{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_egb8vfbadxjv","handle":"atomica-mcp","url":"https://wellknown.network/agents/atomica-mcp","links":{"self":"https://wellknown.network/agents/atomica-mcp/record.json","html":"https://wellknown.network/agents/atomica-mcp","markdown":"https://wellknown.network/agents/atomica-mcp/record.md","api":"https://wellknown.network/api/v1/agents/atomica-mcp","status":"https://wellknown.network/api/v1/agents/atomica-mcp/status","claim":"https://wellknown.network/agents/atomica-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/atomica-mcp/claim.json","badge":"https://wellknown.network/agents/atomica-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/atomica-mcp/history","tools":"https://wellknown.network/api/v1/agents/atomica-mcp/tools"},"ard":{"identifier":"urn:air::server:atomica-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"atomica-mcp","summary":"MCP server for PDB structure queries and protein resolution using AnAge database","description":"# atomica-mcp\n\n[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nMCP (Model Context Protocol) server for ATOMICA longevity proteins dataset and PDB structure analysis.\n\n## What is ATOMICA?\n\n**ATOMICA** is a geometric deep learning model that learns atomic-scale representations of intermolecular interactions across proteins, small molecules, ions, lipids, and nucleic acids. Trained on 2M+ interaction complexes, it generates embeddings that capture physicochemical features shared across molecular classes.\n\n### Why ATOMICA Embeddings Are Useful\n\n- **Interaction Scoring**: ATOMICA embeddings quantify interface similarity and predict binding partners based on learned physicochemical patterns\n- **Critical Residues**: Identifies residues that most influence interactions (low ATOMICA scores = high impact on binding)\n- **Cross-Modal Transfer**: Knowledge learned from protein-protein interactions helps predict protein-ligand binding\n- **Visualization**: PyMOL commands highlight interaction-critical regions for structural analysis\n\n### This MCP Server Provides\n\nThis server provides access to the ATOMICA longevity proteins dataset from Hugging Face, which contains comprehensive structural analysis of key aging-related proteins using the ATOMICA deep learning model. The server also provides auxiliary functions for resolving arbitrary PDB structures and UniProt IDs.\n\n## Features\n\n- **ATOMICA Dataset Access**: Query the curated dataset of 94 longevity-related protein structures\n- **Automatic Dataset Management**: Downloads dataset from Hugging Face on first use\n- **Comprehensive Metadata**: Access PDB metadata, ATOMICA interaction scores, critical residues, and PyMOL scripts\n- **Gene-Based Queries**: Search structures by gene symbols (NFE2L2, KEAP1, SOX2, APOE, OCT4)\n- **Organism Queries**: Filter structures by organis…","publisher":null,"homepage":null,"repository":null,"version":"0.1.5","license":null,"protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:atomica-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","summary":"pypi","version":"pypi","description":"pypi"}},"derived":{"capabilities":[{"slug":"data.database","name":"Databases","confidence":0.791,"provenance":"derived"}],"categories":["data"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"atomica-mcp","registry":"pypi","observedAt":"2026-11-08T23:27:46.644Z","publishedAt":"2025-10-22T22:35:54.208912Z","latestVersion":"0.1.5"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"atomica-mcp","url":"https://pypi.org/project/atomica-mcp/","firstSeenAt":"2026-09-09T09:24:11.805Z","fetchedAt":"2026-11-08T23:26:54.943Z","normalizedAt":"2026-11-08T23:26:54.943Z"}]},"firstSeenAt":"2026-09-09T09:24:11.805Z","updatedAt":"2026-11-08T23:27:46.644Z"}