Experiment Protocol

Experiment Protocol

Drives the experiment-registry MCP server through a controlled experiment lifecycle. The MCP owns the state machine, validates artefacts, and enforces methodology. This skill is the caller — not the logic.

Core Problem

Uncontrolled testing contaminates results. The most common failure mode is embedding success criteria inside the input the subject under test receives — this measures instruction-following ability, not the quality of the instructions themselves. A second failure mode is changing multiple variables between runs, which makes it impossible to attribute any result to any cause. The third is writing scoring criteria after seeing output, which lets expected results shape the rubric rather than the other way around.

The experiment-registry MCP server enforces the correct protocol mechanically. Claude's role is to produce artefacts and submit them — not to manage the workflow.

Phase 1 — Setup (collaborative)

Work with the user to identify the experiment type before starting the execution loop.

flowchart TD
    Infer[Infer domain from current task context] --> List["Call list_experiment_types()"]
    List --> BestMatch[Identify best-matching type from descriptions]
    BestMatch --> Inspect["Call inspect_experiment_type(name)"]
    Inspect --> Propose[Propose type and first-step requirements to user]
    Propose --> Q{User accepts?}
    Q -->|Yes| Start["Call start_experiment(base, context, extensions)"]
    Q -->|Adjust| Adjust[User specifies different base or inline extensions]
    Adjust --> Start
    Start --> Ready[Receive experiment ID and first step — enter Phase 2]

The extensions parameter is optional. Pass it when the user specifies additions to the base type (e.g., extra checklist items or artefacts not in the registry definition).

Phase 2 — Execution (mechanical, MCP-driven)

No discussion during execution. Step through the MCP workflow autonomously.

flowchart TD
    GetStep["Call get_current_step(experiment_id)"] --> TermCheck{status is complete<br>or inconclusive?}
    TermCheck -->|Yes| Handoff[Experiment already done — see Retrospective Handoff]
    TermCheck -->|No| StepDetail[MCP returns step + checklist + required artefacts]
    StepDetail --> Human{REQUIRES_HUMAN_INPUT flagged?}
    Human -->|Yes| Surface[Surface the question to the user and wait for answer]
    Surface --> Resubmit[Include answer in artefacts and resubmit]
    Human -->|No| Produce[Produce the required artefacts]
    Produce --> Complete["Call complete_step(experiment_id, step_id, artefacts)"]
    Resubmit --> Complete
    Complete --> MCPResult{MCP response?}
    MCPResult -->|Missing artefacts| Fix[Produce the missing artefacts and resubmit]
    MCPResult -->|Validation errors| FixV[Fix validation issues and resubmit]
    Fix --> Complete
    FixV --> Complete
    MCPResult -->|Next step| GetStep
    MCPResult -->|complete| HandoffC[Experiment complete — see Retrospective Handoff]
    MCPResult -->|inconclusive| Report[Report iteration limit reached — summarise what changed]

The MCP advances state, validates artefact presence, and determines when the experiment is done. Do not attempt to track or infer step state from memory.

Read-Only Status

When the user calls /experiment-protocol status {id}, call get_current_step(experiment_id) and display the result without calling complete_step(). This does not interrupt or advance the execution loop.

Anti-Patterns

The MCP enforces these mechanically, but understanding why they are prohibited helps produce correct artefacts.

Embedding criteria in the input artefact — writing expected outcomes or scoring hints inside the fixture or input the subject receives. This tests instruction-following, not instruction quality. The rubric and fixture are separate artefacts for this reason.

Changing multiple things between iterations — if two things change simultaneously, the result cannot be attributed to either. The MCP enforces one-change-per-iteration via the iterate step.

Writing rubric criteria after seeing output — post-hoc criteria are shaped by what the subject produced. The MCP requires rubric artefacts before the baseline step runs.

Reporting only passing runs — every iteration is recorded. The MCP log captures all runs, including regressions.

Changing the control input between iterations — the task prompt, fixture, and baseline conditions are frozen after the baseline run. Changing them starts a new experiment.

Scoring by impression — every criterion is binary. Call get_current_step() to retrieve the rubric and score each criterion explicitly for each run.

Retrospective Handoff

When the MCP returns complete or inconclusive status:

1. Call get_experiment_summary(experiment_id) — returns artefact file paths and final status.
2. Pass the file paths to @retrospective-analyst for post-experiment analysis.

The analyst reads artefacts directly from disk. No reformatting or summarisation required.