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Remember a fact

remember

Record ONE thing worth keeping, in a single call.

`text` is the fact in plain language — it becomes the object's
description and is indexed for search. `class_name` says what KIND of
thing it is (decision, risk, convention, person, session_checkpoint, …);
it defaults to 'note' and a class that does not exist yet is created.
`properties` are typed fields ({"status": "accepted", "decided_on":
"2026-09-11"}), and `relationships` are edges to other objects
([{"type": "decided_by", "target": "sarah_chen"}] — add "target_class"
to create a target that isn't here yet).

Writes land `status='unapproved'`: a proposal, flagged until a person
approves it — an agent proposes, a human decides. You cannot read an
unapproved row back yourself until then (a session_checkpoint is the
exception — see SESSION HANDOFF), so tell the user what you recorded
rather than treating it as settled.

Naming: omit `name` and the server derives a unique one from the text.
PASS a `name` that already exists in that class and you UPDATE that
object instead of creating a second one — that is how you revise a fact
rather than accumulate contradictions.

SESSION HANDOFF: before you run out of context, call this with
class_name="session_checkpoint" and a text saying what you did, what is
left and where you stopped. The next session's `recall` hands it straight
back.

For a whole document use the extraction pair (get_extraction_prompt →
submit_extraction_from_llm) instead — it produces many typed rows and
keeps the document on your side. Check `near_duplicates` in the response:
if what you wrote already existed, fold the pair with merge_objects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
textYes
memoryYes
class_nameNonote
propertiesNo
source_docNo
display_nameNo
relationshipsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only give the generic safety profile (readOnlyHint=false, idempotentHint=false); the description goes far beyond by disclosing that writes land status='unapproved', that the agent cannot read the row back until a human approves, that session_checkpoint is the exception, and that reusing an existing name performs an UPDATE rather than a create. These are precisely the nontrivial behaviors an agent must know before calling.

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?

Front-loads the core action and then organizes the rest into labeled concerns (Naming, SESSION HANDOFF), so it is scannable. It is long for a tool description, but nearly every sentence carries operational information rather than 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 complex 8-param mutation tool with an output schema, the description covers the mutation semantics, approval workflow, upsert dedup path, handoff use case, and even points at the near_duplicates field to check. Since an output schema exists, return-value detail is correctly omitted.

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

Parameters4/5

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

Schema coverage is 0%, so the description carries the burden, and it does so well for text, class_name, properties, relationships and name (including the upsert semantics and the relationships target_class trick). However, `memory` is a required parameter and is never explained, and source_doc/display_name are also unaddressed, leaving clear gaps.

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?

States a specific verb+resource ('Record ONE thing worth keeping, in a single call') and explicitly scopes what belongs here versus the extraction pair (get_extraction_prompt → submit_extraction_from_llm). An agent can distinguish this from recall, merge_objects and semantic_search without opening a schema.

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?

Names alternatives and the condition selecting them: whole documents go to the extraction pair, duplicates get folded with merge_objects, and the SESSION HANDOFF paragraph gives an explicit when-to-use trigger ('before you run out of context'). Exclusions are stated, not inferred.

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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