List the caller's recent async jobs (also available as gi://jobs/recent).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max number of recent jobs to return. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 and destructiveHint=false, signaling a safe read operation. The description adds that jobs are scoped to the caller and recent, plus an alternative identifier (gi://jobs/recent), but it does not disclose behavior like pagination, ordering, or response details. It provides modest additional context but not deep transparency.
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 succinct sentence, front-loaded with the action and resource, and includes a brief parenthetical alternative identifier that adds value without bloat. Every word earns its place.
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?
This is a simple read-only list tool with a single well-documented parameter and an output schema present. The description covers purpose and scope, and annotations cover safety. It omits minor details like result ordering or pagination behavior, but these are not critical given the tool's simplicity and schema coverage.
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 coverage is 100%: the only parameter 'limit' has a clear description, default, and bounds. The tool description adds no additional parameter semantics beyond what the schema already provides, so 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 clearly states the tool lists the caller's recent async jobs, using the specific verb 'List' and identifying the resource as jobs scoped to the caller. This distinguishes it from sibling tools like get_job, which presumably retrieves a single job, and other fetch/find tools.
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 this tool is for retrieving a list of the caller's recent async jobs, but it does not explicitly explain when to use it versus alternatives such as get_job (e.g., for a specific job ID). There is no mention of exclusions or when not to use it.
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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Each tool targets a distinct operation: fetching gene/region/expression-window sequences, finding genes, predicting chromatin/enhancer/expression/promoter/splice, and managing jobs/models. The fetch tools are explicitly cross-referenced with guidance on which to use, eliminating ambiguity.
All tools follow a consistent lowercase snake_case verb_noun pattern, with clear verbs like fetch, find, predict, get, list, load, and store. Even compound names like find_genes_and_predict_expression remain readable and predictable.
Fifteen tools is at the upper end of the ideal range but each tool earns its place: five prediction tasks, five sequence acquisition paths, two gene-finding tools, plus job and model management. No tool feels redundant or superfluous.
The surface covers sequence acquisition, gene finding, five prediction tasks, async job polling, and model discovery—strong coverage for the stated domain. A minor gap is that store_inline_sequence references a load_local_fasta tool that does not exist in the tool list, though agents can work around it via fetch_ensembl_sequence or fetch_region.
