Local AI agent orchestration tool with MCP server support for webhooks and admin operations
Single tool 'send_webhook' has a clear action verb and acceptable description (89 chars), but critical gaps undermine quality. Input schema is present with two parameters (url, payload), both typed as string/object with descriptions. However, the schema lacks critical constraints: no URL format validation, no payload structure documentation, no response schema defined. The tool performs a destructive write operation (POST to external webhook) but lacks error handling guidance, retry semantics, or idempotency markers. No tool annotations (destructiveHint) despite being a write operation. Output structure is undocumented, callers cannot know what success looks like or how to handle failures. Baseline: 194 chars avg description (this tool: 89 chars, acceptable); 100% of A+ tools document return types (this tool: none). The tool is functional but falls short of production-grade quality.
Send unauthenticated JSON POST requests to webhook URLs when explicitly instructed
No output schema documented. Callers cannot know what fields the response contains, success criteria, or how to handle errors. This forces LLMs to guess and wastes tokens on error recovery.
Destructive write operation (POST to webhook) lacks tool annotations. Missing destructiveHint annotation prevents agents from understanding irreversible consequences and planning accordingly.
No error handling guidance. Tool description does not explain what happens on network failure, invalid URL, or webhook rejection. LLMs cannot determine if errors are retryable or fatal.
URL parameter lacks format constraint. Schema should specify 'format: uri' or 'pattern: ^https?://' to prevent LLMs from passing invalid URLs. Current schema accepts any string.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 47 | 2026-07-28+ | v2 |
Payload parameter lacks structure documentation. No guidance on what fields are expected, required vs optional, or valid JSON structure. LLMs must infer payload shape from context alone.