CatchAll (by NewsCatcher)

create_monitor

Create a recurring monitor from a completed job.

Monitors re-run a job's query on a schedule. Use the explore -> refine -> automate pattern: submit a job, refine until results match, then create a monitor.

The schedule is defined in natural language (e.g., 'every day at 9 AM EST'). Always include a timezone (in the schedule text or via the timezone arg). API-enforced constraints apply:

If backfill=true, reference job end_date must be within the last 7 days
If backfill=false, reference job age does not matter
Minimum schedule frequency depends on your plan

Webhooks are now centralized: register them with create_webhook, then pass their IDs here via webhook_ids (there is no inline webhook config anymore).

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Optional max records per run (minimum 10). If omitted, API uses plan default.
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`backfill`	No	Optional gap-fill toggle before first run (default true).
`schedule`	Yes	Natural language schedule (e.g., 'every day at 9 AM EST', 'every Monday at 8 AM UTC', 'every 48 hours')
`timezone`	No	Optional IANA timezone for the schedule (e.g. 'America/New_York'). Defaults to UTC. A timezone written into the schedule text overrides this.
`project_id`	No	Optional project ID to associate this monitor with.
`webhook_ids`	No	Optional list of webhook IDs to notify on each run completion (max 5).
`reference_job_id`	Yes	ID of a completed job to use as the template

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. Discloses key behaviors: natural language schedule, backfill constraints, minimum frequency, and webhook centralization. Could add more about lifecycle but sufficient.

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?

Well-structured, front-loaded with purpose, then usage pattern, schedule guidance, constraints as bullets, and webhook note. No redundant sentences.

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

Completeness5/5

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

Given complexity (8 params, 2 required, output schema exists), description covers when to use, schedule format, timezone rules, backfill constraints, and webhook integration. Output schema exists, so return format not needed.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description adds meaning beyond schema by explaining backfill constraints, timezone override, and webhook_id referencing, so a 4 is warranted.

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?

Clearly states it creates a monitor from a completed job and explains the monitor concept. Distinguishes from sibling tools like delete_monitor or update_monitor by focusing on creation.

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

Usage Guidelines5/5

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

Provides explicit guidance: use after job refinement, always include a timezone, and use centralized webhooks. Implicitly tells when not to use (e.g., for updates, use update_monitor).

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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