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kernl

kernl

by fastslack
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TypeScript
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kernel_agents_call_meeting

Call a structured group meeting with multiple agents to gather collaborative input, run discussion rounds, and synthesize decisions and action items before proceeding.

Instructions

Call a group meeting with other agents. You (the caller) become the moderator. The meeting runs N structured rounds (default 2, capped at 20) where each attendee gives input, then you synthesize into decisions and action items. Use this when you need collaborative input from multiple agents before proceeding.

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": {
      "topic": "<topic>",
      "attendee_ids": "<attendee_ids>"
    },
    "name": "kernel_agents_call_meeting"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicYesMeeting topic / agenda item
roundsNoNumber of discussion rounds (default: 2, max: 20)
contextNoBackground document or partial result to discuss
urgencyNoUrgency level — 'urgent' triggers red alert in the office
attendee_idsYesAgent IDs to invite (you are already the moderator)
__caller_agent_idNo[internal] injected by the agent executor when this tool is invoked from an agent run. Not meant to be set by external callers.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations supplied, the description carries the full burden, and it does disclose real behavior: the caller becomes moderator, the run is N structured rounds, and the result is a synthesis of decisions and action items. It omits operationally important traits such as blocking/latency, cost of fanning out to multiple agents per round, and what happens if an attendee fails to respond.

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?

Three sentences, front-loaded with the action, then the process, then the routing condition; nothing is wasted. The only minor redundancy is repeating the rounds default and cap that already appear in the schema.

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?

For a six-parameter orchestration tool with no output schema, the description covers the essential contract: who moderates, how input is gathered, and what comes back (decisions and action items). It would be stronger if it noted whether the call blocks until all rounds finish or how attendee non-response is handled.

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%, so every parameter is already documented, including the rounds default and cap that the description restates. The description adds clarifying meaning only for the process (rounds → synthesis) rather than for parameter semantics, so the baseline of 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?

The description gives a specific verb and resource ("Call a group meeting with other agents") and immediately explains the mechanics: caller becomes moderator, rounds of attendee input, then synthesis. It is clearly distinct from single-target siblings like ask_supervisor or post_to_colleague, though it never names an alternative explicitly.

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

Usage Guidelines4/5

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

"Use this when you need collaborative input from multiple agents before proceeding" states a clear triggering condition and implies this is a fan-out, pre-decision step. There are no explicit when-not cases or named alternatives for smaller-scale queries, so it stops short of full routing guidance.

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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