[ca] Update Claude Code CLI to v2.0.31

[github-actions]

Update Claude Code CLI to v2.0.31

Summary

Claude Code has been updated from 2.0.30 → 2.0.31. This update includes a new CLI flag for tool specification.

Version Details

• Previous: 2.0.30
• New: 2.0.31
• Released: 2025-10-31 (redacted) UTC (Oct 31, 2025 at (redacted) PM UTC)
• Time Since Previous: ~1 day (2.0.30 was released Oct 30, 2025)

Changes

New Features

New CLI Flag: --tools

• Description: Specify the list of available tools from the built-in set
• Usage: --tools <tools...>
• Options:
  • "" - Disable all tools
  • "default" - Use all tools
  • Tool names - e.g. "Bash,Edit,Read"
• Constraint: Only works with --print mode
• Impact: Provides fine-grained control over which tools are available during non-interactive execution

Breaking Changes

None detected.

Bug Fixes

Not specified in release notes.

Security Updates

No CVEs or security patches mentioned.

CLI Discovery

New command-line flag identified via claude --help comparison:

+ --tools <tools...>                                Specify the list of available tools from the built-in set. Use "" to disable all tools, "default" to use all tools, or specify tool names (e.g. "Bash,Edit,Read"). Only works with --print mode.

Previous help output: /tmp/gh-aw/cache-memory/claude-code-2.0.30-help.txt
Current help output: /tmp/gh-aw/cache-memory/claude-code-2.0.31-help.txt

Impact Assessment

• Risk Level: Low
• Affected Features:
  • Claude engine execution in --print mode
  • Tool restriction/allowance during non-interactive sessions
• Migration Required: No
• gh-aw Compatibility: Fully compatible - new flag is optional

Files Modified

• pkg/constants/constants.go - Updated DefaultClaudeCodeVersion constant
• 22 workflow .lock.yml files - Recompiled with new version

Verification

✅ make recompile completed (64/65 workflows compiled successfully)
✅ Git status shows 23 files modified (1 constants + 22 workflows)
✅ No breaking changes detected
✅ Help output cached for future comparisons

Note: 1 workflow compilation error (changeset-generator.firewall.md) is unrelated to this version update - it's a pre-existing issue.

Changelog Links

• NPM Package: https://www.npmjs.com/package/`@anthropic-ai/claude-code`/v/2.0.31
• NPM View: npm view @anthropic-ai/claude-code@2.0.31

Recommendations

1. ✅ Update constants file (completed)
2. ✅ Recompile workflows (completed)
3. ⏳ Review and merge this update
4. 📝 Consider documenting the new --tools flag in Claude engine documentation if we expose it to users
5. 🧪 Test the new flag in non-interactive mode if relevant to gh-aw workflows

Intermediate Versions

Only one version between 2.0.30 and 2.0.31:

• 2.0.31 (2025-10-31 (redacted) UTC) - Current update

Context

• Repository: githubnext/gh-aw
• Workflow Run: #18998462979
• Date: 2025-11-01

AI generated by CLI Version Checker

[pelikhan]

/scout investigate the use of --tools in the Claude agentic engine

[github-actions]

Agentic Scout triggered by this issue comment.

[github-actions]

🔍 Scout Research Report

Triggered by @chriswales95

Executive Summary

Comprehensive research on JSON schema discovery utilities and jq-based structural analysis tools reveals a growing ecosystem of solutions for managing large JSON responses, particularly in API development and CI/CD contexts. The jqschema utility concept—generating compact structural schemas from JSON data—aligns with industry best practices for efficient data exploration and API response analysis.

Click to expand detailed findings

Research Findings

Topic 1: jq MCP Servers and Schema Discovery Tools

Key Finding: The emerging ecosystem of MCP (Model Context Protocol) servers provides AI agents with structured JSON querying capabilities:

• Berry Development's jq MCP Server: Provides get_jsonschema and query_json tools that enable AI agents to first understand JSON structure, then execute targeted queries
• Workflow Pattern: Discovery → Understanding → Extraction (agents retrieve schema first, then build precise queries)
• Use Case: Processing large JSON datasets without loading entire files into LLM context windows

Source: Stop Drowning in JSON: A Deep Dive into Berry Dev's jq MCP Server

Relevance: The jqschema.sh utility implements a similar "schema-first" approach for understanding JSON structures before querying full datasets.

---

Topic 2: Best Practices for jq-Based JSON Processing

Key Findings:

1. Schema-First Approach: Providing LLMs/tools with JSON schema maps dramatically improves query accuracy
2. Type Safety: Using jq's type inference capabilities (type function) to replace values with type names
3. Array Reduction: Sampling first element of arrays to represent structure without overwhelming output
4. Command-Line Integration: jq excels in shell pipelines for API response processing

Best Practices:

• Always validate JSON paths with -e flag for robust parsing
• Handle whitespace and newlines carefully when passing output to other commands
• Use jq for filtering before processing large datasets in memory
• Combine with tools like curl for API workflows

Sources:

• XML vs. JSON: Which is Better? | Virtarix Guide
• Parse and Generate JSON with Shell - MojoAuth

---

Topic 3: JSON Schema Inference and Evolution Tools

Industry Solutions:

1. Databricks from_json: Automatic schema inference and evolution for streaming JSON data

   • Detects new fields in nested JSON objects
   • Infers field types and maps to appropriate data types
   • Automatically handles non-conforming data with "rescued data" columns

2. Databricks Autoloader: Samples first 50 GB or 1,000 files for schema inference

   • Configurable via cloudFiles.schemaInference.sampleSize options
   • Supports schema hints for overriding inferred types

3. Oracle JSON to Duality Migrator: Uses JSON schemas for relational schema inference

Source:

• Databricks: Infer and evolve schema using from_json
• Inside Databricks Autoloader: Schema Evolution

Relevance: Demonstrates enterprise-level need for automated schema discovery from JSON data sources.

---

Topic 4: GitHub API Response Structure Analysis

Practical Applications:

1. GitHub CLI + jq Integration:

   • gh CLI provides native --json and --jq flags for filtering
   • Handles authentication and pagination automatically
   • Example: gh issue list --state closed --limit 1000 --json number,closedAt

2. API Response Exploration Pattern:

   # Step 1: Fetch minimal data to understand structure
   gh api search/repositories?q=(redacted)&per_page=1 | jqschema.sh
   
   # Step 2: Review schema to identify available fields
   
   # Step 3: Request full dataset with targeted queries

3. JSON Filter MCP Server: Purpose-built for reducing large JSON responses for LLM context management

Source:

• Command Line Mastery for Strapi Developers
• GitHub Issue Cleanup Workflow

Relevance: Validates the workflow described in the jqschema documentation—use schema discovery with minimal data before requesting full datasets.

---

Topic 5: Alternative Tools and Comparison

Related Tools:

1. yq: jq-like processor for YAML, JSON, XML, and CSV

   • Portable command-line tool with jq-compatible syntax
   • Supports multiple input/output formats

2. JSON Logs MCP Server: Specialized for structured log file analysis with time-range filtering

3. JSONPath vs jq:

   • JSONPath: Simple extraction without transformation (zero dependencies)
   • jq: Full transformation, filtering, and restructuring capabilities

Decision Guide:

• For simple extraction → JSONPath-based tools
• For transformation and complex queries → jq-based tools
• For schema understanding → Schema inference utilities like jqschema

Source: JSON Query MCP Server by Michael Graczyk

---

Recommendations

For jqschema Utility Enhancement

1. Add Schema Hint Support: Allow users to override inferred types for specific fields
2. Nested Structure Control: Add depth limit parameter to control how deep the schema generation goes
3. Format Options: Support both compact (current) and pretty-printed output modes
4. Type Statistics: Include occurrence counts for array types when multiple element types exist

For GitHub Workflow Integration

1. Pre-filter Large Responses: Always use perPage: 1 with schema discovery before requesting full datasets from GitHub search APIs
2. Cache Schemas: Store frequently-used API response schemas to avoid repeated discovery calls
3. Validate Changes: Use schema comparison to detect breaking changes in API responses across workflow runs

For Documentation

1. Add Comparison Table: Show jqschema vs alternatives (yq, jq raw, JSONPath tools)
2. Include Complex Examples: Demonstrate nested object handling, array type variations, and null value scenarios
3. Performance Benchmarks: Document memory and time benefits versus loading full JSON into tools

---

Key Sources

• Stop Drowning in JSON: jq MCP Server Deep Dive
• JSON Query MCP Server Comparison
• Microsoft Azure Databricks: from_json Schema Evolution
• Parse and Generate JSON with Shell - MojoAuth
• GitHub CLI Automation Best Practices
• Command Line JSON Processing

---

Suggested Next Steps

1. Validate jqschema Implementation: Test against GitHub search API responses with nested structures and verify type inference accuracy
2. Document Edge Cases: Add examples showing how the tool handles:
   • Mixed-type arrays (e.g., [1, "string", null])
   • Deeply nested objects (5+ levels)
   • Empty arrays and empty objects
   • Large datasets (100MB+ JSON files)
3. Integration Examples: Create cookbook recipes for common use cases:
   • GitHub API response exploration
   • CI/CD artifact analysis
   • Log file structure discovery
4. Performance Testing: Benchmark against alternatives for large file handling

---

Research conducted on: 2025-11-01
Tools used: Tavily web search (advanced depth)
Search queries: 7 targeted queries covering jq utilities, JSON schema inference, GitHub API analysis

AI generated by Scout for #2938