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list_webhooks

Audit active webhook subscriptions on the relay, viewing URLs, events, filters, and secret presence—without exposing raw secrets.

Instructions

List every webhook subscription registered on the relay.

When to use: sanity-checking integrations ('is the Slack notifier still wired up?'), pre-cleanup audits, or building an admin UI. To narrow by event you currently filter client-side from this list.

Behavior: pure read. The raw HMAC secret is NEVER returned, each row exposes has_secret: boolean only. Auth: any registered agent (subscriptions are observable so admins can audit them, but secrets stay write-only).

Returns: { webhooks: { id, url, event, filter, has_secret, created_at }[], count }.

Errors: AUTH_FAILED, RATE_LIMITED.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_tokenNoYour agent token (from register_agent response). Optional here — also resolvable from RELAY_AGENT_TOKEN env or X-Agent-Token header.
Behavior4/5

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

With no annotations provided, the description carries the full burden. It explicitly states this is a pure read, that the raw HMAC secret is never returned (only has_secret boolean), specifies auth requirements (any registered agent), and discloses errors like AUTH_FAILED and RATE_LIMITED. This is thorough behavioral disclosure for a read tool.

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 tightly organized with clearly labeled sections (When to use, Behavior, Returns, Errors). Every sentence delivers distinct value — no filler, no redundancy with the schema. It front-loads the core purpose and groups related information efficiently.

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?

For a parameterless-read tool with 100% schema coverage on its single optional param, this description is complete. It covers purpose, usage context, security guarantees, auth model, return format, and error codes. An output schema is absent, so specifying the exact return shape is appropriate and enhances completeness.

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

Parameters4/5

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

The single parameter agent_token has 100% schema coverage in the schema. The description adds clarity by explaining it's optional and resolvable via env var or header. Since schema coverage is high, the baseline is 3, and the extra context about alternative resolution methods earns a 4.

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 lists every webhook subscription on the relay. It uses a specific verb+resource combination and differentiates from siblings like register_webhook and delete_webhook by describing the read/list nature. The return shape and event filtering are explicit.

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?

Provides concrete when-to-use scenarios (sanity-checking integrations, pre-cleanup audits, admin UI) and notes client-side filtering limitation for narrowing by event. It doesn't explicitly name an alternative tool for server-side filtering, but the context of siblings is sufficient to guide selection.

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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