A Next.js-based marketing website for VEPANDO AI Agent services with WebMCP tool registration for browser-based AI model context protocol integration. Exposes agent discovery endpoints, API catalogs, and MCP server metadata.
Two tools with minimal definitions. Both have names starting with action verbs (get_, navigate_), but descriptions are extremely brief (under 100 chars). Input schemas are present but sparse: get_vepando_services has empty properties (no parameters), navigate_vepando_site has one enum parameter with description. No output schemas documented. No error handling guidance. No parameter validation rules or constraints beyond the enum. Tool definitions appear inferred from React component code rather than explicit MCP registration.
Return VEPANDO's AI Agent services and the booking URL for a free strategy session.
Navigate the current browser page to a relevant VEPANDO section.
No output schemas documented for either tool. LLMs cannot plan downstream calls or extract return values without knowing what fields to expect.
Tool definitions inferred from React component code, not explicitly registered in MCP server. Cannot verify actual tool registration, schema validation, or runtime behavior.
get_vepando_services description (79 chars) lacks context: what data is returned? When should the LLM call this vs navigate_vepando_site? What format is the booking URL?
navigate_vepando_site description (82 chars) does not explain what happens after navigation or what the tool returns. Does it return success/failure? A URL? Page content?
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 42 | 2026-07-28+ | v2 |
No error handling guidance. If navigate_vepando_site is called with an invalid section, what error is returned? How should the LLM recover?