Cancel Job
job_cancelCancel a running job.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID to cancel. |
job_cancelCancel a running job.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID to cancel. |
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 the operation as destructive and idempotent, so the description does not need to repeat that. It adds the useful precondition that the job must be running, but does not describe what happens after cancellation or any side effects beyond what annotations convey.
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, four-word sentence with no filler or redundant information. It front-loads the action and the target resource effectively.
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 one-parameter cancellation tool with annotations covering destructive behavior, the description is mostly sufficient. It could clarify post-cancellation behavior or error handling for non-running jobs, but these are minor gaps given the full schema coverage and annotation context.
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?
The input schema fully documents the single job_id parameter with 100% coverage, so the description does not need to add parameter details. The description provides no additional semantic meaning about job_id, but the schema already carries that burden.
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 uses the specific verb 'cancel' and clearly identifies the resource as 'a running job,' so the operation is unambiguous. It does not explicitly name sibling tools like job_get or job_list, but the action is distinct from them.
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 phrase 'running job' implies the tool is intended for jobs currently in progress, providing a usage context. However, it does not explicitly state when not to use it or mention alternatives such as job_get for checking job status.
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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Tools are grouped by clear resource prefixes (account_, brain_, connector_, credential_, file_, job_, key_), and most actions have distinct purposes. A few boundaries overlap—brain_admin's lint action duplicates brain_lint, and account_preferences/setup/switch could momentarily confuse—but the descriptions resolve most ambiguity.
The dominant pattern is resource_verb for actions (file_read, job_cancel, key_create) and resource_noun for state views (credits_balance, brain_settings, account_preferences), which is readable. However, exceptions like discover, use_tool, top_up_credits, and feedback_request_tool break the pattern, and the set is not consistently verb_noun.
47 tools is well beyond the comfortable range; even though prefixes organize them, the agent faces a large selection surface with many narrowly scoped tools. A more consolidated set with action-based subcommands would be easier to navigate.
Core workflows are covered end-to-end: account setup and billing, connector and credential management, file CRUD, job polling, key lifecycle, brain knowledge management, and catalogue discovery/execution. Gaps are minor—outfit/persona/product/scene are list-only, connectors lack an update operation, and there is no explicit single-page brain get—but agents can generally work around them.