More AI Tools, Worse Choices? Build an Enterprise Tool Catalog First

A business connects orders, customer records, and a knowledge base to AI, expecting more tools to mean more help. Yet when someone asks whether an order is eligible for return, the agent may choose cancellation instead of eligibility lookup. Similar names, vague purposes, and too many options can be as important as model capability.

A recent signal: tools need discovery too

The SCOUT preprint submitted to arXiv on August 25, 2026 describes selecting relevant tools through search in a PayPal environment. This is an author-reported implementation, not a universal performance guarantee. Firebase also described skill activation and end-to-end evaluations on August 11. Together, these examples suggest that connecting capabilities and helping an agent use them correctly are distinct engineering tasks. The guidance below is Millionasia’s practical interpretation.

Describe each tool as a business service

Record purpose, suitable and unsuitable situations, required inputs, outputs, and an owner. Checking return eligibility reads rules and order status; creating a return request opens a case; issuing a refund moves money. Similar names do not adequately distinguish them. State whether a tool writes data, requires approval, and whether an error means nothing happened or the outcome is unknown.

Select for the task, then load the details

Think of a catalog as a library index: find a small set of candidates before reading their full operating specifications. MCP is one way to connect tools; discovery is a separate design concern and does not appear automatically with an MCP connection. A small catalog may need only clear grouping. At larger scale, keyword and semantic search may help. Too few candidates can hide the right tool; too many increase the selection burden. Tune the balance with real tasks.

Discoverability is not authorization

Filter visible tools by user, department, and data scope, then recheck identity and permissions in the backend at execution time. Search rank grants no authority, and a description cannot replace approval. Review catalog entries, version them, and track disabled tools so obsolete interfaces or untrusted descriptions do not slip in. For writes, confirm that the actual parameters match the approved action.

Use a return workflow as an acceptance exercise

This is a hypothetical scenario, not a client result. A request to check return eligibility should retrieve the rules and explain the result without issuing a refund. Creating a request is a separate action. A user without refund permission must remain blocked even if they know the tool name. Include duplicate customer names, missing order IDs, disabled tools, empty search results, and a user changing their mind. Missing information should trigger clarification or handoff, not a guessed tool call.

Measure completed work, not just fewer tokens

Establish a baseline with the existing integration. Compare correct tool selection, task completion, unintended writes, latency, and manual correction time. Discovery itself takes time and money; a shorter prompt does not necessarily make the workflow faster. Rerun representative cases after renaming, versioning, or adding tools. Record the selected tool, catalog version, and backend authorization result.

Millionasia’s recommendation

Start with one service or application workflow, a small set of common tools, and requests that are easy to confuse. Connect the website, app, existing APIs, permissions, and operational records around it. Enterprise AI integration increasingly depends on using the appropriate capability safely and reliably in context. A maintained tool catalog makes that goal manageable and testable.

References: SCOUT — arXiv preprint, 2026-08-25; Firebase — Eval-driven development, 2026-08-11