Edison Experimentation Prover MCP Connector for Claude
A+A team chose paper filing because 'best practice.' No pilot. No alternatives tested. 8 months later, 14,000 submissions/day — 47 hours behind on retrieval. Emergency migration under pressure. Cost: 3x the estimate, 6 weeks frozen. Edison tested 3,000+ filament materials before carbonized bamboo — he did not pick the 'obvious choice.' This tool forces experimentation: test alternatives with measured criteria, iterate beyond the first solution, build the ecosystem, prove viability under real conditions, and document dead ends.
AI agents pick the first solution that comes to mind and call it done. They do not test alternatives. They do not iterate. They build the feature but forget the ecosystem. They claim 'it works' without real-world evidence. And they never document the dead ends.
The Problem
LLMs commit five experimentation failures:
- Experiment Absent — 'The best approach is the traditional method.' Based on what? Which alternatives were evaluated? With what criteria? What did each score? Edison tested 3,000+ filament materials — platinum, carbon, bamboo, horsehair, fishing line, cork, celluloid — before finding carbonized bamboo from Kyoto lasted 1,200 hours. 'Best practice says' is not an experiment.
- Iteration Insufficient — 'It works, let us ship it.' The first working version is rarely the best. Edison ran 10,000+ experiments on the storage battery. What variations did you test? What improved? Where did returns diminish? 'Good enough' means you stopped experimenting too early.
- System Incomplete — 'We built the verification module. Rollout is someone else's problem.' Edison did not just build the light bulb — he built generators, cables, switches, meters, fuses, AND sockets. The bulb without the power grid is a curiosity, not a product. Is your solution deployable? Monitored? Documented? Does a user know how to go from zero to using it?
- Viability Unproven — 'The design is operationally sound.' Theoretically. Under what volume? At what cost? With what adoption friction? Edison required every invention to be commercially viable within 6 months. 'Works in our pilot' is not viability evidence.
- Failure Undocumented — 'We tried some things and settled on this approach.' Which things? What happened with each? Why did they fail? What did you learn? Edison kept 3,500+ notebooks: 'Experiment #247: carbonized cedar — lasted 38 minutes, crumbled at high temperature.' THAT is documentation.
How It Works
5 Decision Pivots following Edison's methodology:
- experimentConducted — Alternatives tested with measured criteria and results.
- iterationSufficient — Variations tested beyond first working solution to diminishing returns.
- systemComplete — Ecosystem built: rollout plan, monitoring, documentation, transition, onboarding.
- viabilityProven — Real-world evidence, operating cost, adoption friction, success metrics.
- failureDocumented — Each dead end: approach, measured result, root cause, learning.
The Verdict Matrix
| First Failing Pivot | Verdict | Meaning |
|---|---|---|
| experimentConducted = false | EXPERIMENT_ABSENT | First idea accepted without testing. |
| iterationSufficient = false | ITERATION_INSUFFICIENT | Stopped at first working solution. |
| systemComplete = false | SYSTEM_INCOMPLETE | Built the bulb, forgot the grid. |
| viabilityProven = false | VIABILITY_UNPROVEN | "Should work" — no real evidence. |
| failureDocumented = false | FAILURE_UNDOCUMENTED | "We tried some things" — no record. |
| All pivots pass | EXPERIMENT_PROVEN | Tested. Iterated. Complete. Viable. Documented. |
Related Connectors
Database Architect Prover MCP
An AI agent designed a database schema with no indexes on search columns, no foreign keys, and a VARCHAR(255) for every field. Query response time went from 200ms to 14 seconds after 500K rows. Table locks during updates froze the application for 8 minutes. This tool forces 3NF normalization, index strategies mapped to access patterns, explicit foreign key constraints, and partition planning for high-volume tables.
Technical Writing Prover MCP
An AI wrote API documentation for 'developers.' No expertise level. No prerequisites. A wall of text with no headings. Code examples that referenced a deprecated method — untested. Passive voice throughout: 'it is recommended that the configuration be updated.' A junior engineer followed the docs, deployed to production with the wrong config, and caused a 4-hour outage. This tool forces audience definition, task-based structure, tested examples, ambiguity elimination, and completeness verification.
Copernicus Perspective Prover MCP
Your AI analyzed the problem from the default perspective and added workarounds when it did not fit. That is an epicycle — not a solution. Copernicus did not add more epicycles to Ptolemy's model. He moved the center from Earth to Sun. 40+ epicycles vanished. This tool forces default questioning, epicycle counting, alternative framing, observer shifting, and simplicity comparison.
Yakunashi-Safety Gate MCP
LLMs hallucinate confidently when context is missing. This tool enforces epistemic calibration: map required preconditions, audit information sufficiency, detect speculation (yakunashi), and trigger safe folding (Beta-Ori) when data is missing.