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agent_memory_store

Store a memory for an AI agent (key-value, with TTL and metadata)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYes
valueYesAny JSON value
agentIdYesAgent identifier
metadataNo
namespaceNodefault
ttlSecondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions 'key-value, with TTL and metadata', but omits critical details such as whether storing overwrites existing keys (upsert behavior), what is returned on success, or required permissions. The agent lacks clarity on side effects.

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?

Single sentence of 12 words, front-loaded with the action and resource, parenthetical adds key details. No redundancy, every phrase earns its place.

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

Completeness2/5

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

With 6 parameters, no output schema, and no annotations, the description is too spare. It does not explain return values, error conditions, namespace behavior, or idempotency. The agent cannot reliably infer how to invoke the tool or what to expect from it.

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 33%, only defining agentId and value. The description adds context for 'key', 'value', 'TTL' (ttlSeconds), and 'metadata', partially compensating for missing parameter descriptions. However, 'namespace' and 'agentId' remain unexplained beyond the schema, and the description does not provide explicit parameter mappings.

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 clearly states 'Store a memory for an AI agent' with verb 'store' and resource 'memory'. It distinguishes from sibling tools like agent_memory_delete, agent_memory_get, and agent_memory_search by implying creation/update versus retrieval or deletion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., agent_memory_search for retrieval, agent_memory_delete for deletion). The description does not specify prerequisites, best practices, or context for selection.

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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