Minimal reference client for the DDMarketer MCP server. Connects over Streamable HTTP, lists tools, and runs the three open tools with no API key.
Four tools with basic descriptions and minimal parameter documentation. All tools are READ_ONLY and follow verb_noun naming (search_, get_, validate_, get_). However, descriptions are terse (10-20 chars), parameter descriptions lack detail on format/constraints, and output schemas are not documented in the source. No error handling guidance, no pagination support despite tools returning lists, and no tool annotations. The client code shows tools exist and are callable, but the server implementation details are not visible, tool definitions appear inferred from CLI usage rather than explicitly registered with full schemas.
Get detailed dossier information for a specific gap or market item
Get the top market gaps across all markets
Search for market gaps and pain points in a specific market or domain
Validate a product or business idea against market data and pain points
Descriptions are under 20 characters and lack context. 'Search for market gaps and pain points in a specific market or domain' is the longest; others are vague. LLMs cannot determine when to select these tools vs alternatives.
Output schemas are not documented. The CLI shows tools return objects with fields like 'gaps', 'verdict', 'matchCount', but no formal schema is visible in the source. LLMs cannot plan downstream calls or extract data reliably.
Parameter descriptions lack format/constraint details. 'query' is described as 'Market or domain to search for gaps' but does not specify length, format, or examples. 'limit' lacks min/max bounds (e.g. 1 - 100). LLMs will pass invalid values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 53 | 2025-06-18+ | v2 |
No pagination support visible. search_gaps and get_top_gaps accept 'limit' but no offset/cursor. If results exceed limit, the agent cannot fetch the next page. Large result sets will blow context windows.
No error handling guidance. If search_gaps returns no results, or validate_idea fails due to invalid input, the response does not tell the LLM what to do next (retry, ask user, try different tool). Agents will stall.