Permanently delete a job and its results.
Use when:
You want to remove a job you no longer need from your account.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The job ID to delete. | |
| api_key | No | CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'permanently delete,' implying irreversibility, but does not elaborate on side effects, authorization requirements, or rate limits. With no annotations, the description is insufficiently transparent about the tool's full behavioral impact.
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?
Extremely concise: two sentences that directly convey the core action and usage scenario. No wasted words, front-loaded with the primary purpose.
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?
Given the tool's simplicity and presence of an output schema, the description is mostly complete. However, it lacks mention of irreversibility, potential dependencies, or whether deletion is immediate/asynchronous, which would enhance completeness.
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 parameters are documented in the schema. The description adds little beyond what the schema already provides (e.g., job_id and api_key). Baseline 3 is appropriate; no extra value.
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 it deletes a job and its results, with a specific verb and resource. It distinguishes from sibling tools like delete_dataset or delete_entity, which are for different resources. However, it could be more explicit about what constitutes a 'job' in this context.
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?
Includes a 'Use when' bullet with a clear scenario: removing a job you no longer need. However, it does not provide when-not-to-use or mention alternative tools (e.g., continue_job) for other job actions. Guidance is adequate but not thorough.
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.
Most tools have distinct purposes, but some pairs like create_dataset vs create_dataset_from_csv or pull_results vs pull_job_csv could cause confusion. However, descriptions clarify differences.
Tools follow a consistent verb_noun pattern (e.g., create_dataset, list_datasets) with minor exceptions like append_csv_to_dataset and pull_job_csv. Overall predictable.
60 tools is high for an MCP server, but the domain (web research, job processing, multiple resource types) justifies the count. Still borders on excessive.
The server offers full CRUD for datasets, entities, monitors, projects, webhooks, plus job submission, status polling, result retrieval (JSON/CSV), webhook management, and health endpoints. No obvious gaps.