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log_prediction

Record a prediction when citing a specific pack step, capturing the recommended action, expected signal, and deadline to enable later outcome verification.

Instructions

Log a pack-grounded prediction. REQUIRED whenever the assistant cites a specific relative_day of an installed experience pack as the basis for a real-world action recommendation. Captures: which step was cited, which case it applies to, what was recommended (and what was explicitly NOT recommended), the observable signal that resolves the prediction, and the window in days. Returns prediction_id for later log_outcome.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoOptional free-text context
case_idNoExternal reference for this case (CRM lead_id, ticket ID, etc.) — opaque string
pack_idNoThe pack's slug (e.g. 'inbound-acquisition-with-free-pilot')
client_idNo[deprecated alias for case_id, accepted for backward compat]
cited_stepNoThe exact relative_day cited (e.g. 'day +57')
confidenceNo[deprecated, removed from required schema 2026-04-27 — Claude confidence is uncalibrated until ≥30 outcome datapoints. Accepted for backward compat.]
predictionNo[deprecated] Free-text prediction. Use applied_action + expected_signal instead. Accepted for backward compat.
pack_authorNoThe pack's author handle (e.g. 'ivan-pasichnyk')
applied_actionNoWhat the assistant recommended TO do, derived from the cited step
expected_signalNoObservable signal that resolves this prediction (e.g. 'counterparty signs both sides')
memory_ids_usedNoMemory IDs that were retrieved for this prediction (recorded as data)
strategic_valueNo[deprecated, accepted for backward compat]
prevented_actionNoWhat the assistant recommended NOT to do (negative-space prediction). Optional but encouraged — often the higher-value half.
expected_window_daysNoDeadline in days for log_outcome to be called

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.0.0
    • changedInput schema / properties / memory_ids_used / description
      Previous value: -"Memory IDs that were retrieved for this prediction (for legacy Q-value updates on log_outcome)"New value: +"Memory IDs that were retrieved for this prediction (recorded as data)"
  2. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden. It discloses that this is a logging operation, captures the prediction details, and returns a prediction_id. It also transparently explains deprecated fields (confidence, prediction, strategic_value) and the rationale for confidence deprecation, which adds behavioral context beyond a simple 'logs prediction.'

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?

The description is a single, well-structured paragraph that front-loads the purpose and requirement, then lists captured fields without redundancy. Each sentence contributes value, and the deprecated-field notes are appropriately placed in the schema rather than bloating the description.

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

Completeness4/5

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

For a tool with 14 optional parameters and no output schema, the description covers the when, what, and return value (prediction_id). It doesn't detail error handling or edge cases, but given the logging nature and the explicit trigger, it provides sufficient context for correct invocation.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents every parameter. The description adds semantic nuance by explaining the trigger (relative_day citation), emphasizing that prevented_action is 'often the higher-value half,' and clarifying the deprecated confidence field's uncalibrated nature. This goes beyond the schema's descriptions.

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?

The description opens with a specific verb and object: 'Log a pack-grounded prediction.' It goes further to state the exact trigger condition ('REQUIRED whenever the assistant cites a specific relative_day...') and enumerates the data captured, which distinguishes it from the sibling log_outcome by clarifying it records the prediction itself, not the outcome.

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?

It explicitly states when the tool is required (when citing a relative_day as basis for a recommendation) and implicitly routes the next step via 'Returns prediction_id for later log_outcome.' It does not enumerate exclusions or alternative tools, but the requirement framing is unambiguous.

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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