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plur_report_failure

Report a failure of a procedural engram to trigger automatic procedure improvement using an LLM. Describe what went wrong to help evolve the procedure.

Instructions

Report a failure for a procedural engram — triggers procedure evolution via LLM if configured. Only works on procedural engrams. Max 3 revisions per procedure per 24h.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
engram_idYesID of the procedural engram that failed
llm_modelNoModel name (default: gpt-4o-mini)
llm_api_keyNoAPI key for the LLM
llm_base_urlNoOpenAI-compatible API base URL for procedure evolution
failure_contextYesDescription of what went wrong when following this procedure
Behavior4/5

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

Annotations provide idempotentHint=false and destructiveHint=false. The description adds that failure reporting triggers evolution via LLM if configured, and imposes a revision limit, which are important behavioral traits beyond the annotations.

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?

Three sentences, front-loaded with the core purpose. No wasted words; every sentence adds value.

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?

No output schema, but the description explains the outcome (triggers evolution) and constraints. The tool is simple enough that this is sufficient.

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 description coverage is 100% with clear parameter descriptions. The tool description adds context about the LLM-triggered evolution, giving extra meaning to the llm_* parameters. This goes beyond the baseline of 3.

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 action (report failure), the target (procedural engram), and the effect (triggers procedure evolution via LLM). It distinguishes from sibling tools like 'plur_feedback' by specifying the procedural engram requirement.

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?

The description specifies that the tool only works on procedural engrams and includes a rate limit (max 3 revisions per procedure per 24h). It does not explicitly state when not to use it or list alternatives, but the context is clear enough for an agent.

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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