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by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_ai_memory_search

Read-only

MANAGER READ: bounded canonical AI Memory search.

Instructions

MANAGER READ: bounded canonical AI Memory search.

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": {
      "query": "<query>"
    },
    "name": "aiworkhub_manager_ai_memory_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum memories to return; default 8, clamped to 1-10.
queryYesWords to search active AI Memories for, non-blank, at most 512 bytes; any word may match and results are ranked by how many do.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.12.67
    • addedInput schema / properties / limit / description
      Added value: +"Maximum memories to return; default 8, clamped to 1-10."
    • addedInput schema / properties / query / description
      Added value: +"Words to search active AI Memories for, non-blank, at most 512 bytes; any word may match and results are ranked by how many do."
  2. First observedv0.1.0

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds that results are 'bounded' (consistent with the 1-10 clamp) and drawn from canonical memory, which is modest but real extra context; it does not mention auth/prerequisites or what happens when a query matches nothing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four words with zero waste and the key qualifier ('bounded canonical') front-loaded, but the brevity comes from under-specification rather than tight editing. There is no wasted sentence, yet the one sentence does little work.

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?

An output schema exists, so return values need not be explained, but for a tool sitting alongside ai_memory_get, ai_memory_related, and kb_search the description never disambiguates which to call. The meaning of 'canonical' and 'MANAGER READ' is left entirely to the agent to guess.

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 schema itself documents the query matching/ranking behavior and the limit default and clamp range. The description contributes nothing beyond that, so the baseline 3 applies.

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

Purpose3/5

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

The fragment 'bounded canonical AI Memory search' names the resource and implies a search verb, so an agent can roughly place it among the ai_memory_* siblings (get/related/write). But it is a label rather than a sentence, and terms like 'canonical' and 'MANAGER READ' are unexplained jargon, leaving the exact scope to inference.

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 when-to-use guidance is given, and no alternatives are named even though the sibling list contains obviously adjacent tools (aiworkhub_manager_ai_memory_get, aiworkhub_manager_ai_memory_related, aiworkhub_manager_kb_search, aiworkhub_manager_context_graph_search). The 'MANAGER READ' prefix hints at an audience but not a selection rule.

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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