Fast, deterministic NLP entity extraction and transcript cleaning pipeline with LLM adapters and MCP server support for AI Agents.
Qirrel provides 5 tools with complete JSON schemas and descriptions. All tools have proper input schemas with type definitions and required fields. Tool annotations (readOnlyHint, destructiveHint, idempotentHint) are present and correct. However, descriptions are brief (10-20 chars for tool names), and parameter descriptions lack actionable constraints (e.g., no min/max for concurrency, no format guidance for configPath). Output schemas are not documented. Error handling is minimal, no recovery guidance or categorization. Tool names use verb_noun pattern correctly (parse_text, parse_batch, speech_clean), but qirrel.capabilities and qirrel.tool_help are meta-tools that don't follow the primary action pattern.
Return Qirrel feature profile and interoperability surface for agent/tool selection
Parse many text inputs while preserving order
Parse one text input into a structured QirrelContext
Clean voice-to-text transcript by removing filler words, stutters, and repetitions
Explain available Qirrel tools, their arguments, and examples so an agent can choose and call correctly
Output schemas not documented. Tools return structured content but LLMs cannot predict field names, types, or nesting. Downstream tool chaining is unsupported.
Parameter descriptions lack actionable constraints. 'concurrency' has no min/max bounds; 'configPath' has no format or validation guidance. LLMs cannot self-correct invalid inputs.
No error handling or recovery guidance. Tools throw TypeErrors but do not guide LLMs on retry strategy, alternatives, or next steps.
Meta-tools (qirrel.capabilities, qirrel.tool_help) do not follow verb_noun naming. Names should be 'describe_capabilities' or 'list_tools' to clarify intent.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 57 | 2025-06-18+ | v2 |
No pagination or result limits documented. parse_batch accepts unbounded text arrays; no guidance on max batch size or token limits.