drug A,Placebo,Mortality,10,100,20,100
Study2,Jones 2021,2021,Another RCT,Drug A,Placebo,Mortality,15,150,30,150For continuous outcomes:
study_id,authors,year,title,intervention,comparison,outcome,mean_treatment,sd_treatment,n_treatment,mean_control,sd_control,n_controlDevelopment
# Watch mode
npm run dev
# Build
npm run build
# Test (coming soon)
npm testArchitecture
TypeScript MCP Server: Handles tool requests from Claude
R Bridge: Executes statistical analyses via Rscript
Validation Layer: Zod schemas for data validation
Tools: Modular tool implementations for each MCP capability
Integration with Existing Tools
This MCP server integrates with your existing meta-analysis infrastructure:
Uses your R meta-analysis scripts (
~/meta_analysis_workflow.R)Compatible with medical research multi-agent system
Can leverage AI citation processors for literature extraction
Cochrane Compliance
Follows:
Cochrane Handbook for Systematic Reviews (Chapter 10)
PRISMA reporting guidelines
Cochrane risk of bias (RoB 2) recommendations
GRADE framework for evidence certainty
License
MIT
Version
0.1.0
Author
Matheus Rech
This server cannot be installed
Maintenance
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