Vision MCP server for software intelligence, mapping, and analysis
This server has only 1 tool ('applications') with critical definition gaps. The tool name does not start with an action verb (should be 'list_applications' or 'get_applications'), making intent ambiguous. The description is minimal (22 chars) and lacks WHEN/WHY context required for LLM tool selection. The input schema is present but sparse: only 'page' parameter with a description, but no information about page size limits, minimum/maximum values, or expected range. No output schema is documented, the LLM cannot predict what fields to extract from the result. The tool appears designed for pagination support (good signal), but lacks the structured output guidance needed for reliable agent composition. No error handling patterns visible. Security aspects (if calling an external Vision API) are not evident in the schema. Overall, this server fails to meet production-quality baseline for LLM-driven tool composition.
Available applications with Vision server. Paginated results.
Tool name 'applications' does not start with action verb. Should be 'list_applications' or 'get_applications' to clearly signal the operation to LLMs.
Tool description is only 22 characters ('Available applications with Vision server. Paginated results.'). Too short to guide LLM tool selection, missing WHAT, WHEN, and any prerequisites or return structure hints.
No output schema documented. LLM cannot predict response structure, fields, or how to chain this tool with others. Prevents reliable composition.
Input parameter 'page' lacks constraints: no minimum/maximum, no indication of valid range, no default value guidance. Parameter description does not explain consequences of invalid page numbers or what page size is used.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 27 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 13 | - | v1 |
No pagination result structure defined. Tools accepting pagination parameters must document and return: current_page, total_pages, page_size, total_items, has_next, has_previous, and per-item results.
No error handling guidance. If page is out of range, invalid type, or the API fails, no recovery instructions provided to LLM.