Fix ToolUniverse Tools

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

Instructions

When fixing a failing tool:

1. Run targeted test to identify error:

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

2. Verify API is correct – search online for official API documentation to confirm endpoints, parameters, and patterns are correct

3. Identify error type (see Error Types section)

4. Apply appropriate fix based on error pattern

5. Regenerate tools if you modified JSON configs or tool classes:

python -m tooluniverse.generate_tools

5. Check and update unit tests if they exist in tests/unit/:

ls tests/unit/test_<tool-name>_tool.py

6. Verify fix by re-running both integration and unit tests

7. Provide fix summary with problem, root cause, solution, and test results

Where to Fix

Error Types

1. JSON Parsing Errors

Symptom: Expecting value: line 1 column 1 (char 0)

Cause: Tool expects JSON but receives binary data (images, PDFs, files)

Fix: Check Content-Type header. For binary responses, return a description string instead of parsing JSON. Update return_schema to {"type": "string"}.

2. Schema Validation Errors

Symptom: Schema Mismatch: At root: ... is not of type 'object' or Data: None

Cause: Missing data field wrapper OR wrong schema type

Fix depends on the error:

• If Data: None → Add data wrapper to ALL operation methods (see Multi-Operation Pattern below)
• If type mismatch → Update return_schema in JSON config:
  • Data is string: {"type": "string"}
  • Data is array: {"type": "array", "items": {...}}
  • Data is object: {"type": "object", "properties": {...}}

Key concept: Schema validates the data field content, NOT the full response.

3. Nullable Field Errors

Symptom: Schema Mismatch: At N->fieldName: None is not of type 'integer'

Cause: API returns None/null for optional fields

Fix: Allow nullable types in JSON config using {"type": ["<base_type>", "null"]}. Use for optional fields, not required identifiers.

4. Mixed Type Field Errors

Symptom: Schema Mismatch: At N->field: {object} is not of type 'string', 'null'

Cause: Field returns different structures depending on context

Fix: Use oneOf in JSON config for fields with multiple distinct schemas. Different from nullable ({"type": ["string", "null"]}) which is same base type + null.

5. Invalid Test Examples

Symptom: 404 ERROR - Not found or 400 Bad Request

Cause: Test example uses invalid/outdated IDs

Fix: Discover valid examples using the List → Get or Search → Details patterns below.

6. API Parameter Errors

Symptom: 400 Bad Request or parameter validation errors

Fix: Update parameter schema in JSON config with correct types, required fields, and enums.

7. API Key Configuration Errors

Symptom: Tool not loading when API key is optional, or api_key parameter causing confusion

Cause: Using required_api_keys for keys that should be optional, or exposing API key as tool parameter

Key differences:

• required_api_keys: Tool is skipped if keys are missing
• optional_api_keys: Tool loads and works without keys (with reduced performance)

Fix: Use optional_api_keys in JSON config for APIs that work anonymously but have better rate limits with keys. Read API key from environment only (os.environ.get()), never as a tool parameter.

8. API Endpoint Pattern Errors

Symptom: 404 for valid resources, or unexpected results

Fix: Verify official API docs – check if values belong in URL path vs query parameters.

9. Transient API Failures

Symptom: Tests fail intermittently with timeout/connection/5xx errors

Fix: Use pytest.skip() for transient errors in unit tests – don’t fail on external API outages.

Common Fix Patterns

Schema Validation Pattern

Schema validates the data field content, not the full response. Match return_schema type to what’s inside data (array, object, or string).

Multi-Operation Tool Pattern

Every internal method must return {"status": "...", "data": {...}}. Don’t use alternative field names at top level.

Finding Valid Test Examples

When test examples fail with 400/404, discover valid IDs by:

• List → Get: Call a list endpoint first, extract ID from results
• Search → Details: Search for a known entity, use returned ID
• Iterate Versions: Try different dataset versions if supported

Unit Test Management

Check for Unit Tests

After fixing a tool, check if unit tests exist:

ls tests/unit/test_<tool-name>_tool.py

When to Update Unit Tests

Update unit tests when you:

1. Change return structure: Update assertions checking result["data"] structure
2. Add/modify operations: Add test cases for new operations
3. Change error handling: Update error assertions
4. Modify required parameters: Update parameter validation tests
5. Fix schema issues: Ensure tests validate correct data structure
6. Add binary handling: Add tests for binary responses

Running Unit Tests

# Run specific tool tests
pytest tests/unit/test_<tool-name>_tool.py -v

# Run all unit tests
pytest tests/unit/ -v

Unit Test Checklist

• Check if tests/unit/test_<tool-name>_tool.py exists
• Run unit tests before and after fix
• Update assertions if data structure changed
• Ensure both direct and interface tests pass

For detailed unit test patterns and examples, see unit-tests-reference.md.

Verification

Run Integration Tests

python scripts/test_new_tools.py <pattern> -v

Run Unit Tests (if exist)

pytest tests/unit/test_<tool-name>_tool.py -v

Regenerate Tools

After modifying JSON configs or tool classes:

python -m tooluniverse.generate_tools

Regenerate after:

• Changing src/tooluniverse/data/*_tools.json files
• Modifying tool class implementations

Not needed for test script changes.

Output Format

After fixing, provide this summary:

Problem: [Brief description]

Root Cause: [Why it failed]

Solution: [What was changed]

Changes Made:

• File 1: [Description]
• File 2: [Description]
• File 3 (if applicable): [Unit test updates]

Integration Test Results:

• Before: X tests, Y passed (Z%), N failed, M schema invalid
• After: X tests, X passed (100.0%), 0 failed, 0 schema invalid

Unit Test Results (if applicable):

• Before: X tests, Y passed, Z failed
• After: X tests, X passed, 0 failed

Common Pitfalls

1. Schema validates data field, not full response
2. All methods need {"status": "...", "data": {...}} wrapper
3. JSON config changes require regeneration
4. Use optional_api_keys for APIs that work without keys
5. Check official API docs for correct endpoint patterns
6. Unit tests should skip on transient API failures, not fail

Debugging

• Inspect API response: Check status code, Content-Type header, and body preview
• Check tool config: Load ToolUniverse and inspect the tool’s configuration
• Add debug prints: Log URL, params, status, and Content-Type in the run method

Quick Reference