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recall

Read-only

Search stored long-term memories before answering to retrieve relevant facts, preferences, and past events, so responses reflect what was previously said instead of guesses or forgotten details.

Instructions

Look things up before you answer. Call it whenever the person mentions anything from before (a person, pet, place, plan, date, preference, promise, something you did together, "remember when", "last time", "like I said", 还记得, 上次, 之前说过), and whenever you are about to guess, generalize about them, or ask something you may already know. Never say you don't remember before calling it. Returns the most relevant memories with ids (pass an id to remember's replaces or to forget) plus matching private words, as { data: { hits, glossary_hits, week_blocks, meta } }. It changes no memory; it only notes which ones were recalled, which keeps them from expiring.

Input Schema

NameRequiredDescriptionDefault
kNoMaximum number of memory hits. Defaults to 20.
queryYesWhat to recall, in the person's words or your own.
typesNoOnly recall these memory types (fact, event, preference, relationship, boundary, habit, decision, note). Omit for all types.
min_scoreNoRelevance floor after reranking. Defaults to the server setting (0.15); lower it to see more.
namespaceNoMemory space to use. Defaults to 'default'. Ignored when the API key is bound to a fixed namespace.
include_historyNoWhen true, also return older versions that were replaced. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses the exact return shape ({ data: { hits, glossary_hits, week_blocks, meta } }) and an important non-obvious side effect: it changes no memory but notes which ones were recalled, preventing expiration. That is precisely the behavioral context an agent needs.

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 front-loaded with the trigger conditions, then the return shape, then the behavioral note. Despite being dense, every sentence earns its place by covering usage, output, and side effects without repetition.

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

Completeness5/5

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

For a retrieval tool with no output schema, the description fully covers what it returns, when to call it, and its non-mutating but recency-affecting behavior. Nothing an agent needs in order to call it correctly is missing.

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 coverage is 100%, so the schema already documents all six parameters thoroughly. The description adds context about output ids and their downstream use with remember/forget, but it does not add 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.

Purpose5/5

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

The description states a specific retrieval action ('Look things up') over a specific resource ('memories') and clarifies the output is the most relevant memories with ids. It also distinguishes its role from mutating siblings by explaining ids are passed to remember's replaces or forget.

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?

It gives unusually clear triggering conditions: when the person references anything from before, with concrete examples and multilingual cues, plus when the agent is about to guess or ask something it may already know. It adds a strong 'Never say you don't remember before calling it' rule, but does not explicitly distinguish when to use this versus list_memories or state exclusions.

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