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Glama

memory_juez

Resolve ambiguous memory conflicts by LLM-judging observation pairs in the 0.6-0.8 cosine band, where heuristics miss vocabulary-different contradictions. Judge IDs or scan entities; cache verdicts.

Instructions

[alias of cuba_juez] LLM-judge for semantically-conflicting observations in the ambiguous cosine-similarity band (0.6-0.8), where heuristics miss vocabulary-different conflicts. Verdicts are cached in brain_judgments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesjudge_pair: decide on two given observation ids. scan_entity: pull ambiguous pairs for an entity and judge each.
max_pairsNoMax pairs to escalate per call (default 5; controls LLM cost)
entity_nameNoEntity to scan (for scan_entity)
observation_aNoUUID of first observation (for judge_pair)
observation_bNoUUID of second observation (for judge_pair)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.25.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that verdicts are cached in brain_judgments and implies LLM cost via the judge role, but does not cover permissions, mutation semantics, or failure behavior.

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?

Two concise sentences with no wasted words. The alias and core purpose are front-loaded before the caching detail.

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?

No annotations and no output schema mean the description should do more to explain return values and side effects. It covers the judge’s scope and caching but leaves the output format and mutation implications unspecified.

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 the schema already documents all five parameters. The description adds no parameter-level meaning beyond what the schema provides, making the baseline 3 appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific function: an LLM judge for semantically conflicting observations in a defined cosine-similarity band. It also names the alias relationship with cuba_juez and distinguishes itself from heuristics, though it does not differentiate from all sibling memory tools.

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?

Clearly indicates when to use it: the ambiguous 0.6-0.8 cosine-similarity band where heuristics miss vocabulary-different conflicts. It implies heuristics as an alternative but does not explicitly list when-not or other alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.