Loading ToolBench
Build agents with any framework; ship them with FastAgentic. A framework for deploying AI agents with REST, MCP, and A2A interfaces with streaming support and checkpointing.
FastAgentic exposes 13 tools across multiple example applications. Critical issues: (1) Tool definitions are inferred from example files, not explicitly registered in visible source code, cannot verify actual schema registration. (2) Most tools lack detailed parameter descriptions; many parameters have generic or missing type info. (3) No input schemas visible for tools like 'analyze_content' and 'deep_research' (marked as 'type:object, description:AnalysisRequest model', schema not shown). (4) Descriptions are present but brief (10-50 chars), below the 50-200 char baseline for LLM optimization. (5) No error handling guidance, recovery hints, or output schema documentation visible. (6) Duplicate tool names ('agent-info' appears 3 times across examples) signal poor composition and naming clarity. (7) No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite all tools being READ_ONLY.
Get information about the research agent
Get information about the reasoning agent
Get information about this agent
Analyze a topic using the multi-agent crew
Get coding help from the AI assistant
Return crew information
Conduct deep research with checkpointing and streaming
Tool definitions inferred from example files, not explicitly visible in source code. Cannot verify actual schema registration, parameter types, or MCP protocol compliance. Schemas for 'analyze_content' and 'deep_research' marked as 'type:object' with no visible field definitions.
Duplicate tool names ('agent-info' appears 3 times across examples) violate single-responsibility principle and create ambiguity for LLM tool selection. Each should have a unique, descriptive name reflecting its context (e.g., 'autogen_agent_info', 'dspy_agent_info').
Descriptions are too brief (10-50 chars, baseline 50-200). Examples: 'Get an explanation of code' (28 chars), 'Return crew information' (23 chars). Lack context for LLM tool selection, prerequisites, and when to use vs. similar tools.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 46 | <=2025-11-25 | v2 |
Get an explanation of code
Explain a concept using reasoning
Answer a question with step-by-step reasoning
Conduct deep research on a topic with automatic checkpointing
Get a code review with suggestions
List supported programming languages
No input schemas visible for 'analyze_content' and 'deep_research', marked as 'type:object, description:AnalysisRequest model' with no field definitions. Cannot verify parameter types, required fields, or constraints. Violates schema documentation requirement.
No error handling guidance, recovery hints, or output schema documentation visible. Tools lack actionable error messages (e.g., 'If research fails, try a simpler topic'). No indication of retryability, side effects, or expected response structure.