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Kanysh Basic

Kanysh Basic

by almukhanomr
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

job_logs

Read-onlyIdempotent

Retrieve worker stdout/stderr tails, workflow node logs, and stage events for a job; calculation runs also return child job statuses and worker tails. Check local execution evidence; no secrets.

Instructions

Tail of worker stdout/stderr, workflow node log and stage events; for calculation_run also each child job's status and worker tail. Local evidence only; no secrets.

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": {
      "request": "<request>"
    },
    "name": "job_logs"
  }
}

Input Schema

NameRequiredDescriptionDefault
requestYesTool arguments object (all fields documented; Z is positive upward).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context by noting that the output is 'local evidence only; no secrets' and that calculation_run jobs include child job status and worker tails. It does not clarify how much log tail is returned or whether the 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The definition is a single compact sentence that front-loads the primary content, then adds the calculation_run special case and the local/no-secrets constraint. It is dense but efficient, with no wasted filler.

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 tool with rich annotations, the description covers the main return components and the special calculation_run behavior. Since there is no output schema, it could say more about log tail length or completeness, but it provides enough context for correct invocation in most cases.

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 job_id parameter is fully documented in the schema. The description adds no parameter-level detail beyond what the schema already provides, so the baseline score of 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 the specific content returned: worker stdout/stderr, workflow node logs, and stage events, with an additional child-job detail for calculation_run. This clearly separates it from status-only siblings like job_status or workflow_status, though it does not explicitly name those alternatives.

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 guidance on when to call this instead of job_status, workflow_status, or diagnostics. The description only states what the tool contains, leaving the agent to infer that this is the log-retrieval option rather than a status-checking 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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