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update_webhook

Update an existing webhook's configuration.

Use when:

  • You want to change a webhook's URL, method, headers, or other settings.

  • You want to enable or disable a webhook (set is_active).

  • Only the fields you provide are updated; omitted fields remain unchanged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoUpdated target URL.
authNoUpdated auth object. One of: - {"type": "bearer", "token": "..."} - {"type": "api_key", "header": "X-API-Key", "value": "..."} - {"type": "basic", "username": "...", "password": "..."}
nameNoUpdated webhook name.
typeNoUpdated webhook type: 'generic', 'slack', 'teams', or 'custom'.
methodNoUpdated HTTP method: one of GET, POST, PUT, PATCH, DELETE.
paramsNoUpdated dict of query string parameters.
api_keyNoCatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
headersNoUpdated dict of custom HTTP headers.
is_activeNoSet to false to disable the webhook (stop deliveries), true to re-enable it.
webhook_idYesThe webhook ID to update.
delivery_modeNoUpdated delivery mode: 'full' or 'per_record'.
formatter_configNoUpdated formatter configuration dict.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A3.9/5.0
Behavior3/5

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

No annotations provided, so the description carries full burden. It discloses partial update semantics ('omitted fields remain unchanged') but does not mention return behavior, permissions, idempotency, or side effects beyond configuration change.

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 concise with five sentences, front-loads the purpose, and uses bullet-style lines for usage. No redundant or wasted words.

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

Completeness3/5

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

Given 12 parameters and no annotations, the description is somewhat minimal. It covers the partial update behavior but does not explain output (though output schema exists) or potential error states. Adequate but not thorough.

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 parameters are fully documented. The description adds minimal extra meaning beyond listing examples (URL, method, headers), making it baseline adequate.

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 explicitly states 'Update an existing webhook's configuration' with a clear verb-resource pair. It distinguishes from sibling tools like create_webhook, delete_webhook, and get_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 'Use when' section lists specific contexts (changing URL, method, enabling/disabling) and notes partial update behavior. However, it lacks explicit when-not-to-use guidance or alternatives.

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.