devtu-fix-tool（devtu 修复工具）

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

1. Verify API is correct - search online for official API documentation to confirm endpoints, parameters, and patterns are correct
2. Identify error type (see Error Types section)
3. Apply appropriate fix based on error pattern
4. Regenerate tools if you modified JSON configs or tool classes:

python -m tooluniverse.generate_tools

1. Check and update tool tests if they exist in tests/tools/:

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

1. Verify fix by re-running both integration and unit tests
2. 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. 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
}

Fix: Make mutually exclusive parameters nullable:

{
  "neuron_id": {"type": ["integer", "null"]},      // ✅ Allows None
  "neuron_name": {"type": ["string", "null"]}      // ✅ Allows None
}

Common patterns:

• id OR name parameters (get by ID or by name)
• 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

Unit Test Management

Check for Unit Tests

After fixing a tool, check if unit tests exist:

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

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

Unit Test Checklist

• Check if tests/tools/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/tools/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

Testing Best Practices

Verify Parameter Names Before Testing

CRITICAL: Always read the tool's JSON config or generated wrapper to get the correct parameter names. Don't assume parameter names.

Example of incorrect testing:

# ❌ WRONG - assumed parameter name
AllenBrain_search_genes(query='Gad1')  # Fails: unexpected keyword 'query'

Correct approach:

# ✅ RIGHT - checked config first
# Config shows parameters: gene_acronym, gene_name
AllenBrain_search_genes(gene_acronym='Gad1')  # Works!

How to find correct parameter names:

1. Read the JSON config: src/tooluniverse/data/*_tools.json
2. Check the generated wrapper: src/tooluniverse/tools/<ToolName>.py
3. Look at test_examples in the JSON config

Systematic Testing Approach

When testing multiple tools:

1. Sample first: Test 1-2 tools per API to identify patterns
2. Categorize errors: Group by error type (param validation, API errors, data structure)
3. Fix systematically: Fix all tools with same issue type together
4. Regenerate once: Run python -m tooluniverse.generate_tools after all JSON changes
5. Verify all: Test all fixed tools comprehensively

Understanding Data Structure

Tools can return different data structures:

• Object: {"data": {"id": 1, "name": "..."}} - single result
• Array: {"data": [{"id": 1}, {"id": 2}]} - multiple results
• 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

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
7. Mutually exclusive parameters MUST be nullable - most common new tool issue
8. Verify parameter names from configs - don't assume or guess
9. 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