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Log interaction

record_interaction

Record one dated touchpoint with someone in the user's graph — an email exchanged, a call, a meeting, a LinkedIn touch, or a manually noted contact — so 'when did I last talk to them?' has an answer. Creates an interaction object connected to the person it happened with; get with_object_id from search_graph_objects. occurred_at is ISO 8601 ('2026-08-28T15:00:00Z') and may be in the future ('we're meeting next Tuesday'). direction is from the user's point of view: inbound means they reached out to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelYesHow it happened.
summaryYesOne or two sentences on what happened.
directionYes'inbound' when they contacted the user, 'outbound' when the user reached out.
occurred_atYesWhen it happened (ISO 8601; future is fine).
with_object_idYesWho it happened with (their exact object_id).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removed
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations only mark readOnlyHint=false, so the description adds value by stating that a new interaction object is created and connected to the person. It also discloses that future timestamps are acceptable and that the person must already be a graph object. It does not cover failure modes or duplicate interactions, but this is adequate for a simple create action.

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?

Three sentences, each with a distinct job: scope and purpose, object creation and prerequisite, parameter semantics. There is no filler and no mere repetition of schema text.

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 mutation tool with five required parameters and no output schema, the description covers all the non-obvious information an agent needs: input types, date syntax, direction interpretation, and where to obtain the person's ID. No critical usage gap remains.

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?

The input schema already documents all five parameters, so the baseline is 3. The description adds real meaning for three of them: occurred_at format and future dates, direction from the user's point of view, and the source of with_object_id. Channel and summary are self-explanatory and need no extra detail.

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 opens with a specific verb ('record'), defines the resource ('one dated touchpoint' / 'interaction object'), and enumerates accepted interaction types. It makes clear that the tool creates an interaction object connected to an existing person, which distinguishes it from generic graph creation tools.

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 a clear purpose — answering 'when did I last talk to them?' — and a concrete prerequisite: get with_object_id from search_graph_objects. It does not explicitly name alternatives or state when not to use the tool, so it stops just short of full guidance.

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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