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kernl

kernl

by fastslack
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TypeScript
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kernel_chat_concepts

List extracted concepts from chat conversations ranked by cross-conversation importance, with optional limits and minimum gravity thresholds to surface key ideas.

Instructions

List extracted concepts from chat conversations, ordered by gravity (cross-conversation importance).

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_concepts"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax concepts to return (default: 50)
min_gravityNoMinimum gravity threshold (default: 0)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the read-only listing behavior and the gravity ordering. It omits whether extraction is precomputed or expensive at call time, whether results are paginated, and what happens when no concepts meet the gravity threshold.

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 front-loaded sentence with no filler, and the ordering rule is placed where it is immediately useful. It is efficient but slightly under-informative for the amount of behavior an agent needs to know.

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?

There is no output schema and no annotations, so the description must convey the return shape, and it does not say what a 'concept' record contains or how results are bounded. It is adequate for selecting the tool but thin for interpreting the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with documented defaults, so the baseline would be 3. The description earns an extra point by defining 'gravity' as cross-conversation importance, which the schema leaves unexplained when it says only 'Minimum gravity threshold'.

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 uses a specific verb and resource ('List extracted concepts from chat conversations') and adds an ordering rule ('ordered by gravity'), which is more than a restatement of the name. It does not, however, distinguish this from siblings such as kernel_chat_recall or kernel_chat_patterns, so an agent must still infer why it would pick this over those.

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 explicit when-to-use guidance, no prerequisites, and no named alternative. The agent can infer that this is for browsing cross-conversation concepts, but nothing in the text says when this is preferable to a search or recall tool.

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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