Fix failing ToolUniverse tools by diagnosing test failures, identifying root causes, implementing fixes, and validating solutions. Use when ToolUniverse tools fail tests, return errors, have schema validation issues, or when asked to debug or fix tools in the ToolUniverse framework.
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Fix failing ToolUniverse tools by diagnosing test failures, identifying root causes, implementing fixes, and validating solutions. Use when ToolUniverse tools fail tests, return errors, have schema validation issues, or when asked to debug or fix tools in the ToolUniverse framework.
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?
Prevent, don't recover — fix the root cause so the failure can't happen, rather than adding hint text after it does
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
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
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
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.
src/tooluniverse/data/*_tools.json (use optional_api_keys not required_api_keys)
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. Mutually Exclusive Parameter Errors
Symptom: Parameter validation failed for 'param_name': None is not of type 'integer' when passing a different parameter
Cause: Tool accepts EITHER paramA OR paramB (mutually exclusive), but both are defined with fixed types. When only one is provided, validation fails because the other is None.
Example:
{"neuron_id":{"type":"integer"},// ❌ Fails when neuron_name is used"neuron_name":{"type":"string"}// ❌ Fails when neuron_id is used}
acronym OR name parameters (search by symbol or full name)
Optional filter parameters that may not be provided
Important: Also make truly optional parameters (like filter_field, filter_value) nullable even if not mutually exclusive.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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
String: {"data": "description text"} - text response
Test accordingly:
# For object data
result = tool()
data = result.get('data', {})
value = data.get('field_name') # ✅# For array data
result = tool()
items = result.get('data', [])
count = len(items) # ✅
first = items[0] if items else {} # ✅
Common Pitfalls
Schema validates data field, not full response
All methods need {"status": "...", "data": {...}} wrapper
JSON config changes require regeneration
Use optional_api_keys for APIs that work without keys
Check official API docs for correct endpoint patterns
Unit tests should skip on transient API failures, not fail
Mutually exclusive parameters MUST be nullable - most common new tool issue
Verify parameter names from configs - don't assume or guess
Test with correct data structure expectations - list vs dict vs string
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
Task
Command
Run integration tests
python scripts/test_new_tools.py <pattern> -v
Run unit tests
pytest tests/tools/test_<tool-name>_tool.py -v
Check if unit tests exist
ls tests/tools/test_<tool-name>_tool.py
Regenerate tools
python -m tooluniverse.generate_tools
Check status
git status --short | grep -E "(data|tools|.*_tool.py|tests/tools)"
Error Type
Fix Location
JSON parse error
src/tooluniverse/*_tool.py run() method
Schema mismatch
src/tooluniverse/data/*_tools.json return_schema
404 errors
src/tooluniverse/data/*_tools.json test_examples or endpoint
