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mh-zeynal

gapify-mcp-server

by mh-zeynal

Webhooks: Add a webhook

gapify_add_webhook

Add a webhook subscription to an account to receive event notifications at a specified URL. Choose which events to subscribe to.

Instructions

Add a webhook subscription to the account [Webhooks] POST /api/v1/accounts/{account_id}/webhooks Auth: User API key (header api-access-token). Body fields: url, subscriptions. Extra fields can be passed via extra_body. Text values may be written in Persian (Farsi) or any other language and are sent unchanged; IDs, numbers and phone numbers may use Persian or Arabic-Indic digits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoThe url where the events should be sent Example: "https://example.com"
account_idYesThe numeric ID of the account
extra_bodyNoAdditional JSON body fields to merge into the request (for keys not listed above).
subscriptionsNoThe events you want to subscribe to. Example: ["conversation_created"]

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly=false, idempotent=false, destructive=false, openWorld=true), so the bar is lower. The description adds real value beyond that: required auth header, HTTP endpoint, body field set, the extra_body escape hatch, and the Persian/Arabic-Indic digit handling for inputs. It stops short of stating behavior on duplicate URLs or success/error responses.

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?

Purpose is front-loaded, then auth and body details follow in a compact block. Every sentence carries information (endpoint, auth, fields, encoding note); it is dense but not padded.

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?

With no output schema and annotations covering the safety profile, the description is nearly self-sufficient: it supplies endpoint, auth mechanism, required body fields, and encoding caveats. It could go further on valid subscription event values and failure behavior, but nothing essential for a correct call is missing.

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 url, account_id, subscriptions, and extra_body, making 3 the baseline. The description restates the body fields and notes the language/digit-input flexibility, which adds minor context but no syntax the schema lacks.

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?

States a specific verb and resource ('Add a webhook subscription to the account') and the endpoint. It is clearly distinguishable from siblings gapify_list_webhooks, gapify_update_webhook, and gapify_delete_webhook, which cover the other CRUD operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the create verb, but the description never states when to add a webhook versus updating or listing them, nor any prerequisites beyond the auth key. No alternatives or exclusions are named, so guidance remains implicit.

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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