Boost your tools: Introducing ToolOps, the tool lifecycle extension in ALTK

As enterprise developers begin to deploy agents, they face a common challenge: the tools they rely on are not always ready for agent use. We recently introduced the Agent lifecycle Toolkit (ALTK), which includes components that can stabilize tool calls and post-processing, but many failures originate much earlier. Tools may have unclear descriptions, or sparse metadata that make it difficult for agents to select and invoke them reliably. This results in incorrect tool selection, malformed arguments, and brittle agent behavior that is hard to diagnose at scale.

To address this gap, we are introducing ToolOps, a new set of build time ALTK components that help teams build, prepare, and validate tools for enterprise-grade agentic workflows. ToolOps focuses on improving tool semantics, generating test scenarios, and validating the ways that agents interact with tools before they’re deployed.

Why use ToolOps

Even simple Python tools can be difficult for agents to use without clear semantics. Missing descriptions, or vague parameter names often lead to incorrect arguments or tool selection. ToolOps provides a structured way to improve tool clarity and evaluate readiness before deployment.

ALTK ToolOps components

ToolOps operates at the build stage of the ALTK lifecycle and introduces three modular capabilities to enhance and evaluate a tool:

Tool Enrichment

Tool Enrichment analyzes a Python tool and produces clearer metadata for agent use by refining the tool description, clarifying parameter descriptions, and generating examples consistent with tool functionality. This helps agents understand when a tool should be used and how to supply valid arguments. In our evaluations, applying enriched tool metadata yielded up to a roughly 10% improvement in correct tool invocations, particularly those with complex input schemas.

Test Case Generation

This component generates diverse test inputs and expresses them as natural language phrases. These scenarios mimic user queries and help evaluate whether an agent can identify the correct tool and format arguments appropriately. Test case generation enhances test coverage, prevents runtime issues, and strengthens regression testing.

Tool Validation

Tool Validation runs these phrases through an agentic workflow (such as LangGraph ReAct) and inspects the agent’s behavior. It highlights tool selection errors, argument mismatches, output parsing issues, and categorizes them based on an error taxonomy. In our evaluations, a major source of errors stemmed from incorrect generations of input schemas, particularly parameter type or value mismatches, which was observed in 13% to 19% of test cases. Based on this error taxonomy, the module provides targeted recommendations for tool repair.

ToolOps in use

For tool developers, we put together this demo where we follow a minimally defined Python tool through the ToolOps lifecycle. Tool Enrichment automatically refines the metadata, Test Case Generation simulates user queries, and Tool Validation reveals errors that surface, and provides recommendations to strengthen the tool before production use.

ToolOps integrates seamlessly with the ContextForge MCP Gateway, enabling tool enrichment, test case generation, and validation. We also created a demo where MCP tools registered with sparse metadata are automatically enriched at the gateway layer, and agent interactions are evaluated using Test Case generation and Tool Validation.

Getting started

We are excited to have ToolOps in ALTK as build time components. The README files includes sample pipelines to help you get started quickly.

As part of ALTK, ToolOps is open, modular, and extensible. We invite builders to explore ToolOps and build it together with the community.