This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
An MCP server that lets AI agents answer questions about a single-cell atlas of the zebrafish dorsal habenula, using only the data, with guardrails against made-up or overstated results.
Ask an agent "Is KCTD12.1 a marker for cluster 9?" and it calls these tools, gets normalized statistics with effect sizes, FDR-corrected q-values, confidence intervals, caveats and provenance, and can plot the gene on the UMAP.
The data is the Halpern Lab habenula projectome database: scRNA-seq of ~4,300 adult zebrafish dHb neurons in 14 clusters, each assigned a classifier-predicted projection target in the interpeduncular nucleus (IPN).
Tool | Answers |
| The 14 clusters, cell counts, and most common predicted IPN target |
| Predicted IPN target distribution for one cluster, with 95% CIs |
| Gene symbols matching a query (max 20) |
| % of cells expressing a gene (95% CI) and mean normalized expression, per cluster |
| Where a gene's expressing neurons are predicted to project, and enrichment over the dHb baseline |
| Exploratory two-cluster test: p-value, rank-biserial effect size, detection difference with 95% CI |
| Up to 10 candidate markers for a cluster vs all other cells, Benjamini-Hochberg FDR across the list |
| UMAP colored by a gene's normalized expression (PNG) |
Resource dataset://card: what the data is, how it was made, and what it can't tell you. The server instructs agents to read it first.
Related MCP server: cxg-census-mcp
Risk | What the server does |
Hallucinated genes or clusters | Exact matching only. Unknown names return |
Predictions read as facts | IPN targets are XGBoost predictions with no saved confidence. Every projection answer says so and lists what it does not support. |
Depth-confounded comparisons | A one-time index of per-cell UMI totals enables log1p(CP10K) normalization. Without it, answers fall back to raw counts and say so. |
Significant but trivial results | Every test reports a rank-biserial effect size. "Significant but negligible" (|r| < 0.1) is called out as not a real difference, which matters with thousands of cells. |
Multiple comparisons | Multi-gene tests use Benjamini-Hochberg FDR; the response notes that only the listed genes were tested. |
Pseudoreplication | Cells from one dataset aren't independent replicates; every test is labeled exploratory. |
Small samples | Projection summaries need ≥ 10 expressing cells; tests need ≥ 20 cells per group. Below that, the server refuses with |
Overreach | Each answer carries |
Expensive or open-ended compute | Read-only; at most 10 genes per call; genome-wide scans refused; per-gene LRU cache. |
Malformed or injected input | Pydantic schemas validate every argument (gene symbols must match |
Untraceable answers | Every answer includes provenance: data fingerprint (SHA-256 of the source tables), files read, normalization, parameters, server version, timestamp. Every call is audit-logged as a JSON line. |
git clone https://github.com/tanyabudhiraja/hb-projectome-db # the data
git clone https://github.com/tanyabudhiraja/hb-projectome-mcp
cd hb-projectome-mcp
pip install -e .
# one-time, about 2 minutes: per-cell UMI totals for normalization
HB_DATA_DIR=../hb-projectome-db hb-projectome-mcp build-indexAdd it to an MCP client. For Claude Desktop, in claude_desktop_config.json:
{
"mcpServers": {
"hb-projectome": {
"command": "hb-projectome-mcp",
"env": { "HB_DATA_DIR": "/absolute/path/to/hb-projectome-db" }
}
}
}Variable | Purpose | Default |
| Path to the hb-projectome-db clone | current folder |
| Where the normalization index is stored |
|
| JSON-lines audit log file | stderr |
pip install -e ".[dev]"
pytest33 tests on a 4-gene slice of the real data (tests/fixtures/), covering:
statistics checked against published values (Wilson interval; Newcombe 1998 difference CI) and statsmodels (BH)
every refusal path and error code
provenance on every answer
an end-to-end run that starts the server over stdio, calls it with an MCP client, and checks schema rejection of injected input and the audit log
src/hb_mcp/data.py data access, normalization and guardrails (plain Python, no MCP)
src/hb_mcp/stats.py Wilson and Newcombe CIs, Mann-Whitney with rank-biserial r, BH FDR
src/hb_mcp/server.py MCP tools, input schemas, dataset card, audit log, CLI
tests/ unit, statistics and stdio end-to-end testsThis server cannot be deployed
Read-only MCP tools for AI agent discovery, structured resources, and NIULAI information.
MCP facade over the Nebelus Construction API. ~48 tools give full agent build parity: create/update/probe agents, edit graphs, attach knowledge and vector stores, wire connectors, set governance policies and locked guardrails, enable grounding-trace, and read deployment wiring. Purpose-built for regulated industries: data residency is enforced per region (EU / GCC-KSA), with PII controls and an audit trail. Agents are created as drafts — no deploy tool is exposed over MCP by design; publishing happens in the Nebelus console.
214 tools for AI agents over one MCP endpoint: image, video, audio, 3D, data, storage, mail, GPUs.
Your org's AI agents, tasks, runs, search, and brain files as MCP tools and resources.
