agent.json

{
  "$schema": "https://google.github.io/A2A/schemas/agent-card.json",
  "schemaVersion": "0.2.0",
  "name": "OpenMCAS Hypothesis Agent",
  "description": "An open, MIT-licensed hypothesis-generation agent for MCAS / MCAD (Mast Cell Activation Syndrome / Disorder). Curates a compound library across pharma, herbs, supplements, and AI-generated analogs; runs ligand-based virtual screening, covalent-warhead detection, ADMET QSAR, and multi-objective ranking across rescue / maintenance / remission categories. Every prediction is published openly so any researcher can audit, falsify, or extend it.",
  "version": "0.1.0",
  "url": "https://github.com/mrdulasolutions/MCAS.Opensource",
  "documentationUrl": "https://github.com/mrdulasolutions/MCAS.Opensource#readme",
  "iconUrl": "https://github.com/mrdulasolutions.png",
  "provider": {
    "organization": "MR Dula Medical",
    "legalEntity": "MR Dula Medical, a DBA of MR Dula Enterprise, LLC",
    "address": {
      "locality": "Raleigh",
      "region": "NC",
      "country": "USA"
    },
    "url": "https://github.com/mrdulasolutions",
    "contact": {
      "issuesUrl": "https://github.com/mrdulasolutions/MCAS.Opensource/issues",
      "securityUrl": "https://github.com/mrdulasolutions/MCAS.Opensource/blob/main/SECURITY.md",
      "codeOfConductUrl": "https://github.com/mrdulasolutions/MCAS.Opensource/blob/main/CODE_OF_CONDUCT.md"
    }
  },
  "license": {
    "spdxId": "MIT",
    "url": "https://github.com/mrdulasolutions/MCAS.Opensource/blob/main/LICENSE"
  },
  "disclaimers": {
    "medicalAdvice": "This agent generates research hypotheses only. It does not diagnose, treat, cure, or prevent any condition. It is not a substitute for clinical care. See https://github.com/mrdulasolutions/MCAS.Opensource/blob/main/docs/disclaimers.md.",
    "selfExperimentation": "Outputs of this agent must not be acted on for self-treatment. Patients should work with a mast-cell specialist."
  },
  "status": {
    "hostedEndpoint": "not_yet_available",
    "browseOnlyViewer": "https://huggingface.co/spaces/MRDula/openmcas-browser",
    "manifestPurpose": "Describes the conceptual A2A agent interface. The underlying skills are implemented as scripts in the repository and runnable on a CPU laptop in ~3 minutes. A read-only browse interface to current predictions is live as a public Hugging Face Space (browseOnlyViewer above). A hosted A2A endpoint is on the project roadmap (see ROADMAP.md)."
  },
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "stateTransitionHistory": false,
    "extensions": {
      "x-openmcas/repositoryBacked": true,
      "x-openmcas/runnableLocally": true,
      "x-openmcas/openLicense": "MIT",
      "x-openmcas/dataProvenance": "PubChem PUG-REST, AlphaFold DB, PyTDC, hand-curated"
    }
  },
  "authentication": {
    "schemes": ["public"]
  },
  "defaultInputModes": ["text/plain", "application/json"],
  "defaultOutputModes": ["text/plain", "application/json", "text/csv", "text/markdown"],
  "skills": [
    {
      "id": "library.search",
      "name": "Search the MCAS compound library",
      "description": "Search the curated MCAS/MCAD compound library by name, PubChem CID, category (rescue / maintenance / remission), target, mechanism, or evidence level. Returns matched entries with SMILES, mechanism, and evidence.",
      "tags": ["search", "library", "compounds", "cheminformatics"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json", "text/csv"],
      "examples": [
        "List all rescue compounds with high evidence",
        "Find compounds targeting KEAP1",
        "Get the SMILES for sulforaphane",
        "Which compounds in the library are mast cell stabilizers?"
      ]
    },
    {
      "id": "library.add_compound",
      "name": "Propose a new compound for the library",
      "description": "Propose a pharma drug, herb, supplement, or biologic for the curated library. Validates the PubChem CID, fetches the canonical SMILES, and opens a GitHub PR to data/compounds/seeds.json with the proposed entry.",
      "tags": ["contribute", "library", "compounds"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json"],
      "examples": [
        "Add azelastine to the library as a maintenance H1 antagonist",
        "Propose adding moringin (CID 73895174) under remission with KEAP1 target"
      ]
    },
    {
      "id": "triggers.report",
      "name": "Report an MCAS trigger pattern",
      "description": "Submit an anonymous trigger pattern (food / smell / physical / stress / infection / toxin / hormonal / medication) for inclusion in the trigger -> pathway -> counter-compound map. No PHI is collected; only the pattern + onset + duration + dominant symptom domain.",
      "tags": ["contribute", "triggers", "patient-reported", "anonymous"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json"],
      "examples": [
        "Report that perfumes trigger immediate skin flushing",
        "Log a trigger pattern for aged cheese and GI symptoms"
      ]
    },
    {
      "id": "screen.ligand_based",
      "name": "Ligand-based virtual screening against MCAS targets",
      "description": "Given one or more SMILES, compute max Tanimoto similarity (Morgan radius 2, 2048 bits) against curated reference ligand sets for 8 MCAS targets: MRGPRX2, KIT, KEAP1, HRH1, HRH2, CYSLTR1, BTK, GLP1R.",
      "tags": ["screening", "tanimoto", "targets", "in-silico"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json", "text/csv"],
      "examples": [
        "Score this SMILES against MRGPRX2 and KIT",
        "Run ligand-based screen for CS(=O)CCCCN=C=S across all targets"
      ]
    },
    {
      "id": "screen.warhead",
      "name": "Covalent warhead + KEAP1 pharmacophore scoring",
      "description": "Apply 13 cysteine-reactive SMARTS patterns (isothiocyanate, Michael acceptor, cyanoenone, acrylamide, vinyl sulfone, epoxide, quinone, etc.) plus a KEAP1 pharmacophore filter (MW, cLogP, heavy atoms). Returns warhead presence + KEAP1 pharmacophore pass + a [0,1] warhead score.",
      "tags": ["warhead", "covalent", "smarts", "keap1", "in-silico"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json", "text/csv"],
      "examples": [
        "Does this molecule have a cysteine-reactive warhead?",
        "Score this SMILES for KEAP1 pharmacophore"
      ]
    },
    {
      "id": "qsar.predict",
      "name": "ADMET QSAR prediction (hERG, AMES, BBB)",
      "description": "Predict cardiac liability (hERG), mutagenicity (AMES), and blood-brain barrier penetration (BBB_Martins) for a SMILES using RandomForest classifiers trained on PyTDC tasks (validation AUC 0.89-0.91).",
      "tags": ["qsar", "admet", "safety", "in-silico"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json"],
      "examples": [
        "Predict hERG / AMES / BBB for this SMILES",
        "Is this molecule predicted to cross the blood-brain barrier?"
      ]
    },
    {
      "id": "generate.sfn_analogs",
      "name": "Generate SFN-class candidate analogs",
      "description": "Enumerate candidate analogs of the sulforaphane class using RDKit BRICS recombination + bioisosteric substitution + warhead grafting onto library scaffolds. Seeds: SFN, iberin, erucin, sulforaphene, allyl-ITC, benzyl-ITC, phenethyl-ITC. Returns ranked SMILES with QED + similarity + Lipinski + composite score.",
      "tags": ["generative", "sfn", "isothiocyanate", "in-silico"],
      "inputModes": ["application/json"],
      "outputModes": ["application/json", "text/csv"],
      "examples": [
        "Generate SFN-class analogs from the current library",
        "Generate phenethyl-isothiocyanate analogs with phenyl tether variants"
      ]
    },
    {
      "id": "rank.hypotheses",
      "name": "Multi-objective ranking across rescue / maintenance / remission",
      "description": "Compute the composite hypothesis-ranking signal: 0.30 evidence + 0.35 weighted target similarity + 0.10 QED (generated only) + 0.10 warhead score + 0.15 safety bonus + small contextual BBB term. Returns ranked CSVs per category and auto-updates the Top-10 tables in hypotheses/*.md.",
      "tags": ["ranking", "composite", "hypothesis"],
      "inputModes": ["application/json"],
      "outputModes": ["application/json", "text/csv", "text/markdown"],
      "examples": [
        "Rank the current library + generated analogs for remission",
        "Show me the top 10 maintenance candidates with their safety profile"
      ]
    },
    {
      "id": "experiment.create_report",
      "name": "Create a standardized experiment report",
      "description": "Scaffold a new experiments/EXP-NNN-*.md report from the standard 11-section template, prefilled with hypothesis, method, inputs, parameters, environment, outputs, interpretation, reproduction, limitations, and next-experiment suggestions. Updates the experiments index.",
      "tags": ["experiment", "preregistration", "documentation"],
      "inputModes": ["application/json"],
      "outputModes": ["text/markdown"],
      "examples": [
        "Create an experiment report for a KEAP1 docking run on the top-50",
        "Scaffold EXP-006 for a ChEMBL mast cell bioassay import"
      ]
    },
    {
      "id": "hypothesis.propose",
      "name": "Propose or critique a hypothesis",
      "description": "Submit a falsifiable hypothesis (rescue / maintenance / remission / injury / trigger / cross-cutting) with falsifiable predictions, linked compounds and triggers, proposed in-silico tests, and proposed wet-lab tests. Opens a GitHub issue using the hypothesis_proposal template.",
      "tags": ["hypothesis", "contribute", "preregistration"],
      "inputModes": ["text/plain", "application/json"],
      "outputModes": ["application/json"],
      "examples": [
        "Propose that PEITC + SCFA synergistically restore the gut-mast axis",
        "Critique the SFN remission hypothesis with a falsifying experiment"
      ]
    }
  ]
}