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Parallel multi-agent deep research engine — researches 10 threads as fast as 1
Three tools with complete input schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. Tool names are action-oriented (research, quick_search, follow_up), which is good. However, parameter descriptions lack detail on constraints, formats, and valid ranges. Output schemas are not formally documented, only inferred from code. Error handling is minimal. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite tools being read-only. Descriptions are adequate (100-150 chars) but could be more LLM-optimized with dependency hints and recovery guidance.
Ask a follow-up question based on a previous research session.
Run a fast, shallow research query (depth=1, 2-3 sources).
Run a deep research query and return a comprehensive report.
Output schemas not formally documented. Tools return markdown strings, but LLMs cannot plan downstream operations without knowing the structure. Document return type as structured object with fields (title, sections, citations, etc.) or at minimum describe markdown format expectations.
Parameter constraints missing. 'depth' accepts 1-3 but description lacks min/max bounds. 'output_format' and 'search_backend' lack enum constraints, LLMs may hallucinate invalid values like 'json' or 'google'. Add explicit enums or range declarations.
No tool annotations despite read-only semantics. All three tools are safe to retry and have no side effects, but lack readOnlyHint annotation. This prevents clients from optimizing caching and retry logic.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 58 | 2026-07-28+ | v2 |
Error handling lacks recovery guidance. follow_up returns 'Session not found' but does not suggest calling research() to start a new session. research() and quick_search() have no documented error cases (network failure, invalid model, search backend down). Add actionable error messages.
Parameter descriptions lack format/dependency hints. 'model' parameter accepts 'openai/gpt-4o' format but description does not specify the provider/model syntax. 'search_backend' lists three options but does not explain when to use each or what credentials are required.