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Mnemoq
by Mnemoq

evaluate_prompt

Evaluates structured prompt summaries to detect learnable moments. Auto-logs high-confidence signals and suggests improvements for medium-confidence ones.

Instructions

Evaluate a structured prompt summary for learnable moments. Runs heuristic detectors on the summary, auto-logs high-confidence signals, and returns suggestions for medium-confidence ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepYesCurrent plan step number
textNoSalient gist of the interaction
outcomeYesOutcome category of the prompt/response cycle
componentsYesComponents involved in the interaction
error_textNoError message if outcome is bug_fixed (optional)
prompt_typeYesWho issued the prompt being evaluated
files_touchedYesFiles modified or discussed
rejected_actionNoWhat the human said not to do
corrected_actionNoWhat the human said to do instead
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions auto-logging and returning suggestions but does not disclose side effects (e.g., data modification), authorization needs, or rate limits. The auto-logging behavior is stated but not elaborated.

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?

The description is extremely concise, using one sentence (two clauses) to convey purpose and mechanism. It is front-loaded with the main verb and resource, and every phrase adds value.

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

Completeness2/5

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

Despite 9 parameters and no output schema, the description lacks details about return structure (suggestions) and the auto-logging effect. The agent is left unsure of what the tool returns or whether it modifies state, making it incomplete for a complex tool.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema; it only describes high-level behavior without elaborating on any specific parameter.

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 verb 'evaluate' and the resource 'structured prompt summary' with specific purpose 'for learnable moments'. It distinguishes from siblings like capture_interaction and log_learning by focusing on heuristic detection and suggestion generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage after a prompt summary is created but does not explicitly state when to use this tool versus alternatives like log_learning or retrieve_learnings. No exclusions or context are provided.

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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