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Export Protocol Stats

protocol.export_stats
Read-onlyIdempotent

Paired pre/post analysis for one assignment's recorded sessions: per-block aggregates (descriptive + CSV), paired t / Wilcoxon / dz (per-statistic minimums: paired t and Cohen's dz need ≥ 3 completed sessions, Wilcoxon needs ≥ 5), specificity notes, the adaptive-threshold trajectory, and a pre/post figure (base64 PNG — render or save it to look at the effect). An assignment with no recordings yet returns the empty payload with a note, never an error.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "assignment_id": "<assignment_id>"
    },
    "name": "protocol.export_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
assignment_idYesThe assignment to analyze (from start_assignment).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the readOnly/idempotent annotations by disclosing the empty-payload-with-note behavior instead of an error, the base64 PNG needing the agent to render or save it, and the sample-size thresholds per statistic. These are exactly the operational traits an agent needs before invoking a stats export.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the purpose, then the outputs, then the minimums, then the empty-result caveat — a sensible information order with no filler. It is dense with parenthetical lists that take a second pass to parse, which keeps it just below the top score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only analysis tool with an output schema, the description covers everything material: what is computed, the minimum data requirements, the degenerate empty case, and how to handle the embedded image. Nothing needed to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single parameter already documents itself as coming 'from start_assignment'. The description reinforces scope ('one assignment's recorded sessions') but adds no new naming, format, or edge-case semantics for assignment_id, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb and analysis type ('Paired pre/post analysis for one assignment's recorded sessions') and enumerates the deliverables: per-block aggregates, paired t/Wilcoxon/dz, specificity notes, threshold trajectory, and a figure. An agent can distinguish this statistical analysis export from siblings like stats.grouped or protocol.session_status without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear applicability context by embedding per-statistic minimums (paired t and dz need >= 3 completed sessions, Wilcoxon >= 5), which tells the agent when results will be meaningful. It does not explicitly name alternatives or exclusion cases, so it stops short of a full when/when-not map.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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