"Tools for generating charts based on data" matching MCP servers:

This server enables multi-agent conversations for interacting with Cirro's biological data platform through its OpenAPI interface, auto-generated using AG2's MCP builder.

Provides a collection of MCP servers for computational chemistry tasks including molecular generation and retrosynthesis. Also offers property prediction and molecule pricing capabilities.

Enables MCP-capable AI clients to characterize proteins end to end: PDB/mmCIF validation, sequence extraction and typing, pLDDT confidence summaries, metal-site geometry with bidentate chelate detection, GROMACS trajectory metrics and plots, and live UniProt, AlphaFold DB, and RCSB lookups. Also ships a point-and-click Protein Lab web UI (and a persistent run registry) so students and wet-lab biologists can get the same results without any client setup.

Enables AI agents to retrieve and analyze breeding data from BrAPI-compliant databases through shared tools, with human-in-the-loop approval and verification.

Parses MS-DIAL binary lipidomics outputs and runs standard analyses server-side, returning compact summaries so an LLM can drive full lipidomics analysis without exposing raw matrices.

Enables deep probabilistic analysis of single-cell omics data using scvi-tools through natural language. Supports SCVI for scRNA-seq analysis, SCANVI for cell type annotation, TOTALVI for multi-modal RNA/protein data, and PEAKVI for scATAC-seq analysis.

A specialized MCP server for Metal-Organic Framework research that provides tools for database searching, structural optimization, and energy calculations via ASE. It enables scientific workflows by allowing users to interact with MOF data and perform chemical simulations through a standard SSE interface.

Enables searching cryo-EM density maps in EMDB and raw datasets in EMPIAR, with filtering by resolution, keyword, and cross-referencing between the two databases.

Enables AI-driven pharmacogenomic analysis by querying structured genetic variant, drug response, and disease risk data. Supports natural language questions about medications, traits, and health risks based on user genome data, with privacy-first local execution.

Enables bioinformatics analysis through natural language conversations with Claude Desktop, automatically generating and executing Python scripts to produce HTML reports and visualizations.

Enables querying and accessing hivtools data through a REST API and MCP-compatible tools.

Enables molecular generation, optimization, and analysis through NVIDIA MolMIM API. Supports generating drug-like molecules with desired properties, extracting molecular embeddings, and exploring chemical space around seed molecules.

An MCP server for running local LLMs using llama-cpp-python, with built-in tools for generating drug-like molecule SMILES.

🔍 A biomedical literature annotation and relationship mining server based on PubTator3, providing convenient access through the MCP interface.

Enables AI assistants to perform genomic variant analysis using OakVar, including running annotation pipelines, managing 200+ annotator modules, querying variant databases, and generating reports in various formats.

Provides access to the ClinicalTrials.gov AACT database, enabling analysis of clinical trial data, tracking development trends, and generating therapeutic landscape insights.

Enables LLMs to run rule-based molecular interaction simulations using the Kappa language through the Kappybara package. Returns simulation results as CSV data for analysis of biochemical systems and molecular binding dynamics.

Enables on-demand querying of live NASA POWER weather anomalies and NCBI Pathogen Detection genomic antimicrobial-resistance data for causal analysis.

Exposes five local tools that let AI agents quantify Western blots, IHC/stain and immunofluorescence colocalization images, and analyze or independently verify qPCR data. Paths are restricted to configured allow-listed directories, so all computation stays on the user's machine.

Enables RNA structure analysis, sequence evaluation, and inverse design using geometric deep learning models. Supports both quick computational analysis and long-running batch processing for generating RNA sequences that fold into target structures.