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Glama

memory_recall

Retrieve relevant agent memories using temporal reasoning, hybrid ranking, and graph traversal, while filtering outdated facts and prioritizing current state.

Instructions

Recall agent memories with temporal reasoning, hybrid ranking, and graph traversal.

Filters superseded historical facts and boosts current state. Optional temporal_filter: 'current_state', 'past_event', 'future_plan', 'preference', 'relationship'.

Input Schema

NameRequiredDescriptionDefault
queryYes
top_kNo
use_hybridNo
traverse_graphNo
temporal_filterNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.67

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It usefully discloses that the tool filters superseded historical facts, boosts current state, and supports temporal filtering, hybrid ranking, and graph traversal. However, it omits whether the operation is strictly read-only, permission requirements, and how the ranking or traversal options materially affect results.

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 concise and front-loaded, beginning with the core action and capabilities. The follow-up sentences add behavioral context and temporal_filter values without unnecessary repetition.

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?

This is a relatively complex recall tool with five parameters, no annotations, and 0% schema description coverage. The description covers high-level capabilities and temporal_filter values, but it leaves most parameter semantics and usage alternatives unexplained. The output schema helps with return values, but the input-side context remains incomplete.

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 description coverage is 0%, so the description must compensate for five parameters. It explains only temporal_filter by listing allowed values, while query, top_k, use_hybrid, and traverse_graph are not meaningfully described beyond their names or defaults.

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?

The description states a clear verb and resource: recall agent memories, with specific capabilities like temporal reasoning, hybrid ranking, and graph traversal. It is clear what the tool does, but it does not explicitly distinguish itself from related sibling tools such as recall_relevant, shared_memory_search, or memory_graph_query.

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

Usage Guidelines2/5

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

The description does not say when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. The temporal_filter categories imply some usage contexts, but there is no explicit guidance on selecting this tool over sibling memory and recall tools.

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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