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by tedin7
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

inspect

Retrieve raw job log lines filtered by grep and limited by tail when the summary is not enough to diagnose an issue.

Instructions

Raw log lines of a job (last tail, filtered by grep). Only when the summary is not enough.

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": {
      "id": "<id>"
    },
    "name": "inspect"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYes
grepNo
tailNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations exist, so the description carries the full behavioral load. It does disclose the return shape (raw lines, tail-limited, grep-filtered), but never states whether grep is applied before or after the tail, what happens when there are no matches or the job is still running, or whether output is truncated.

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 compact sentences, front-loaded with what is returned and immediately followed by the gating condition. Nothing is padded or repeated from the schema.

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 small read tool with no annotations and no output schema, the essentials are present, but the tail-vs-grep ordering ambiguity and unstated failure/empty-result behavior leave real gaps for an agent that must call it correctly on the first try.

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 0%, so the description must compensate, and it does explain the two non-obvious parameters: tail means the last N lines and grep filters them. The required id is left as obvious context, and grep's pattern syntax (regex vs literal, case sensitivity) is never clarified.

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 gives a concrete verb-resource picture: raw log lines of a job, scoped by a tail count and a grep filter. It implicitly separates itself from summary-oriented siblings with 'only when the summary is not enough,' though it never names them.

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?

There is explicit when-not guidance: use this only when the summary is insufficient. What is missing is a named alternative or a positive trigger such as 'use for debugging a failed step' — the agent must infer that this is the fallback 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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