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AIWorkhub

AIWorkhub

by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_context_graph_search

Read-only

Search transcript-backed Context Graph evidence by matching words in recorded conversation events, returning exact matches with a configurable result limit.

Instructions

MANAGER READ: search exact transcript-backed Context Graph evidence.

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_context_graph_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum matching events to return; default 12, clamped to 1-50.
queryYesWords to find in recorded conversation events, non-blank, at most 2048 bytes; all terms (up to 12) must match, with a substring fallback on content and source_ref.

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 matching events to return; default 12, clamped to 1-50."
    • addedInput schema / properties / query / description
      Added value: +"Words to find in recorded conversation events, non-blank, at most 2048 bytes; all terms (up to 12) must match, with a substring fallback on content and source_ref."
  2. First observedv0.1.0

TDQS

B3.2/5.0
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 that evidence is 'transcript-backed' and matching is 'exact', which is genuinely informative about result provenance and relevance behavior, but says nothing about scope limits, empty-result behavior, or ranking.

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 short line with no padding beyond the 'MANAGER READ' access prefix, and the core verb+resource is front-loaded. It is concise but borders on under-specified rather than wasteful.

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

Completeness3/5

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

With a fully described 2-parameter schema and an output schema present, the description arguably has enough to be callable, but for a search tool in a dense family of retrieval siblings it omits the disambiguation an agent needs to choose 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%, so both `query` and `limit` are fully documented in the schema, including the all-terms-must-match rule and the 1-50 clamp. The description's 'exact' hints at the same matching semantics but adds no syntax or format detail 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 verb and resource: 'search exact transcript-backed Context Graph evidence.' An agent can tell it retrieves transcript evidence from a context graph, though the 'MANAGER READ' prefix is access-control framing rather than purpose, and no sibling (context_graph_related, source_graph_query, kb_search) is contrasted.

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 statement of when to use this over related tools — nothing distinguishes it from aiworkhub_manager_context_graph_related, aiworkhub_manager_context_graph_range, aiworkhub_manager_source_graph_query, or the kb/ai_memory search siblings. The agent must infer selection from the name alone.

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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