CatchAll (by NewsCatcher)

trigger_webhook

Manually trigger webhook delivery for a resource (job/monitor/monitor_group).

Use when:

You want to (re-)send a webhook delivery on demand instead of waiting for the automatic dispatch — e.g. to replay a missed or failed delivery.

Input Schema

TableJSON Schema

Name	Required	Description
`job_id`	No	Optional job ID whose payload should be delivered (e.g. a specific monitor run's job). If omitted, the API picks the resource's payload itself.
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`webhook_id`	Yes	The webhook ID to deliver through.
`resource_id`	Yes	The ID of the job/monitor/monitor_group to trigger delivery for.
`resource_type`	Yes	Resource type: 'job', 'monitor', or 'monitor_group'.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

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. It states the tool triggers a manual delivery, which implies a non-destructive action. However, it does not disclose any potential side effects, rate limits, or authentication requirements beyond the optional api_key parameter.

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 extremely concise: two sentences plus a 'Use when' bullet. Every part is meaningful and there is no redundancy.

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's complexity (5 parameters, output schema exists), the description adequately covers its purpose and core use case. It could mention what to expect on success/failure, but the output schema likely covers return values.

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 parameter-level details beyond what the schema already provides, but the 'Use when' section gives context for why one might use the tool.

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 clearly states the action ('Manually trigger webhook delivery for a resource') and specifies the resource types (job/monitor/monitor_group). This distinguishes it from sibling tools like 'test_webhook' and 'delete_webhook'.

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 explicitly provides a 'Use when' section with a concrete scenario: re-sending a missed or failed delivery instead of waiting for automatic dispatch. It does not mention when not to use it, but the context is clear.

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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