Ontonym

Recall — brief me on this memory

recall

Read-onlyIdempotent

CALL THIS FIRST, before working in a memory — it is the briefing.

One composite read that answers what a cold agent actually needs: what
this memory holds (classes and counts), what landed recently, where the
memory contradicts itself, how much is waiting for a human's approval,
and the newest session checkpoint — where the last session stopped.

`memory` is the slug from list_my_memories. `topic` additionally runs the
hybrid search and inlines the hits, so "brief me, and specifically about
pricing" is one call rather than two. `limit` bounds the recent list.

Read the `status` on everything it returns: 'unapproved' rows are
proposals a human has not accepted, and saying so is the difference
between reporting the team's record and inventing it — `review_queue`
counts how many are waiting, and a person clears them in the review UI. `conflicts` are
places the memory disagrees with itself — surface them, don't pick a side.

Reach for something else when you already know what you are looking for:
semantic_search for a topic, get_object for one thing, list_objects for a
population.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`topic`	No
`memory`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedSep 11, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already include readOnlyHint=true and destructiveHint=false, so the bar is lower; the description adds meaningful behavior beyond that: resulting `status` values, what 'unapproved' means, how `review_queue` counts pending human approvals, and that conflicts should be surfaced without taking a side. No contradiction with annotations.

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 most important directive, organized into logical sections, and every sentence earns its place. It covers purpose, parameters, status semantics, and alternatives without unnecessary filler.

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 read-only briefing tool, the description is complete: it explains what the tool returns, how parameters modify the call, the meaning of status fields, and when to prefer sibling tools. Since an output schema exists, the description need not enumerate exact return fields.

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

Parameters5/5

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

Schema description coverage is 0%, so the description carries full responsibility. It describes all three parameters: `memory` as the slug from list_my_memories, `topic` as an optional addition that runs hybrid search and inlines hits, and `limit` as a bound on the recent list. This fully compensates for the missing schema descriptions.

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 verb and resource: 'brief me on this memory' and enumerates exactly what the composite read returns (classes and counts, recent additions, conflicts, approval queue, checkpoint). It also explicitly differentiates from siblings by naming semantic_search, get_object, and list_objects as alternatives for narrower lookups.

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?

It gives an explicit when-to-use instruction: 'CALL THIS FIRST, before working in a memory.' It also provides when-not-to-use guidance: 'Reach for something else when you already know what you are looking for' and names the specific sibling tools for those cases. The optional `topic` behavior is also explained as a conditional use case.

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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