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rlowndes9

Zendesk MCP Server

by rlowndes9

list_webhooks

Read-onlyIdempotent

Retrieve paginated webhooks from Zendesk with id, name, status, endpoint, and updated_at. Supports cursor, field filtering, and verbose mode for full objects.

Instructions

Returns webhooks (the modern outbound integration mechanism) as paginated skeletons (id, name, status, endpoint, updated_at). Default limit: 100; pass cursor, fields, filter, or verbose: true. For per-webhook delivery history use list_webhook_invocations with a webhook_id, that's where you'll find request/response timing and HTTP status. list_targets is the legacy equivalent, most modern instances should be on webhooks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax items to return. Default 100. The full corpus is fetched and cached server-side; this only limits what the response carries.
cursorNoOpaque pagination token from a previous response. Slices the next page from the cached corpus. Stale cursors (older than the cache TTL) auto-reset to offset 0 and set cursor_invalidated: true.
fieldsNoWhitelist of field names. Overrides the default projection. Use to opt into extra fields (e.g. ["id","title","active","position","category_id","updated_at"]) without going fully verbose.
filterNoStructured filter applied to the cached corpus before slicing. Supported keys: active (bool), category_id (number/string), title_contains (string, case-insensitive), updated_since (ISO timestamp). Unsupported keys are ignored with a note in the response.
refreshNoBypass cache and re-fetch from Zendesk
verboseNoReturn full webhook objects instead of the thin projection
instanceNoOverride the sticky instance for this call
Behavior5/5

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

Annotations already declare readOnly, openWorld, and idempotent hints. The description adds valuable behavioral details: default limit of 100, server-side caching, cursor pagination with stale cursor handling, filter application to cached corpus, and ignored unsupported filter keys. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear opening statement followed by parameter behavior and sibling references. Every sentence adds value, though it is slightly longer than necessary but not wasteful.

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?

Despite no output schema, the description covers return structure, pagination, filtering, caching, and all 7 parameters. Given the complexity and richness of the schema, it fully equips an agent to use the tool correctly.

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

Parameters5/5

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

Schema coverage is 100%, baseline 3, but the description significantly enhances each parameter: explains limit default and caching, cursor behavior (stale cursors auto-reset), fields whitelist usage, filter supported keys, refresh bypasses cache, and instance override. Adds substantial meaning beyond the schema.

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 tool returns webhooks as paginated skeletons, listing the specific fields (id, name, status, endpoint, updated_at). It distinguishes itself from sibling tools like list_webhook_invocations and list_targets, which are explicitly mentioned for different use cases.

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 provides explicit guidance on when to use this tool vs. alternatives: for per-webhook delivery history, use list_webhook_invocations; for legacy targets, list_targets. It does not state explicit when-not-to-use scenarios 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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