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kernl

kernl

by fastslack
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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kernel_chat_message

Send a message within a chat episode and receive the LLM response, with relevant memory context automatically retrieved when available.

Instructions

Send a message in a chat episode and receive the LLM response. The system automatically retrieves relevant context from memory (when available).

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": {
      "episode_id": "<episode_id>",
      "message": "<message>"
    },
    "name": "kernel_chat_message"
  }
}

Input Schema

NameRequiredDescriptionDefault
messageYesThe user message to send
episode_idYesEpisode ID from kernel_chat_start
context_budgetNoMax tokens for context injection (default from config)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the burden and does disclose two useful behaviors: automatic memory/context injection and that an LLM response is returned (implying a blocking generation call). It omits message persistence, latency/streaming behavior, failure modes, and whether the sent message is stored in episode history.

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?

Two short sentences, front-loaded with the action and with zero filler. It could be marginally tighter by folding the parenthetical about memory availability, but nothing is 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?

For a 3-parameter, annotation-free tool with no output schema, the definition covers the round-trip concept but leaves the response shape undescribed and does not explain how context_budget interacts with the automatic context injection. Adequate but with clear gaps.

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 episode_id, message, and context_budget are already documented with types and defaults. The description's memory-retrieval sentence relates loosely to context_budget but adds no syntax, units, or guidance beyond the schema, so the 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?

The description states a specific verb and resource: "Send a message in a chat episode and receive the LLM response." An agent can distinguish this from siblings like kernel_chat_history or kernel_chat_start, though it never explicitly contrasts with them.

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

Usage Guidelines3/5

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

Usage is implied rather than stated: the schema note that episode_id comes "from kernel_chat_start" signals a prerequisite episode, and the memory-retrieval sentence hints the tool is the conversational turn. There is no explicit when-to-use vs when-not guidance or named alternative for stateless queries.

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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