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AIWorkhub

AIWorkhub

by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_ai_memory_get

Read-only

Retrieve the newest active AI Memory stored under an exact key to look up canonical context by key name.

Instructions

MANAGER READ: exact active canonical AI Memory lookup.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "key": "<key>"
    },
    "name": "aiworkhub_manager_ai_memory_get"
  }
}

Input Schema

NameRequiredDescriptionDefault
keyYesExact AI Memory key, at most 256 bytes; returns the newest active memory stored under it.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.67
    • addedInput schema / properties / key / description
      Added value: +"Exact AI Memory key, at most 256 bytes; returns the newest active memory stored under it."
  2. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds 'active canonical', signaling that superseded/stale entries are excluded and the newest active record is returned, which is useful non-obvious behavior beyond the annotations. Return shape is handled by the output schema.

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?

A single front-loaded clause with no filler, and the mode marker ('MANAGER READ') comes first. It is efficient, though its terseness leaves context to inference.

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?

With an output schema defining return values and annotations covering safety, the description only needs to convey purpose and lookup mode, which it does. The remaining gap is explicit routing to ai_memory_search for fuzzy/keyword cases, a minor omission for a 1-param read tool.

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 coverage is 100%, and the property description already explains the key length bound and that the newest active memory under it is returned. The description's 'exact' reinforces exact-match semantics but adds little beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (AI Memory) and a specific mode (exact, active, canonical lookup), which implicitly distinguishes it from the search-related siblings. It doesn't name those siblings, but the scope is clear enough for an agent to know this is a keyed single-record fetch rather than a query.

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

Usage Guidelines3/5

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

The word 'exact' hints that this is for precise key lookups versus ai_memory_search's broader querying, but there is no explicit when-to-use/when-not or named alternative. The guidance is implied rather than stated.

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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