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ck_outcome_tracker

Capture session outcomes and fetch performance leaderboards to close the reinforcement-learning loop, enabling data-driven 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
modeYesOperation mode that determines the tool behavior and return shape.
limitNoMaximum number of results to return.
windowNo
outcomeNoResult classification of the operation.
agent_idNo
task_typeNo
session_idNoUnique session identifier for correlating findings, proofs, budget, and audit trail.
workspace_idNoWorkspace identifier for cross-session scope.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
entriesNo
outcomesNo
recordedNo
Behavior4/5

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

Discloses write vs read-only modes and mentions impact on other tools. Annotations lack behavioral details, so description adds value. Could further mention idempotency or side effects but is sufficient given output schema presence.

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?

Concise and well-structured, breaking down modes with clear bullet points. Every sentence adds value without redundancy.

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?

Covers all essential aspects: three modes, required parameters per mode, usage timing, and integration with other tools. Output schema handles return values, so no further detail needed.

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

Parameters5/5

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

Adds significant meaning beyond schema by mapping parameters to specific modes (e.g., 'For record mode: pass...') and enumerating outcome values. Schema coverage is 63%, so description compensates well for undocumented parameters.

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?

Clearly defines three distinct modes (record, get_session, get_leaderboard) with specific verbs and resources. Distinguishes itself from siblings by focusing on outcomes and leaderboards, as evidenced by mention of feeding data to ck_route and ck_cost_optimizer.

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?

Provides explicit usage context: 'Call after task completion before ending the session' and explains downstream dependencies. However, does not directly contrast with alternatives among siblings or state when not to use.

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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