{
  "schema": "oc-mcp/v1",
  "tool": "getProjects",
  "data": {
    "note": "Flagship systems Omar Corral has designed and built. Architecture is described conceptually; no confidential client data or proprietary infrastructure detail is exposed.",
    "projects": [
      {
        "id": "audience-simulation",
        "name": "Audience Simulation",
        "categories": [
          "Applied AI",
          "Decision Intelligence"
        ],
        "summary": "A multi-stage audience simulation engine that evaluates marketing concepts through configurable persona archetypes, propagates reactions through influence relationships, and compares the resulting signals against institutional benchmarks. Framed as a decision-support system, not a persona generator.",
        "architecture": [
          "Parse",
          "Target",
          "React",
          "Propagate",
          "Deliberate",
          "Analyze"
        ],
        "characteristics": [
          "Configurable persona architecture with structured persona data",
          "Model routing across reaction and deliberation stages",
          "Influence graph propagating reactions between archetypes",
          "Population weighting so aggregates reflect a real audience mix",
          "Warehouse enrichment and calibration against observed outcomes",
          "Prediction validation and operational advisories"
        ],
        "structuralFacts": {
          "personaArchetypes": 63,
          "structuredDimensionsPerPersona": 51,
          "influenceRelationships": 84,
          "pipelineStages": 6,
          "calibrationCorpus": {
            "records": 923000,
            "description": "Historical institutional inquiry records informing benchmarks and calibration.",
            "clarification": "These are reference records used for benchmarking and calibration. They are not model training data and do not represent people whose data trained an AI model."
          }
        }
      },
      {
        "id": "program-market-intelligence",
        "name": "Program Market Intelligence",
        "categories": [
          "Decision Intelligence",
          "Data Systems"
        ],
        "summary": "Connects fragmented signals into a single intelligence layer supporting academic program portfolio strategy — which programs to grow, hold, reposition or retire. Described conceptually; internal infrastructure and data are not exposed.",
        "architecture": [
          "Internal data + External signals + AI analysis + Business rules",
          "Decision intelligence"
        ],
        "signalTypes": [
          "labor-market demand",
          "skills demand",
          "enrollment",
          "student demand",
          "geographic demand",
          "competitor completions",
          "program performance",
          "student value",
          "institutional context"
        ],
        "characteristics": [
          "Cross-system entity reconciliation across sources that disagree",
          "Benchmarking against external market signals",
          "Business rules applied as an explicit layer, not hidden in a model",
          "Uncertainty surfaced rather than smoothed away"
        ]
      },
      {
        "id": "agent-ready-website",
        "name": "Agent-Ready Website (WebMCP)",
        "categories": [
          "Agent Architecture"
        ],
        "summary": "omar-corral.com gives AI agents structured access to information through a discovery manifest and typed tools, rather than requiring them to reconstruct identity from presentation-layer HTML. This endpoint is itself part of that architecture.",
        "architecture": {
          "traditional": [
            "Agent",
            "HTML",
            "Scrape",
            "Parse",
            "Guess"
          ],
          "agentReady": [
            "Agent",
            "Discover tools",
            "Request structured information",
            "Typed response"
          ]
        },
        "characteristics": [
          "Discovery manifest at /.well-known/webmcp.json",
          "Typed, schema-versioned JSON tool endpoints under /data/",
          "Browser tool registration via navigator.modelContext.registerTool()",
          "llms.txt and link relations routing agents to the structured path",
          "Machine-readable identity kept in sync with the visible site"
        ],
        "links": {
          "architectureBrief": "https://omar-corral.com/oc-mcp/",
          "implementationGuide": "https://omar-corral.com/seo-resources/docs/ai-search/webmcp-implementation",
          "manifest": "https://omar-corral.com/.well-known/webmcp.json"
        }
      },
      {
        "id": "entity-research-architecture",
        "name": "Entity Research Architecture",
        "categories": [
          "Applied AI",
          "Structured Knowledge"
        ],
        "summary": "A multi-agent research workflow built around factual grounding rather than content generation. Each stage narrows what the next stage may assert: entities are researched, claims are verified against sources, and only what survives verification reaches the brief.",
        "architecture": [
          "Entity Research",
          "Verification",
          "Content Brief",
          "Draft",
          "QA"
        ],
        "characteristics": [
          "Bounded workflow — plan, call verified tools, produce evidence-backed output",
          "Verification stage separate from the research stage",
          "Human review before anything ships",
          "Auditable intermediate artifacts at every step"
        ],
        "links": {
          "guide": "https://omar-corral.com/seo-resources/docs/ai-search/seo-agents"
        }
      }
    ]
  },
  "generated": "2026-08-25",
  "ttl": 604800,
  "_agent": {
    "note": "This is 1 of 7 structured tool endpoints. Discover all tools at the manifest.",
    "manifest": "https://omar-corral.com/.well-known/webmcp.json",
    "allTools": [
      "getProfile",
      "getProjects",
      "getServices",
      "getCaseStudies",
      "getSEOResources",
      "getContact",
      "getInsights"
    ]
  }
}