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CatchAll (by NewsCatcher)

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

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses that the tool returns job history with IDs, queries, statuses, and timestamps, and scopes results to jobs 'submitted by you.' However, it fails to mention that the 'ownership' parameter can broaden results to all or shared jobs, which could mislead the agent about default behavior.

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 short sentences, front-loaded with the primary action and followed by return-value details. Every word contributes meaning; there is no redundancy or filler.

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?

The output schema exists and covers return values, and the description provides the essential purpose and scope. For a list tool with seven optional parameters, the description is sufficient for selecting and invoking, though it could better differentiate from sibling list tools like list_monitor_jobs.

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% and each parameter has a clear description. The tool description adds no additional parameter semantics beyond the schema, so the baseline of 3 applies.

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

Purpose4/5

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

The description states a clear action ('List') and resource ('all jobs submitted by you'), which distinguishes it from single-job tools like get_job_status. However, the optional 'ownership' parameter allowing 'all' or 'shared' filters introduces ambiguity about whether the tool only shows self-submitted jobs, slightly weakening the clarity.

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

Usage Guidelines3/5

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

The description implies usage (viewing your job history) but provides no explicit guidance on when to prefer this tool over alternatives like list_monitor_jobs. There is no mention of exclusions or conditions, so the agent must infer appropriate use from the purpose statement.

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
Disambiguation5/5

Each tool is scoped to a specific resource type and action, with clear distinctions between similarly named operations (e.g., pull_results vs pull_job_csv, initialize_query vs validate_query). No two tools appear to perform the same function.

Naming Consistency4/5

Tools consistently use snake_case verb_noun patterns (create_X, get_X, list_X, update_X, delete_X), with domain-specific verbs like submit, pull, initialize, and validate adding semantic clarity. Minor deviations such as pull_* vs get_* and compound names like create_dataset_from_csv are still predictable.

Tool Count2/5

At 60 tools, the server is heavily overstuffed for a single MCP surface. While the broad domain (datasets, entities, jobs, monitors, projects, webhooks) justifies many operations, the sheer volume exceeds typical recommended limits and includes near-duplicates (pull_results vs pull_job_csv, get_dataset vs get_dataset_status), making agent tool selection unwieldy.

Completeness4/5

The tool set provides robust CRUD and lifecycle coverage for all major resources, including special operations like csv import, webhook mapping, and monitor enable/disable. Minor gaps such as the absence of a get_monitor (single monitor details) and no cancel_job can be worked around via list_monitors and waiting for job completion.

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