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kernl

kernl

by fastslack
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TypeScript
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kernel_chat_start

Start a new chat episode and get an episode ID to use with kernel_chat_message. Set an optional title and choose a model or provider override per conversation.

Instructions

Start a new chat episode (conversation). Returns the episode ID to use with kernel_chat_message.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "kernel_chat_start"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoSpecific model override (e.g. claude-sonnet-4-20250514)
titleNoOptional title for the episode
providerNoLLM provider: claude, openai, lmstudio (default from config)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are supplied, so the description bears the full burden. It usefully discloses the return value (episode ID) in the absence of an output schema, but says nothing about persistence, cost, provider fallback behavior, or whether the episode is private or shared.

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

Conciseness5/5

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

Two short sentences with zero waste; the purpose comes first and the actionable follow-up (the returned episode ID) is second.

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 three-parameter creation tool with fully documented schema and no output schema, the description covers what it does and what it returns. The main remaining gap is lifecycle behavior for a tool with no annotations, but nothing essential to calling it correctly is missing.

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 all three parameters (model, title, provider) are already documented in the schema. The description adds no extra meaning about defaults, precedence between model and provider, or valid values beyond the schema examples.

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 names a specific verb and resource ('Start a new chat episode (conversation)') and specifies the artifact produced (the episode ID). It does not differentiate from other episode-related siblings such as kernel_chat_list or kernel_chat_archive, so it falls short of a 5.

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?

It gives clear workflow context by naming the follow-up tool ('use with kernel_chat_message'), which tells the agent this is the entry point of a convesation. It offers no exclusions or guidance on when a new episode is unnecessary versus reusing an existing one.

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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