CatchAll (by NewsCatcher)

create_webhook

Create a new webhook endpoint.

Use when:

You want to register a URL to receive job or monitor result deliveries.
You need a webhook_id to attach to a monitor (via webhook_ids) or a job submission.
You want the webhook associated with a project from the start (pass project_id).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	Target URL that will receive webhook deliveries (required).
`auth`	No	Optional auth object forwarded with each delivery. One of: - {"type": "bearer", "token": "..."} - {"type": "api_key", "header": "X-API-Key", "value": "..."} - {"type": "basic", "username": "...", "password": "..."}
`name`	Yes	Human-readable name for the webhook (required).
`type`	No	Optional webhook target type: 'generic' (default), 'slack', 'teams', or 'custom'. 'slack'/'teams' send pre-formatted payloads; 'generic'/'custom' send the raw result payload.
`method`	No	HTTP method for delivery (default 'POST'). One of GET, POST, PUT, PATCH, DELETE.	POST
`params`	No	Optional dict of query string parameters appended to the webhook URL.
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`headers`	No	Optional dict of custom HTTP headers to include in deliveries.
`project_id`	No	Optional project ID to associate this webhook with immediately upon creation. A webhook can belong to several projects at once; use `add_project_resources` (resource_type 'webhook') to attach it to more.
`delivery_mode`	No	Optional delivery mode: 'full' (default, whole result set in one call) or 'per_record' (one call per article).
`formatter_config`	No	Optional custom payload transformation config dict.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.8/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It says 'Create a new webhook endpoint' and implies mutation, but does not mention side effects, required permissions, what the response contains, or that creation is permanent. The note about project association is mildly useful but insufficient for a create operation.

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 compact parts: a one-line purpose statement and a three-bullet 'Use when' list. Every sentence earns its place, and the most important information is front-loaded. No filler or repetition.

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 (11 params, output schema present), the description provides strong usage context that complements the schema. It explains when to create a webhook and how the webhook_id will be used later. It does not explain return values, but the output schema presumably covers that, so the description does not need to.

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 schema already documents all 11 parameters thoroughly. The description adds only a brief hint about passing project_id, but this is already covered in the schema. Thus the description adds minimal value beyond the schema, matching the baseline.

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 opens with 'Create a new webhook endpoint' – a specific verb and resource that clearly distinguishes this from siblings like update_webhook, delete_webhook, get_webhook, and trigger_webhook. It leaves no ambiguity about what the tool does.

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 'Use when' bullet list gives concrete scenarios (registering a URL, needing a webhook_id for monitors/jobs, associating a project at creation). It provides clear context but does not explicitly mention when not to use it or name alternative tools, so it falls just short of a 5.

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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