| 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:
- Read the document content provided in the scoring prompt carefully
- Reference the rubric levels — state which level the document meets
- Cite specific evidence from the document text (quote directly)
- Identify gaps — what's missing that would improve the score
- Be the persona — a Subject Expert scores differently from a Writing Specialist
- Do not hallucinate — only reference evidence that appears in the provided text
- 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
| 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
