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name: agents-tools-and-streaming description: "Use AdalFlow Agent, Runner, ReActAgent, FunctionTool, ToolManager, streaming events, permissions, and MCP tools safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

Agents, Tools, and Streaming

Use this sub-skill when the task involves AdalFlow agent execution, tool wrapping, tool calling, streaming run events, human approval for tools, or MCP-served tools.

Route here for

• Wrapping Python callables as FunctionTool, including sync, async, sync-generator, async-generator, and bound-method tools.
• Inspecting or constructing FunctionDefinition, Function, FunctionExpression, FunctionOutput, or ToolOutput values.
• Building a ToolManager, parsing function expressions, or executing tool calls directly.
• Constructing Agent, Runner, or legacy ReActAgent workflows after the model client / generator setup is known.
• Calling Runner.call, Runner.acall, or Runner.astream and consuming RunnerResult, step history, and stream events.
• Adding human-in-the-loop approvals through PermissionManager, CLI/FastAPI handlers, require_approval, or pre-execution confirmation details.
• Integrating MCP tools through MCP server parameter dataclasses, MCPFunctionTool, and MCPToolManager.

Route elsewhere

• Model client, provider credentials, Generator, prompt/model kwargs, and provider streaming setup belong to model-client-and-generator-workflows.
• DataClass schemas, parser design, and general structured output repair belong to core-components-and-structured-io unless the schema is specifically an agent final answer_data_type.
• Retrieval/RAG tools belong to retrieval-rag-and-data-pipelines before wrapping them for agent use here.
• Tracing spans, logs, MLflow, and persistent observability belong to tracing-observability-and-configuration.
• Training and optimizer behavior for agents/tools belongs to evaluation-and-optimization.

Read these references

1. references/agents-and-tools.md for FunctionTool, ToolManager, Agent, Runner, ReActAgent, tool outputs, and fake-planner testing.
2. references/streaming-and-run-results.md for RunnerResult, StepOutput, streaming event types, and robust event consumption loops.
3. references/mcp-and-permissions.md for tool approval flows, permission handlers, and MCP manager/server parameter patterns.
4. references/troubleshooting.md for unsafe tools, missing annotations, async-loop issues, result formatting, max-step exhaustion, finalization, permission denials, stream consumption, and missing MCP dependencies.

Safe bundled checks

• python scripts/function_tool_smoke.py verifies service-free FunctionTool and ToolManager behavior.
• python scripts/agent_runner_fake_planner_smoke.py verifies Agent + Runner sync, async, streaming, and permission behavior with a fake planner and no provider calls.

Run these only in an environment where adalflow is importable. They do not require API keys, network services, provider calls, or MCP servers.