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Glama
sbarbi-gh

rnaseq-mcp

by sbarbi-gh
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Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
COUNTS_FILEYesAbsolute path to counts.csv
METADATA_FILEYesAbsolute path to metadata.csv

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
execute_rA

Execute R code in the isolated analysis container. The R session is PERSISTENT: objects defined in earlier calls remain available.

OUTPUT RULES (enforced server-side — follow them in your code):

  • Write to /output ONLY aggregated results: DE tables (log2FC, pvalue, padj per gene), pathway scores, group-level summary statistics, model coefficients.

  • Do NOT write per-sample matrices (normalized counts, raw counts, PCA coordinates) as CSV/TSV to /output. Keep them in R memory for intermediate computation.

  • Plots (PNG) showing sample-level data are permitted — use anonymous labels (S1, S2, ...).

  • ggsave() and png() write to /output/; use descriptive names.

  • If the server blocks a CSV file, revise your code to export an aggregated version.

Available R packages: DESeq2, edgeR, limma, ggplot2, pheatmap, ComplexHeatmap, EnhancedVolcano, ggrepel, patchwork, clusterProfiler, fgsea, msigdbr, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi. No internet access — use org.Hs.eg.db instead of biomaRt for gene annotation.

execute_pythonA

Execute Python code in the isolated analysis container. The Python namespace is PERSISTENT across calls.

OUTPUT RULES (same as execute_r):

  • Write to /output ONLY aggregated results (gene/pathway level).

  • Do NOT write per-sample matrices as CSV.

  • Plots saved with matplotlib/seaborn: plt.savefig("/output/.png").

  • If the server blocks a CSV, produce an aggregated equivalent instead.

Available packages: pydeseq2, pandas, numpy, matplotlib, seaborn, scipy, statsmodels.

list_data_filesA

List files available in /data (anonymized counts and metadata).

read_tableA

Read the first n_rows of a CSV/TSV file from /data and return it as a markdown table. Useful for inspecting column names, sample aliases, and group labels before writing analysis code.

get_outputsA

Retrieve all files written to /output during this session. CSV files containing per-sample data are blocked and replaced with an explanatory message. Images (PNG) are returned as base64-encoded strings.

describe_sessionA

Return R sessionInfo(), available Python symbols, and files already produced in /output. Useful at the start of a session to confirm what packages are available.

reset_sessionA

Destroy the current Docker container and start a fresh one with the same anonymized data. Use this to clear all in-memory R/Python state and /output files.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 7 tools

Disambiguation5/5

Each tool has a clear, distinct purpose: environment inspection, code execution per language, output retrieval, data exploration (listing and reading), and session reset. No two tools overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., describe_session, execute_python, list_data_files). The naming is predictable and easy to interpret.

Tool Count5/5

With 7 tools, the server covers all essential activities for RNA-seq analysis: environment setup, code execution, data exploration, output retrieval, and session management. The count is well-scoped and not excessive.

Completeness4/5

The tool surface provides a solid foundation for RNA-seq analysis: data listing, reading, execution in both R and Python with relevant packages, output retrieval, and reset. A minor gap is the lack of a direct quality control or standard pipeline tool, but the execution tools suffice.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources