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AIWorkhub

AIWorkhub

by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_kb_search

Read-only

Searches active knowledge base entries for matching words and returns up to eight ranked results, helping AIWorkhub managers retrieve relevant local KB context for coding tasks.

Instructions

MANAGER READ: bounded canonical KB 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_kb_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoAccepted for signature stability but ignored; a search always returns at most 8 entries.
queryYesWords to search active KB entries 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: +"Accepted for signature stability but ignored; a search always returns at most 8 entries."
    • addedInput schema / properties / query / description
      Added value: +"Words to search active KB entries 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

C2.5/5.0
Behavior2/5

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

readOnlyHint=true and openWorldHint=false already declare the safety profile, so the description's "READ" prefix only restates the annotation. "bounded" gestures at the 8-entry cap, but that constraint is fully spelled out in the schema's limit field, so the description contributes no behavioral context of its own.

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?

It is a single front-loaded clause with zero waste, but it is terse to the point of under-specification rather than genuinely concise. Brevity here costs routing clarity instead of saving an agent effort.

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?

Annotations, a full input schema, and an output schema are all present, so return values and safety need no explanation. Still, for a search tool in a dense sibling cluster, the description says nothing about scope (active vs archived entries), ranking, or relationship to kb_get/kb_related, which is the remaining information an agent needs to pick 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 coverage is 100%: the query field documents matching/ranking behavior and the limit field documents that it is ignored and capped at 8. The description adds 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?

"bounded canonical KB search" names a verb (search) and resource (KB), but "canonical" and "bounded" are internal jargon and the phrase does not separate this tool from siblings like aiworkhub_manager_kb_get, kb_related, or context_graph_search. An agent can guess it searches KB entries but not how it differs from the neighbor lookups.

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?

There is no when-to-use statement, no when-not-to-use, and no named alternative, despite several plausible siblings (kb_get, kb_related, ai_memory_search, context_graph_search). The agent is left to infer routing entirely from the tool name.

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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