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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
NameRequiredDescriptionDefault
job_idNoOptional 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_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
webhook_idYesThe webhook ID to deliver through.
resource_idYesThe ID of the job/monitor/monitor_group to trigger delivery for.
resource_typeYesResource type: 'job', 'monitor', or 'monitor_group'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.8/5.0
Behavior2/5

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 only states 'trigger webhook delivery' without disclosing that this actually sends an HTTP request, potential side effects (e.g., external system impact), or error conditions. The description lacks details on authentication requirements (api_key is optional but not explained) and whether the delivery is synchronous or asynchronous.

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 bullet list. Every sentence adds value. The purpose is stated first, followed by explicit usage guidelines. No wasted words.

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 presence of an output schema, the description does not need to explain return values. It covers the essential purpose and usage, but could include more about behavioral aspects (e.g., synchronous vs. async, error handling). Still, it is complete enough to understand the tool's role.

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 parameters well. The description mentions 'resource' and 'webhook' but does not add additional meaning beyond the schema. Baseline score is appropriate.

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 verb ('trigger') and the resource ('webhook delivery for a resource (job/monitor/monitor_group)'). It distinguishes from siblings like 'test_webhook' (testing connectivity) and 'assign_webhook_resource' (linking resources) by specifying on-demand delivery instead of waiting for automatic dispatch.

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 states when to use ('when you want to (re-)send a webhook delivery on demand instead of waiting for automatic dispatch') and provides an example ('replay missed or failed delivery'). It does not explicitly state when not to use or mention alternatives, but the context is clear enough.

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.