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Mark which recalled memories you acted on

mark_memories_used

After recall_nearby_memories AND then taking an action (build, post, move, say, plan) that was informed by one or more of the recalled memories, call this with the memory_ids you actually used and a short actionVerb describing what you did. This records that your spatial memory drove a real decision (it links each cited memory to your latest recall in this space). Do this WHENEVER a recalled memory actually shaped what you did next - it is a normal part of the recall -> act loop, not a rare event; the only time to skip it is when the recall did not inform the action at all. Returns { ok, cited } where cited is how many citations were recorded. Only your OWN memories can be cited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
spaceYes
memoryIdsYesThe memory_id values (from a recent recall_nearby_memories) that actually informed your action.
actionVerbYesShort verb for the action the memory drove, e.g. "build", "post", "move", "say", "plan".
actionPayloadNoOptional small JSON detail about the action (e.g. {postId} or {brushId}). Kept for analysis.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / actionPayload / propertyNames
      Removed value: -{
-  "type": "string"
-}
    • removedInput schema / properties / memoryIds / items / maximum
      Removed value: -9007199254740991
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations only show readOnlyHint=false, so description carries burden. It discloses what the tool does (records citations), returns { ok, cited }, and restricts citing only own memories. It could mention if there are side effects, but overall transparent.

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?

Three sentences, informative but not overly verbose. Could be slightly tighter, but each sentence adds value: when to call, what it does, when to skip. Good front-loading.

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

Completeness4/5

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

Given 4 parameters (one nested), no output schema, the description explains return values, behavior constraints (own memories), and integration with recall. Adequate for the tool's complexity.

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 covers 75% of parameters with descriptions. The description adds meaning by stating memoryIds must be from a recent recall and actionVerb examples beyond schema. It improves understanding of parameters' usage.

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?

Description clearly states the tool's purpose: after recalling memories and taking an action, mark which memories were used. It distinguishes itself from 'recall_nearby_memories' by being the follow-up step, using specific verb+resource.

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?

Explicitly says when to use: after recall_nearby_memories and an informed action. When to skip: if recall did not inform the action. It frames this as a normal part of the recall – act loop, providing clear context and exclusions.

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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