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list_user_jobs

List all jobs submitted by you.

Returns your job history with IDs, queries, statuses, and timestamps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoOptional filter by job processing mode: 'base' or 'lite'.
pageNoPage number for pagination (default: 1)
searchNoOptional text filter on the job query.
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
ownershipNoOptional ownership filter: 'all', 'own', or 'shared'.
page_sizeNoNumber of results per page (default: 100, max: 1000)
project_idNoOptional filter to jobs belonging to a specific project.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the return contents (IDs, queries, statuses, timestamps) and implies a read-only listing operation, but it does not mention authentication requirements, rate limits, or potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences with the main action front-loaded ('List all jobs submitted by you') and no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 7 optional parameters and an output schema, the description effectively covers the core purpose and return fields, and the scope restriction ('submitted by you') helps set context for tool selection. However, it lacks explicit guidance on when to prefer this over related tools, so it is not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add extra meaning beyond the schema's parameter descriptions, though it clarifies that the list is scoped to the user's own jobs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'List' with the resource 'jobs submitted by you', clearly indicating it returns the user's own job history with fields like IDs, queries, statuses, and timestamps. This effectively distinguishes it from sibling tools such as list_monitor_jobs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that this tool lists the caller's own jobs, implying it is for viewing personal job history, and the phrase 'submitted by you' helps differentiate it from monitor-related job listings. However, it does not explicitly state when to use this tool over alternatives or mention any exclusions.

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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TDQS

A3.6/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness5/5

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.