Brain in the Fish — MCP Skill Guide

Description

--- 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 | Tool | Purpose | When to Call | |------|---------|-------------| | `eval_status` | Check server status and session state | First — verify server is running | | `eval_ingest` | Ingest a document (PDF/text) | Step 1 | | `eval_criteria` | Load evaluation framework | Step 2 | | `eval_align` | Align document sections to criteria | Step 3 | | `eval_spawn` | Generate evaluator agent panel | Step 4 | | `eval_scoring_tasks` | Get all scoring prompts for subagents | Step 5 | | `eval_score_prompt` | Get scoring prompt for one agent/criterion pair | Step 5 (per-task) | | `eval_record_score` | Record a score from an agent | Step 6 | | `eval_debate_status` | Check disagreements and convergence | Step 7 | | `eval_challenge_prompt` | Get challenge prompt for debate | Step 7 (per-challenge) | | `eval_report` | Generate final evaluation report | Step 8 | | `eval_whatif` | "What if" re-scoring with modified text | Optional | ## 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 ```json { "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 | Type | Intent Keywords | Framework Auto-Selected | |------|----------------|----------------------| | Academic essay | "essay", "mark", "grade", "coursework" | Academic Essay Marking | | Policy document | "policy", "green book", "impact assessment" | HM Treasury Green Book | | Survey/research | "survey", "methodology", "questionnaire" | Survey Methodology | | Contract/legal | "contract", "legal", "compliance" | Contract Review | | Clinical/NHS | "nhs", "clinical", "patient", "governance" | NHS Clinical Governance | | GCSE English | "gcse", "english language" | GCSE English Language | | Generic | anything else | Generic Quality | ## 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

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