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Recall — brief me on this graph

recall
Read-onlyIdempotent

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

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

`memory` is the graph's 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 graph disagrees with itself — surface them, don't pick a side.
`value_conflicts` are one object's property carrying more than one value,
because two sources disagree (an IC memo's EBITDA against a QoE's): each
lists the values you may read with their status and sources, and
`waiting_for_review` counts values a person has not approved yet, which
you cannot see. Name both sides and say a value is still waiting; never
present one value as settled while its object has a value conflict.

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

NameRequiredDescriptionDefault
limitNo
topicNo
memoryYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses the briefing contents: classes and counts, recent activity, conflicts, review queue, and newest checkpoint. It also explains how to interpret status values, unapproved rows, and value conflicts, including the instruction to surface both sides rather than pick one.

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 key instruction and then organized by purpose, parameters, return semantics, and alternatives. Despite its length, each section carries operational guidance that an agent needs to use the tool correctly.

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 composite read tool with nuanced status semantics, the description covers purpose, parameters, return interpretation, and alternatives. Even though an output schema exists, the added interpretation guidance about unapproved rows and value conflicts is important context rather than redundancy.

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 must carry parameter meaning. It defines `memory` as the graph slug from list_my_memories, explains that `topic` additionally runs hybrid search and inlines hits, and states that `limit` bounds the recent list.

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: a composite read that briefs an agent on a graph. It explicitly distinguishes itself from semantic_search, get_object, and list_objects, so an agent can identify when this tool is the right entry point.

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 explicit when-to-use guidance: 'CALL THIS FIRST, before working in a graph.' It also names alternatives and the conditions that select them: semantic_search for a topic, get_object for one thing, list_objects for a population.

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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