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ck_outcome_tracker

Record session outcomes and retrieve agent performance leaderboards to close the reinforcement-learning feedback loop. Use after task completion to provide fresh data for routing decisions.

Instructions

Record session outcomes or retrieve agent performance leaderboards to close the reinforcement-learning feedback loop. Three modes: record persists a session outcome (write operation); get_session reads a specific outcome by session_id (read-only); get_leaderboard returns ranked agent performance (read-only). For record mode: pass session_id, outcome (success/partial/failure), agent_id, and task_type. For get_leaderboard: pass workspace_id and optional window (days) and limit. Call after task completion before ending the session so ck_route and ck_cost_optimizer have fresh performance data for future routing decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idNo
limitNoMaximum number of results to return.
modeYesOperation mode that determines the tool behavior and return shape.
outcomeNoResult classification of the operation.
session_idNoUnique session identifier for correlating findings, proofs, budget, and audit trail.
task_typeNo
windowNo
workspace_idNoWorkspace identifier for cross-session scope.
Behavior4/5

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

No annotations are provided, so the description carries full burden. It identifies record as a write operation and get_session/get_leaderboard as read-only, and explains data freshness implications. Could be more specific about error handling or idempotency.

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

Conciseness5/5

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

Two sentences with clear structure: first sentence states main purpose, second sentence provides mode-specific details and usage timing. No superfluous text.

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?

Given 8 parameters, no output schema, and no annotations, the description covers the three modes, parameter grouping, and a usage timing hint. Could mention return format or error handling for completeness.

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?

The description groups parameters by mode (e.g., 'for record mode: pass session_id, outcome...') and clarifies the outcome enum values (success/partial/failure), adding value beyond the input schema.

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 clearly states that the tool records session outcomes or retrieves leaderboards, with three distinct modes (record, get_session, get_leaderboard) and specific verbs per mode.

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 provides explicit guidance on when to call (after task completion before ending session) and which parameters to pass per mode. However, it does not explicitly state when not to use or compare to sibling tools.

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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