Observability for LLM plugin and tool infrastructure

LLM plugin and tool observability records how an agent discovers a capability, chooses it, constructs arguments, obtains authorization, executes the call, and uses the result. Tool infrastructure is where model intent becomes an external side effect, so its traces must support debugging and audit.

Trace the complete tool lifecycle

Represent registry search, schema loading, credential resolution, permission checks, execution, retries, and result processing as related spans. Attach tool name and version, server identity, safe arguments, status, latency, retry count, and error class.

Keep secrets out of prompts and telemetry. Resolve credentials inside the trusted tool boundary and redact sensitive inputs and results before export.

Use stable contracts

Version tool schemas and descriptions. A wording or enum change can alter model selection even if backend behavior is unchanged. Link the served schema version to every call so regressions are reproducible.

Validate arguments before execution and return structured errors the model can act on. Distinguish authentication, authorization, validation, timeout, provider, and application errors; one generic “tool failed” category is not diagnosable.

Measure selection and execution

Track correct-tool rate, invalid arguments, denied actions, retry success, duplicate side effects, latency, and task completion. Tool-call success alone is not enough: a perfectly executed wrong tool is still an agent failure.

Use deterministic evaluators for schema and permission rules. Use semantic evaluation for whether tool choice advanced the user's goal. The guide to evaluating tool calling covers a complete metric set.

Design side effects for retries

Use idempotency keys for external mutations. Trace them as safe hashes so a duplicate request can be detected without exposing secrets. Require confirmation before consequential actions and verify the final response matches the actual tool result.

Test timeout after execution but before response—the classic case where a retry can repeat a successful side effect.

Observe MCP tools

For Model Context Protocol servers, trace tool discovery and calls on both client and server when possible, propagating the same trace context. Record server and tool versions. Treat descriptions and returned content as untrusted data; a tool response must not override system policy.

Currai spans can wrap local, HTTP, or MCP tools beneath the agent trace, while generations capture the decisions around them. That shared record lets platform teams diagnose whether a failure came from selection, authorization, infrastructure, or reasoning instead of blaming every error on the model.