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kernl

kernl

by fastslack
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TypeScript
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kernel_agents_get_skills

Retrieve an agent's attached procedural skills, including descriptions and token estimates, to inspect available capabilities before planning or delegation.

Instructions

List the procedural skills attached to an agent, with descriptions and token estimates.

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": {
      "agent_id": "<agent_id>"
    },
    "name": "kernel_agents_get_skills"
  }
}

Input Schema

NameRequiredDescriptionDefault
agent_idYesTarget agent id

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, the description must carry the behavioral burden. It does disclose the returned content shape ('descriptions and token estimates'), which is genuinely useful, but says nothing about permissions, pagination/volume, ordering, empty-skill results, or error behavior for an invalid agent_id.

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?

A single front-loaded sentence with no filler; the resource and the return content are both stated before anything else. Nothing is redundant or padded.

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 one-parameter read-only lookup this is nearly sufficient: the actor (agent), the resource (skills), and the return payload are all covered, and there is no output schema demanding more explanation. Minor gaps remain around result cardinality, ordering, and failure modes for a bad agent_id.

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% and the single agent_id parameter is documented as 'Target agent id', so the schema already does the work. The description adds only the relational framing that skills are retrieved for that agent, which is marginal beyond the schema baseline.

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 ('List') and a specific resource ('procedural skills attached to an agent'), and even hints at payload content ('descriptions and token estimates'). It does not explicitly name the write-side counterpart (kernel_agents_set_skills), so it stops short of full sibling differentiation.

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?

The 'List' framing implies a read-only inspection use case, but there is no explicit when-to-use guidance, no mention of when to prefer kernel_agents_set_skills, and no stated prerequisites (e.g., must the agent exist, what happens for an unknown id). Usage is inferable rather than stated.

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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