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name: tools-and-agents description: "Defines, audits, executes, and composes Mellea 0.8.0.dev0 tools and ReAct agents with explicit schema, approval, sandbox, hook, MCP, and context-compaction boundaries." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Tools and agents

Use this route when a task exposes a Python callable, shell or Python execution, MCP/LangChain/smolagents tool, model tool call, ReAct loop, tool hook, or conversation compaction policy. Treat every model-produced tool request as untrusted data until its tool name, schema, arguments, side effects, and execution boundary have been approved.

Route first

1. Read the API reference for exact imports, return values, tool-call parsing/validation, execution tiers, and optional dependencies.
2. Read workflows for the smallest suitable recipe: plain callable, manual tool loop, ReAct, MCP, framework adapter, or compaction.
3. Read safety and policies before registering an execution-capable, networked, credentialed, or MCP tool.
4. For failures, use troubleshooting. Run the bundled static checker before manually approving a serialized request: python scripts/audit_tool_request.py --help.

Operating rules

• A tool schema advertises a callable; it is not approval to call it. Prefer narrow, typed functions and explicit names. Use @tool or MelleaTool.from_callable; call .run() for a deliberate direct call.
• instruct(), act(), and aact() can produce tool calls but do not execute them. Use call_tools()/acall_tools() for an explicit execution boundary. react() is different: it executes each non-final tool turn itself, so its registered tool list is an execution allowlist, not merely context.
• Validate both the request shape and the business policy. Use validate_tool_arguments(..., strict=True) for schema validation at a manual boundary, and uses_tool/tool_arg_validator when generation must satisfy a requirement-and-repair loop. Lenient validation returns original arguments; it is not a safety gate.
• Treat local shell and local Python tiers as host execution. Capability policy booleans on local tiers are declarations, not isolation. Prefer static checks for inspection and Docker-backed execution for untrusted code; still review network, package, filesystem, credential, and artifact behavior.
• Discover MCP tools first, inspect/filter their names and schemas, then wrap only approved specs. Treat server descriptions, results, headers, stdio commands, and environment values as untrusted boundary data.
• Use tool_pre_invoke for the final allowlist/argument/redaction decision and tool_post_invoke for audit or output redaction. Preserve ReAct's internal final_answer control-flow tool unless the policy intentionally handles is_control_flow.
• Pin the ReAct initiator before compaction. Keep backend-calling summarizers out of per-append ChatContext compaction; use a per-turn or thresholded compactor instead.

Core generative-program design, session/context generation, and validation repair belong to generative-programming. Provider/model selection belongs to backends-and-models. Serving, m, and endpoint deployment belong to serving-and-cli. Telemetry and general plugin lifecycle belong to observability-and-extensions; this route owns the tool-execution hook payload and policy decision.