job_statusState (created|uploaded|queued|launching|running|uploading|done|failed|cancelled), GPU, rate, billed seconds, cost so far, exit code, error, last thermo lines.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Job id, e.g. MDJOB-20260907-3F200C |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context by enumerating the exact fields returned (state, GPU, rate, billed seconds, cost, exit code, error, last thermo lines), which helps the agent understand what to expect. This goes beyond the annotations and is useful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that front-loads the core state values and then lists the remaining fields. It contains no filler and every part adds information. It is appropriately concise for a status tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with one parameter and no output schema, the description provides the essential response content. It does not explain potential errors, pagination, or the meaning of each field, but for a status tool this is likely adequate. The main gap is the lack of explicit differentiation from siblings, but the tool's scope is small and the annotations cover safety.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single 'id' parameter, which is fully documented with an example. The description adds no additional meaning about the parameter; it only describes the response fields. With full schema coverage, the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool returns job status fields including state, GPU, cost, etc. It is clear the tool is for retrieving status of a job, but it does not explicitly distinguish it from sibling tools like job_log or job_results. The name and title reinforce the purpose, so it is clear but not fully differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There is no mention of scenarios, exclusions, or references to sibling tools. An agent has no hint about when job_status is preferred over job_log or job_results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: account and capabilities cover account/configuration info, while create_job, start_job, and submit_job represent clearly differentiated job submission stages. Monitoring, results, cancellation, and deletion tools are all non-overlapping.
Tool names mostly follow a clear snake_case verb_noun pattern (cancel_job, create_job, delete_results, list_jobs, start_job, submit_job). A few noun-only names like account, capabilities, job_status, and job_results deviate slightly, but the pattern remains recognizable and predictable.
With 12 tools, the set covers the full job lifecycle without being bloated. Each tool maps to a meaningful operation, and the count is well within the typical 3-15 range for a domain-specific server.
The tool surface covers the necessary workflow: checking account/capabilities, submitting and starting jobs, monitoring status/logs, retrieving results, canceling jobs, and deleting results. No essential operation for the stated GPU job management domain appears missing.