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Agent framework written in Rust and built for Rust/Python. Provides multi-agent orchestration with supervisor patterns, supporting ReAct reasoning, loop agents, pipeline agents, and multi-agent collaboration.
This is a Python/Rust agent framework, not an MCP server. No MCP protocol implementation detected. Tools are defined in example Python scripts (examples/a2a/main.py, examples/a2a_plan_todo/main.py) with minimal schema documentation. Tool definitions are inferred from function signatures and docstrings rather than explicit MCP tool registration. Descriptions exist but are brief (10-50 chars for most tools). Input schemas are partially visible but lack formal JSON Schema with type constraints. No error handling guidance, no output schema documentation, no parameter validation rules. The bash tool is destructive with no confirmation pattern. Framework appears to be a multi-agent orchestration library, not an MCP-compliant server.
Delegate a web-search question to the search sub-agent. Use this when the user wants to look up information online.
Delegate a weather-related question to the weather sub-agent. Use this when the user asks about weather, temperature, or forecasts.
Execute a bash shell command and return the output.
Get current weather for a city via wttr.in.
List the contents of a directory.
Return the current working directory.
Read and return the content of a file.
Not an MCP server. Framework is a Python/Rust agent orchestration library without MCP protocol implementation. Tools are defined in example scripts, not via MCP tool registration.
Destructive bash tool lacks confirmation pattern. No dry-run, no confirmation_before_execute, no audit trail. Agents can execute arbitrary shell commands with no safeguards.
Tool descriptions are too brief (10-50 chars). Rubric baseline is 194 chars. Descriptions lack WHEN to use, WHAT it returns, and dependencies. Examples: 'pwd' (20 chars), 'ls' (35 chars).
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 38 | 2026-07-28+ | v2 |
Send a task message to a sub-agent via A2A communication.
Manage a todo list. Actions: 'add' (requires 'item'), 'list', 'complete' (requires 'index'), 'remove' (requires 'index').
Search the web via DuckDuckGo Instant Answer API.
Write content to a file at the given path.
Input schemas lack formal JSON Schema with type constraints and enums. 'todo' action parameter accepts free-form strings instead of enum(add|list|complete|remove). No validation rules in descriptions.
No output schemas documented. Tools return unstructured responses (strings, lists). LLMs cannot plan downstream calls or extract structured data. No pagination, no field documentation.