Kratos MCP Server

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job_logs

Retrieve tail lines from a Kratos simulation job log, filter by case-insensitive substring, and select an MPI rank to inspect rank-specific output for debugging.

Instructions

Return the last 'tail' lines of a job's simulation log, optionally only lines containing the 'grep' substring (case-insensitive). For MPI jobs, 'rank' selects another rank's log (job_status lists rank_logs); rank 0 / unset is the main log.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`grep`	No
`rank`	No
`tail`	No
`job_id`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.7.0Oct 2, 2026

addedInput schema / properties / rank

Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Rank"
+}

First observedv0.3.1Aug 10, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations, so the description carries the full burden. It discloses tail/grep/rank behavior and the case-insensitive matching, but is silent on read-only nature, permission requirements, behavior for a still-running or missing job, and log truncation/rotation.

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 dense sentences, front-loaded with the core action, then the optional filters and the rank nuance. No filler text.

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?

An output schema exists, so return values need not be described, and the parameter semantics are covered. The remaining gap is edge-case behavior (missing/finished jobs, permissions) for a no-annotation tool.

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?

With 0% schema coverage, the description compensates well: it defines tail as line count, grep as a case-insensitive substring filter, and rank as MPI rank selection with rank 0/unset meaning the main log. Only the obvious job_id is left unexplained.

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 and resource ('Return the last tail lines of a job's simulation log') plus the filtering modifiers. It also distinguishes itself from job_status by noting where rank_logs is enumerated, though it never explicitly frames itself against a sibling log-retrieval tool.

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?

Gives implied context (view a simulation log, use rank for MPI jobs, consult job_status for rank_logs) but offers no explicit when-to-use/when-not guidance or named alternatives for retrieving logs.

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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