A custom reactive AI Agent framework for LLM-driven task execution with MCP integration, tool management, and intelligent agent orchestration
The Reactive Agents Framework provides 10 tools with varying quality. Six playground tools (web_search, get_weather, get_crypto_price, calculate, analyze_sentiment, fetch_data) have basic function-level docstrings and parameter descriptions. Four system tools (final_answer, signal_stuck, request_strategy_switch, request_clarification) are sparse. Tool names follow verb_noun convention, but descriptions are brief (10-50 chars in many cases), and output schemas are not documented. Parameters have type information and descriptions, but lack validation constraints (enums, ranges). No evidence of pagination support, error recovery guidance, or destructive operation confirmation patterns. The framework appears to be in alpha (v0.1.0a7), with room for maturity before production use.
Analyze text sentiment (simulated).
Evaluate a mathematical expression safely.
Fetch mock data of a specified type.
Provides final answer and concludes task
Get cryptocurrency price (simulated).
Get weather for a location (simulated).
Request clarification or additional information
Output schemas not documented. Tools return strings or objects with no documented field structure. LLMs cannot infer what fields to expect or how to chain downstream calls.
Descriptions are under 20 characters for 'signal_stuck' (21 chars) and very brief (40-60 chars) for system tools. Tool selection descriptions should be 50-200 characters explaining WHAT, WHEN, and WHY.
No input validation or constraints. 'fetch_data' accepts free-form 'data_type' string (should enumerate: weather, stock, news). 'get_weather' accepts any location string (should enumerate supported cities). Free-form inputs invite hallucinated values.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 55 | <=2025-11-25 | v2 |
| 2026-03-09 | D | 50 | 1.4.1+ | v1 |
Request a different reasoning strategy
Signal being stuck and request framework intervention
Search the web (simulated).
No error recovery guidance. Tools have no documented error cases, retryability, or actionable error messages. LLMs cannot determine whether to retry, ask user, or abandon.
System tools (signal_stuck, request_strategy_switch, request_clarification) lack clarity on when the LLM should invoke them vs proceeding with available tools. Descriptions do not explain the triggering condition or expected behavior.
No pagination support. Tools like 'web_search' and 'fetch_data' return unspecified result counts. If 'web_search' returns 5 results, can the LLM request more? Is there a next_cursor? Missing pagination forces large result sets into context or requires extra calls.
'num_results' parameter in 'web_search' has no min/max constraints (e.g., 1-100). LLMs may pass absurd values (0, 10000) without validation.