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session_save_experience

Record structured behavioral events—corrections, successes, failures, learning, or validation results—for pattern detection and analysis of agent performance.

Instructions

Record a typed experience event. Unlike session_save_ledger (flat logs), this captures structured behavioral data for pattern detection.

Event Types:

  • correction: Agent was corrected by user

  • success: Task completed successfully

  • failure: Task failed

  • learning: New knowledge acquired

  • validation_result: Verification sandbox passed or failed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoOptional. Agent role for Hivemind scoping. Omit to let the server auto-resolve from dashboard settings.
actionYesWhat action was tried.
contextYesWhat the agent was doing when the event occurred.
outcomeYesWhat happened as a result.
projectYesProject identifier.
correctionNoWhat should have been done instead (for correction type).
event_typeYesType of behavioral event.
confidence_scoreNoAgent's confidence in the outcome (1-100).
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It explains the nature of events but omits details on side effects, authentication needs, idempotency, or error handling, which are important for a mutation tool.

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?

Very concise with a clear structure: a title, a contrast sentence, and a succinct bullet list of event types. No superfluous content.

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

Completeness3/5

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

Given 8 parameters (5 required) and no output schema, the description adequately explains purpose and differentiation but fails to mention what the tool returns or any post-conditions, leaving some gaps.

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?

With 100% schema description coverage, baseline is 3. The description adds value by elaborating on event types and their meaning, supplementing the 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 the tool records typed experience events and explicitly distinguishes it from the sibling tool 'session_save_ledger' by contrasting flat logs with structured behavioral data.

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 clear context on when to use this tool (for structured behavioral data and pattern detection) and contrasts it with a specific sibling, implying when not to use it. It lacks explicit exclusion criteria for other scenarios.

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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