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MeshMarket

Agent Memory

agent-memory

Agent Memory — Store or recall private, scoped memory for your agent. Use {action:'store', content:'...'} to save a fact and {action:'recall', query:'...'} to fetch the most relevant memories by meaning (or the latest memories when query is empty). Good for continuity across sessions; agent-brain is the reasoning layer that reads and writes memory in the loop. (2 MESH/call, a tool · memory)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesPayload for agent-memory

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations cover the safety profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true) and the description does not contradict them. It adds non-obvious context: memory is private and scoped, recall ranks by meaning (or returns latest when query is empty), and the operation costs 2 MESH/call — a useful disclosure the annotations don't carry.

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 purpose, then usage examples, then context and cost in a compact form with no wasted sentences. The trailing '(2 MESH/call, a tool · memory)' parenthetical is slightly noisy but still informative.

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?

No output schema exists, and the description plus schema together convey store/recall behavior, scoping, ranking, and cost. Return-shape detail is partially delegated to the schema ('The result's mode says which ran'), leaving minor gaps but nothing an agent needs to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100% and the nested payload documents aliases, truncation, defaults, and ranking modes in depth. The description's inline examples restate how action/content/query combine but add little semantic detail beyond the schema, so the baseline 3 is appropriate.

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 ('Store or recall private, scoped memory for your agent') and explicitly positions itself against the sibling agent-brain ('the reasoning layer that reads and writes memory'), so an agent can tell the memory store from the reasoning loop without opening either schema.

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?

Gives concrete when-to-use examples for both operations ({action:'store'...} to save a fact, {action:'recall'...} to fetch) plus a context cue ('continuity across sessions'). It names agent-brain as the related layer but does not spell out an explicit when-not-to-use-this rule, so it stops short of a 5.

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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