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kernl

kernl

by fastslack
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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kernel_audit_cost_report

Analyzes token consumption and costs across agents to reveal high usage, failure rates, and wasted spend, helping monitor budgets and control LLM expenses.

Instructions

Token consumption and cost analysis. Shows which agents burn the most tokens, failure rates, and wasted spend. Critical for budget monitoring.

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_audit_cost_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
hoursNoLook back period in hours (default: 24)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose the analytical content returned (per-agent token burn, failure rates, wasted spend), which signals an aggregate read operation. However, it never states that the tool is read-only, whether it requires elevated permissions, cost implications of running it, or how results are aggregated/ranked.

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 short sentences, front-loaded with the core purpose and followed by concrete output dimensions. The closing 'Critical for budget monitoring' is slightly promotional but still earns its place as a usage cue. No filler or repetition.

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 single-parameter analytics tool with no output schema, the description is reasonably complete — it compensates for the absent output schema by describing what the report contains. The main gap is the absence of any relationship to the similarly named cost-report sibling, which matters given this large tool catalog.

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?

There is one optional parameter (hours) with 100% schema description coverage including the 24h default, so the schema does the heavy lifting. The description adds no meaning about the lookback window, granularity, or how a larger window affects results — baseline 3 is appropriate.

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 resource and analysis type ('Token consumption and cost analysis') and enumerates the dimensions it reports: token burn per agent, failure rates, wasted spend. An agent can tell this is an aggregate cost/usage analytics tool. It does not explicitly distinguish itself from the sibling kernel_meta_cost_report, leaving some ambiguity between the two cost-oriented tools.

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?

'Critical for budget monitoring' implies the use case (budget oversight) but gives no explicit when-to-use vs. when-not, no prerequisites, and no named alternative such as kernel_meta_cost_report. Usage is inferable but not directed.

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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