Loading ToolBench
Collection of MCP servers for precision medicine genomics, cell analysis, clinical integration, and bioinformatics workflows
This multi-server MCP suite exposes 17 precision medicine tools across cardiometabolic, genomics, cell imaging, and clinical integration domains. Strengths: all tools have explicit names starting with action verbs (calculate_, interpret_, classify_, segment_, deconvolve_, parse_, download_, get_, detect_, deidentify_, query_), complete input schemas with typed parameters and descriptions, and clear domain-specific documentation. Weaknesses: output schemas are not documented anywhere in the source provided, error handling guidance is absent, descriptions lack actionable context for LLM selection (e.g., when to use Framingham vs other risk calculators), no discussion of idempotency or retry semantics for tools that write data (segment_cells, download_geo_dataset, download_sra_dataset, deidentify_*), and missing parameter dependencies (e.g., which annotation databases are available in parse_vcf_results). Security concerns: several tools accept file paths and credentials (cibersortx_token in deconvolve_celltype_composition) without clear guidance on secret injection. Tool composition is reasonable, each tool has a single responsibility, but chaining guidance is absent (e.g., after segment_cells, what's the next step?). Descriptions average ~80 - 120 characters, below the 194-char baseline for A+ tools but above the minimum floor.
Calculate Framingham cardiovascular risk score from patient demographics and biomarkers
Classify cell phenotypes from segmentation masks and marker expression images using machine learning
Perform immune cell-type deconvolution from bulk transcriptomics using CIBERSORTx API or NNLS fallback
De-identify single-cell transcriptomics AnnData objects by removing patient identifiers and clinical metadata
De-identify clinical text documents by removing or masking protected health information (PHI) using Claude Haiku with Safe Harbor standard
De-identify PDF clinical documents by detecting and masking protected health information across multi-page documents
Detect and characterize sequencing library chemistry and preparation method from BAM file analysis
Output schemas not documented. No tool specifies what fields, types, or structure the response will have. LLMs cannot plan downstream tool calls or extract results without knowing expected response shape.
Error handling and recovery guidance missing. No tool description explains what to do if inputs are invalid, resources not found, or operations fail. Descriptions lack actionable error context.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 59 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 26 | - | v1 |
Download gene expression datasets from NCBI GEO database with automatic processing
Download sequencing data from NCBI SRA with automatic format conversion and quality assessment
Retrieve diagnostic reports (pathology, radiology, genomics) from Epic FHIR API
Retrieve patient clinical data from Epic EHR via FHIR API with HIPAA de-identification
Retrieve genomic reference sequences and annotation data from FGbio toolkit for specific chromosomal regions
Interpret cardiovascular biomarkers including troponin, BNP, and other cardiac markers
Parse copy number segment (CNS) files and identify amplifications and deletions with clinical relevance
Parse somatic variant (VCF) files and annotate variants with clinical significance, COSMIC, and ClinVar data
Query laboratory observations and vital signs from Epic FHIR API for patient cohorts
Perform deep learning-based cell segmentation using DeepCell-TF on microscopy images
Credential exposure risk: cibersortx_token in deconvolve_celltype_composition accepts secrets as a parameter. Credentials must use server-side secret injection, not tool parameters. Agent traces will log this token.
Idempotency and write semantics unclear. Tools like segment_cells, download_geo_dataset, download_sra_dataset, and all deidentify_* tools are marked WRITE but lack guidance on retry behavior, duplicate prevention, or transactional guarantees. LLMs cannot safely retry without risk of side effects.
Tool descriptions lack context for LLM selection. For example, calculate_framingham_score and interpret_biomarkers both assess cardiovascular risk, but the description doesn't explain when to use one vs the other. Similarly, parse_vcf_results and parse_cns_results are complementary but lack cross-reference hints.
Parameter dependencies not documented. For example, deconvolve_celltype_composition requires cibersortx_token only if method='cibersortx_api', but this conditional requirement is not stated in the parameter descriptions.
Pagination and result limits not addressed. Tools like parse_vcf_results, parse_cns_results, query_observations, and get_diagnostic_reports may return hundreds of variants or observations, but no limit, offset, or pagination mechanism is documented. Large unbound results exhaust context.
Tool chaining guidance absent. After classify_cell_phenotype, what field identifies the cells for downstream analysis? After download_geo_dataset, what field contains the matrix path? Missing chaining IDs force discovery detours.