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memory_recall

Search stored long-term memories using vector and keyword retrieval to recover user preferences, past decisions, or previously discussed topics across sessions.

Instructions

Search through long-term memories using hybrid retrieval (vector + keyword search). Use when you need context about user preferences, past decisions, or previously discussed topics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default: 5, max: 20)
queryYesSearch query for finding relevant memories
scopeNoSpecific memory scope to search in (optional)
sinceNoTime range filter. Use shorthand like "3d" (3 days), "1w" (1 week), "2h" (2 hours), or ISO timestamp.
topicNoFilter by topic label (e.g. "remotion", "invoice"). Only returns memories tagged with this topic.
queriesNoMultiple search queries in one call. Results are merged and deduplicated. Memories matching multiple queries rank higher. Use instead of calling memory_recall multiple times.
summaryNoWhen true, returns a topic-grouped overview (topic name, count, latest date, preview) instead of individual memories. Use a broad query like "project decisions" or "recent work" to scan your memory space, then follow up with a normal recall on a specific topic to drill in.
categoryNoFilter by category

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.33

TDQS

A3.6/5.0
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 the retrieval method (hybrid vector + keyword), which is useful, but says nothing about read-only/non-destructive nature, result ordering, or limits beyond what the schema states. Read-only is only implied by 'Search'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with no waste, front-loading the retrieval mechanism before the usage trigger. Appropriately sized for the tool's complexity.

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?

With eight parameters and no output schema, the description is somewhat thin. It does not describe the return shape, and while the schema richly documents individual parameters (including the summary overview mode), the description itself adds limited context for such a feature-rich recall 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 the schema already documents all eight parameters in detail. The description adds no additional parameter semantics beyond the schema. Baseline 3 applies when structured fields do the heavy lifting.

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 verb (Search) and resource (long-term memories) plus the retrieval mechanism (hybrid vector + keyword). It is clearly distinct from siblings like memory_store, memory_forget, and memory_update, though it does not name them explicitly.

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?

Gives concrete trigger conditions: user preferences, past decisions, or previously discussed topics. It lacks explicit when-not guidance or direct routing to siblings such as memory_history, but the context of use is unambiguous.

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