Fix ToolUniverse Tools

Fix ToolUniverse Tools

Diagnose and fix failing ToolUniverse tools through systematic error identification, targeted fixes, and validation.

First Principles for Bug Fixes

Before writing any fix, ask: why does the user reach this failure state?

1. Prevent, don't recover — fix the root cause so the failure can't happen, rather than adding hint text after it does
2. Validate at input, not at output — wrong parameters, unknown disease names, unsupported drugs should be caught and rejected early with clear guidance, not discovered after a silent API call
3. Don't mask silent mutations — if input is auto-normalized (fusion notation, Title Case), either accept both forms natively OR reject with explicit guidance; never silently transform and hide it
4. Distinguish "no data" from "bad query" — zero results because the filter is wrong is different from zero results because the data doesn't exist; the response must distinguish these clearly
5. Fix the abstraction, not the instance — if a parameter name is inconsistent, fix the interface; don't add an alias list that grows forever

Anti-patterns to avoid:

• Adding hint text to zero-result messages instead of validating upfront
• Adding parameter aliases instead of fixing naming consistency
• Post-hoc probing to rescue a failed query instead of pre-validating

Bug Verification (CRITICAL)

Before implementing any bug report, verify it via CLI first:

python3 -m tooluniverse.cli run <ToolName> '<json_args>'

Many agent-reported bugs are false positives caused by MCP interface confusion. Always confirm the bug is reproducible before implementing a fix.

Instructions

When fixing a failing tool:

1. Run targeted test to identify error:

python scripts/test_new_tools.py <tool-pattern> -v