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ArkaAiAdmin

Agentic Memory

by ArkaAiAdmin

memory_record_ctr_feedback

Record click-through rate feedback when a search result is shown to the user, used by the re-ranker to refine ranking over time.

Instructions

Record click-through rate feedback for a search result.

G4 fix (2026-06-22): memory_record_ctr_feedback (this tool) and memory_reinforce (mcp_memory.py) record two different signals on purpose — they are not interchangeable.

  • memory_record_ctr_feedback records the implicit signal: "the user saw this result in the response." Writes a row to ctr_feedback with action=returned/clicked/etc. The search re-ranker reads this table to adjust ranking over time. Use this when a search result is delivered to the user, regardless of whether the user does anything with it.

  • memory_reinforce records the explicit signal: "the user judged this memory useful (or not)." Updates success_score and recomputes fitness_score. Use this when a user acts on a memory — e.g. cites it in a lesson, marks a decision as right, or undoes a save because the memory was wrong. Skipping memory_reinforce on every "user saw it" event would over-credit the success score.

In short: record_ctr_feedback = "delivered to user", reinforce = "user acted on it positively". Call both when a user follows up on a search hit. Call only record_ctr_feedback when the user just sees the result. Call only reinforce when the success/failure signal comes from outside the search path (e.g. a downstream agent confirms the memory was correct).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
actionNoreturned
sourceNo
query_idYes
returned_atNo
ranking_paramsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description carries full burden. It explains the tool writes a row to ctr_feedback with action=returned/clicked/etc., and that the search re-ranker reads this table. It does not mention side effects like permissions or idempotency, but it covers the core behavior well.

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?

Despite length, every sentence adds value. Clear section labeling (G4 fix, bullet lists) and front-loaded with purpose. No redundancy.

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?

Tool is simple, and description provides good purpose and usage context. However, it lacks parameter details and does not explain what the tool returns (output schema exists but not described). With 6 parameters and 0% schema coverage, more parameter-level information is needed for full completeness.

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

Parameters2/5

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

Schema coverage is 0%, so description should explain parameters. It only briefly mentions 'action=returned/clicked/etc.' and implies 'id' and 'query_id' are required, but does not describe 'source', 'returned_at', or 'ranking_params'. Agent would have to infer or guess their meaning.

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?

Description clearly states the tool records click-through rate feedback for a search result. It distinguishes itself from memory_reinforce by explaining the difference between implicit and explicit signals, making the purpose unmistakable.

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

Usage Guidelines5/5

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

Provides explicit guidance on when to use this tool vs. memory_reinforce, including concrete scenarios like 'delivered to user', 'user acted on it', and 'call both when...'. No ambiguity.

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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