Agently Tools

This skill covers Agently's tool system for local Python tools, built-in tools, and request-time tool loops. It focuses on tool registration, agent scoping, loop control, built-in Search/Browse/Cmd usage, and result inspection. It does not cover MCP transport registration, TriggerFlow orchestration, or generic web-service design.

Prerequisite: Agently >= 4.0.8.5.

Scope

Use this skill for:

• @agent.tool_func
• agent.register_tool(...)
• agent.use_tools(...)
• built-in Search, Browse, and Cmd
• set_tool_loop(...)
• custom plan-analysis or tool-execution handlers
• tool logs and tool results from one model request

Do not use this skill for:

• MCP server registration or MCP transport choices
• model setup, output schema design, or structured streaming as the main problem
• TriggerFlow workflow design
• FastAPI service exposure

Workflow

1. Start with references/registration-and-selection.md when choosing how a tool should be registered or attached to an agent.
2. Read references/tool-loop-and-control.md when the issue is loop behavior, rounds, concurrency, timeout, or custom handlers.
3. Read references/builtin-tools.md when the task should use Search, Browse, or Cmd.
4. Read references/observability-and-results.md when the task is about tool logs, action results, or response inspection.
5. If the tool comes from an MCP server, switch to agently-mcp.
6. If the task becomes a workflow or multi-step orchestration problem, switch to agently-triggerflow-playbook.
7. If behavior still looks wrong, use references/troubleshooting.md.

Core Mental Model

Agently tools are request-time capability extensions.

• tools are registered on the shared tool manager
• agent.use_tools(...) scopes selected tools to one agent by tag
• the tool loop plans, executes, and feeds tool results back into the same request
• the final response keeps tool logs in metadata

Agently guidance for tool work should remain async-first:

• prefer async tool functions
• prefer async built-in tool calls
• cap tool-loop rounds and concurrency explicitly

Selection Rules

• local deterministic Python helper -> @agent.tool_func or register_tool(...)
• attach one or more already-registered tools to an agent -> use_tools(...)
• expose web search, browsing, or shell access with existing helpers -> built-in tools
• tune max rounds, concurrency, or timeout -> set_tool_loop(...)
• replace planning or execution policy -> custom plan-analysis or execution handler
• external tool server instead of local Python function -> agently-mcp

Important Boundaries

• the tool loop is an agent/request capability, not a TriggerFlow substitute
• MCP registration belongs to agently-mcp, even though MCP tools later participate in the same tool loop
• response metadata and output control are separate concerns; use agently-output-control when the main problem is response consumption

References

• references/source-map.md
• references/registration-and-selection.md
• references/tool-loop-and-control.md
• references/builtin-tools.md
• references/observability-and-results.md
• references/troubleshooting.md