list_jobsJobs of this API key, newest first. Compact rows (id, state, label, created, finished, gpu, cost_usd, error) unless full=true.
| Name | Required | Description | Default |
|---|---|---|---|
| full | No | Return the complete status object per job (default false) | |
| limit | No | 1..200, default 20 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / fullAdded value: +{
+ "description": "Return the complete status object per job (default false)",
+ "type": "boolean"
+}Input schema / properties / limit / descriptionPrevious value: -"1..200, default 50"New value: +"1..200, default 20"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds behavioral details beyond annotations: the ordering ('newest first') and the effect of full=true (switching from compact rows to complete status object). No contradictions with annotations.
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 purpose (jobs of this API key, newest first) and then adds the output format detail. No wasted words, and the key information is immediately accessible.
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 read-only listing tool with no output schema, the description provides the essential return fields (id, state, label, created, finished, gpu, cost_usd, error) and the full option. It does not mention pagination beyond the limit parameter (which is in the schema) or error behavior, but given the simplicity and annotation coverage, it is reasonably complete.
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%, so both 'full' and 'limit' are already documented. The description adds context for 'full' ('unless full=true' implying a switch to complete status objects), which goes slightly beyond the schema. However, it does not add any meaning for 'limit', so it only partially enhances parameter understanding. Baseline 3 is appropriate.
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 a specific verb-resource pair: 'Jobs of this API key' clearly indicates a listing operation scoped to the caller's API key, with a defined ordering ('newest first'). It also distinguishes itself from siblings like job_status (single job) and job_results (specific results) by the scope and output format.
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 implies when to use it—listing all jobs for the API key—and the output format (compact vs full). However, it does not explicitly state alternatives or exclusions, such as 'for a single job, use job_status', leaving the routing to siblings implicit. Given the clear context and sibling names, this is adequate but not fully explicit.
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.