name: brain-in-the-fish description: Universal document evaluation engine — evaluate any document against any criteria using cognitively-modelled AI agents with ontology-grounded scoring version: 0.1.0

Brain in the Fish — MCP Skill Guide

What This Does

Brain in the Fish evaluates documents (essays, policies, contracts, clinical reports, surveys) against evaluation criteria using a panel of AI agents. Each agent's mental state exists as OWL ontology. Scoring is grounded in an Evidence Density Scorer (EDS) that makes hallucination mathematically detectable.

MCP Tools Available

Evaluation Workflow

Quick Mode (deterministic, no subagents needed)

eval_ingest → eval_criteria → eval_align → eval_spawn → eval_report

The server runs evidence scoring internally. eval_report produces a complete evaluation with deterministic scores.

Full Mode (with Claude subagent scoring)

1. eval_ingest(path, intent)
2. eval_criteria(framework_or_intent)
3. eval_align()
4. eval_spawn(intent)
5. eval_scoring_tasks() → get all tasks
6. For each task:
   - Read the scoring prompt
   - Evaluate the document content against the criterion as the agent persona
   - eval_record_score(agent_id, criterion_id, score, justification, evidence, gaps)
7. eval_debate_status() → check for disagreements
8. If disagreements:
   - eval_challenge_prompt(challenger, target, criterion)
   - Generate challenge argument
   - eval_record_score() with revised score
   - Repeat until converged
9. eval_report() → final report

Subagent Dispatch Pattern

When orchestrating with multiple Claude subagents:

Orchestrator reads eval_scoring_tasks()
  → For each agent in the panel:
      Dispatch subagent with system prompt from eval_scoring_tasks
      Subagent receives: persona, criteria, document sections
      Subagent calls eval_record_score with their assessment
  → After all scores recorded:
      Check eval_debate_status
      If disagreements: dispatch challenge subagents
  → eval_report for final output

Scoring Guidelines for Subagents

When scoring as an agent persona:

1. Read the document content provided in the scoring prompt carefully
2. Reference the rubric levels — state which level the document meets
3. Cite specific evidence from the document text (quote directly)
4. Identify gaps — what's missing that would improve the score
5. Be the persona — a Subject Expert scores differently from a Writing Specialist
6. Do not hallucinate — only reference evidence that appears in the provided text
7. Use the full scale — don't cluster all scores at 6-8. Use 1-10 range appropriately.

Response Format for eval_record_score

{
  "agent_id": "from the scoring task",
  "criterion_id": "from the scoring task",
  "score": 7.5,
  "max_score": 10.0,
  "round": 1,
  "justification": "Detailed justification referencing specific document content and rubric levels. This section meets Level 3 (score range 6-8) because it demonstrates [specific evidence]. To reach Level 4, the document would need [specific improvement].",
  "evidence_used": ["Direct quote from document", "Another quote"],
  "gaps_identified": ["Missing topic X", "No counter-argument for claim Y"]
}

Supported Document Types

Architecture Notes

• Three ontologies coexist in one Oxigraph triple store: Document, Criteria, Agent
• Evidence scorer provides deterministic evidence-grounded scoring baseline
• Validation signals (citations, structure, reading level, fallacies, hedging) feed into the scorer as spikes
• Epistemic state tracks justified beliefs with empirical/normative/testimonial bases
• Philosophical analysis applies Kantian/utilitarian/virtue ethics lenses
• Belief dynamics — Maslow needs update based on findings, trust evolves during debate
• Cross-evaluation memory persists results for historical comparison
• All triples are queryable via SPARQL through the underlying onto_* tools