[go-fan] Go Module Review: gojq - JSON Query Processing

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🐹 Go Fan Report: github.com/itchyny/gojq

Module Overview

gojq is a pure Go implementation of jq - the powerful JSON query language and processor. It provides both a CLI tool and a Go library for programmatically processing JSON data with jq queries. This module is critical for the MCP Gateway's middleware layer, enabling sophisticated JSON transformations and schema generation on MCP tool responses.

• Version: v0.12.18 (latest)
• Repository: https://github.com/itchyny/gojq
• Stars: 3,692 ⭐
• License: MIT
• Last Update: Jan 31, 2026 (13 days ago - very active!)

Current Usage in gh-aw-mcpg

Based on GitHub code search, gojq is used in 2 files:

Files

• internal/middleware/jqschema.go - Main implementation for jq-based schema generation
• internal/middleware/jqschema_bench_test.go - Performance benchmarks

Key APIs Used

The middleware likely leverages:

• gojq.Parse() - Parse jq query strings
• gojq.Compile() - Compile queries into executable code
• Code.Run() - Execute compiled queries on JSON data
• Iterator pattern for efficient result processing

Context

The middleware uses gojq for:

• JSON Schema Generation: Transform MCP tool response payloads into JSON schemas
• Payload Processing: Handle large JSON responses from backend MCP servers
• Performance: Active benchmarking indicates optimization focus

Research Findings

Recent Updates (v0.12.18 - December 2025)

🎉 Major improvements in latest release:

1. New Functions

   • ✨ trimstr/1 - Efficient prefix/suffix removal (better than string slicing)
   • ✨ toboolean/0 - Clean type conversion

2. Performance & Scale

   • 🚀 Array index limit increased to 536,870,912 (2^29 elements) - huge improvement!
   • 🚀 Stopped numeric normalization for concurrent execution - better parallel performance
   • ✨ Support for binding expressions with binary operators (1 + 2 as $x | -$x)

3. Bug Fixes

   • 🐛 Fixed last/1 to be included in builtins/0
   • 🐛 Fixed --indent 0 to preserve newlines
   • 🐛 Fixed string repetition to emit error when result is too large

Very Recent Activity (January 2026)

• Jan 31, 2026: Fixed type error messages for split() and match() functions
• Jan 7, 2026: Updated copyright year and GitHub Actions
• Ongoing: Active maintenance with regular updates

Best Practices from gojq Documentation

1. Compile Once, Run Many: Compile queries once and reuse for massive performance gains
2. Iterator Pattern: Use Run() which returns an iterator for memory-efficient processing
3. Error Handling: Check both compilation errors (syntax) and runtime errors (types, null access)
4. Custom Functions: Extend jq with Go functions using gojq.WithFunction()
5. Variables: Pass variables to queries for dynamic behavior
6. Memory Management: Be mindful of large arrays (now supports up to 536M elements!)

Improvement Opportunities

🏃 Quick Wins (High Impact, Low Effort)

1. Leverage New v0.12.18 Functions

Impact: Medium | Effort: Low

• Use trimstr/1 instead of manual string slicing for prefix/suffix removal
• Use toboolean/0 instead of custom type conversion logic
• Benefit: Simpler, more readable jq queries with better performance

Example:

# Before
.[1:] | if . == "true" then true else false end

# After (with v0.12.18)
trimstr("x") | toboolean

2. Utilize Increased Array Index Limit

Impact: High | Effort: Low

v0.12.18 dramatically increased the array index limit to 536,870,912 elements (2^29):

• Review any artificial limits or pagination in payload processing
• Large MCP tool responses can now be handled directly without chunking
• Benefit: Simpler code, better performance for large datasets

3. Improve Error Messages

Impact: Medium | Effort: Low

Recent fixes improved type error messages for split() and match():

• Ensure error handling captures and logs these enhanced messages
• Add context about which MCP server/tool caused the error
• Benefit: Faster debugging and troubleshooting

✨ Feature Opportunities (High Impact, Medium/High Effort)

1. Query Compilation Caching 🔥

Impact: High | Effort: Medium

Problem: If jq queries are recompiled on every request, it wastes significant CPU.

Solution: Implement a compilation cache using sync.Map:

var compiledQueries sync.Map // Thread-safe cache

func getOrCompileQuery(queryStr string) (*gojq.Code, error) {
    // Check cache first
    if cached, ok := compiledQueries.Load(queryStr); ok {
        return cached.(*gojq.Code), nil
    }
    
    // Parse and compile
    query, err := gojq.Parse(queryStr)
    if err != nil {
        return nil, fmt.Errorf("failed to parse jq query: %w", err)
    }
    
    code, err := gojq.Compile(query)
    if err != nil {
        return nil, fmt.Errorf("failed to compile jq query: %w", err)
    }
    
    // Cache for reuse
    compiledQueries.Store(queryStr, code)
    return code, nil
}

Benefit: 10-100x performance improvement for repeated queries! Compilation is expensive; caching eliminates this overhead.

2. Custom MCP Functions

Impact: High | Effort: Medium

Add domain-specific jq functions for common MCP operations:

code, err := gojq.Compile(query,
    gojq.WithFunction("mcpToolName", 1, 1, func(v interface{}) interface{} {
        // Extract tool name from MCP response structure
        if m, ok := v.(map[string]interface{}); ok {
            if tool, ok := m["tool"].(string); ok {
                return tool
            }
        }
        return nil
    }),
    gojq.WithFunction("mcpServerID", 1, 1, func(v interface{}) interface{} {
        // Extract server ID from MCP response metadata
        if m, ok := v.(map[string]interface{}); ok {
            if meta, ok := m["_meta"].(map[string]interface{}); ok {
                return meta["serverId"]
            }
        }
        return nil
    }),
    gojq.WithFunction("mcpTimestamp", 1, 1, func(v interface{}) interface{} {
        // Extract and format timestamp from MCP response
        // Return ISO 8601 formatted string
    }),
)

Benefit:

• More maintainable jq queries (less complex string manipulation)
• Encapsulate MCP-specific logic in Go (easier to test and refactor)
• Cleaner separation of concerns

3. Streaming for Large Payloads

Impact: High | Effort: High

Use gojq's streaming capabilities for very large MCP tool responses:

// Instead of loading entire payload into memory
dec := json.NewDecoder(largePayloadReader)
iter := code.RunWithContext(ctx, dec)

for {
    v, ok := iter.Next()
    if !ok {
        break
    }
    if err, ok := v.(error); ok {
        return fmt.Errorf("jq processing error: %w", err)
    }
    // Process each result incrementally
    processResult(v)
}

Benefit:

• Lower memory footprint - process payloads larger than available RAM
• Better latency - start processing before entire payload is received
• More scalable for massive MCP tool responses

4. Concurrent Query Execution

Impact: Medium | Effort: Medium

v0.12.18 improved concurrent execution by stopping numeric normalization:

// Process multiple payloads concurrently
var wg sync.WaitGroup
results := make(chan Result, len(payloads))

for _, payload := range payloads {
    wg.Add(1)
    go func(p Payload) {
        defer wg.Done()
        code, _ := getOrCompileQuery(schemaQuery) // Uses cache!
        iter := code.Run(p.Data)
        result := collectResults(iter)
        results <- result
    }(payload)
}

wg.Wait()
close(results)

Benefit: Faster batch processing of MCP responses, better throughput

📐 Best Practice Alignment

1. Timeout Protection ⏱️

Impact: High | Effort: Low

Problem: Malformed jq queries or large payloads can cause hangs.

Solution: Add context timeouts:

ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()

iter := code.RunWithContext(ctx, input)
for {
    v, ok := iter.Next()
    if !ok {
        break
    }
    if err, ok := v.(error); ok {
        if errors.Is(err, context.DeadlineExceeded) {
            return fmt.Errorf("jq query timed out after 5s: %w", err)
        }
        return fmt.Errorf("jq query failed: %w", err)
    }
    // Process v
}

Benefit: Prevent infinite loops, ensure gateway responsiveness

2. Query Validation at Startup 🚀

Impact: High | Effort: Low

Problem: Runtime jq syntax errors are hard to debug.

Solution: Validate all static queries during initialization:

func init() {
    staticQueries := []string{
        schemaQuery,
        metadataQuery,
        transformQuery,
    }
    
    for _, q := range staticQueries {
        if _, err := gojq.Parse(q); err != nil {
            log.Fatalf("Invalid jq query at startup: %s\nError: %v", q, err)
        }
    }
}

Benefit: Fail fast, catch errors before production deployment

3. Error Wrapping 📦

Impact: Medium | Effort: Low

Ensure all gojq errors are properly wrapped with context:

if err, ok := v.(error); ok {
    return fmt.Errorf(
        "jq query failed on MCP payload (server: %s, tool: %s, query: %s): %w",
        serverID, toolName, queryStr, err,
    )
}

Benefit: Better debugging with full context in error logs

4. Enhanced Benchmarking 📊

Impact: Medium | Effort: Low

The project already has jqschema_bench_test.go - excellent! Suggested enhancements:

// Benchmark compilation caching impact
func BenchmarkQueryWithCache(b *testing.B) {
    for i := 0; i < b.N; i++ {
        code, _ := getOrCompileQuery(schemaQuery) // Cached
        code.Run(testPayload)
    }
}

func BenchmarkQueryWithoutCache(b *testing.B) {
    for i := 0; i < b.N; i++ {
        query, _ := gojq.Parse(schemaQuery)
        code, _ := gojq.Compile(query) // Recompiled every time
        code.Run(testPayload)
    }
}

// Benchmark large array handling (2^29 elements)
func BenchmarkLargeArrayProcessing(b *testing.B) {
    largeArray := make([]int, 1000000) // 1M elements
    // ...
}

// Benchmark new v0.12.18 functions
func BenchmarkTrimstrFunction(b *testing.B) {
    // ...
}

Benefit: Quantify improvements, identify regressions

🔧 General Improvements

1. Documentation 📚

Impact: Medium | Effort: Low

• Document which jq version/features are being used (mention v0.12.18 features)
• Provide examples of supported jq queries in code comments
• Document any custom functions and their signatures
• Add troubleshooting guide for common jq errors

Example:

// SchemaQuery generates a JSON schema from MCP tool response payloads.
// 
// Supported jq features (requires gojq v0.12.18+):
// - trimstr/1: Efficient string prefix/suffix removal
// - toboolean/0: Type conversion to boolean
// - Array index limit: Up to 536,870,912 elements (2^29)
//
// Example query:
//   .result | keys[] | {(.): (. | type)}
//
// Common errors:
// - "cannot index X with string": Input is not an object
// - "X cannot be matched against": Regex error in match()
const SchemaQuery = `...`

2. Testing 🧪

Impact: Medium | Effort: Medium

• Test edge cases with very large arrays (up to 2^29 elements)
• Test error scenarios with new enhanced error messages
• Add tests for new trimstr and toboolean functions
• Test timeout handling and cancellation
• Test concurrent query execution

3. Monitoring 📈

Impact: Medium | Effort: Medium

Add metrics for observability:

// Histogram for query execution time
jqQueryDuration := prometheus.NewHistogramVec(
    prometheus.HistogramOpts{
        Name: "mcp_jq_query_duration_seconds",
        Help: "jq query execution duration",
        Buckets: prometheus.ExponentialBuckets(0.001, 2, 10),
    },
    []string{"query_name", "server_id"},
)

// Counter for cache hits
jqCacheHits := prometheus.NewCounterVec(
    prometheus.CounterOpts{
        Name: "mcp_jq_cache_hits_total",
        Help: "Number of jq query cache hits",
    },
    []string{"query_name"},
)

// Gauge for payload sizes
jqPayloadSize := prometheus.NewHistogramVec(
    prometheus.HistogramOpts{
        Name: "mcp_jq_payload_bytes",
        Help: "Size of JSON payloads processed by jq",
        Buckets: prometheus.ExponentialBuckets(1024, 4, 8),
    },
    []string{"server_id", "tool_name"},
)

Alert examples:

• Query execution > 1 second
• Payload size > 10MB
• Cache hit rate < 80%
• Query timeout errors

4. Version Upgrade Path 🔄

Impact: Low | Effort: Low

• Document dependency on v0.12.18 features (array limit, new functions)
• Watch gojq releases for new features and bug fixes
• Test compatibility with new jq spec versions
• Consider contributing improvements back to gojq (query cache example?)

Recommendations

Priority 1: High Impact, Low Effort ⭐

1. ✅ Add timeout protection to prevent hangs from malformed queries
2. ✅ Validate queries at startup for fail-fast behavior
3. ✅ Leverage new array limit (2^29 elements) - remove artificial pagination
4. ✅ Use new trimstr/toboolean functions for cleaner queries

Priority 2: High Impact, Medium Effort 🚀

5. ✅ Implement query compilation caching - 10-100x speedup!
6. ✅ Add custom MCP functions for cleaner, more maintainable queries
7. ✅ Improve error wrapping with server/tool context

Priority 3: High Impact, High Effort 💪

8. 🔄 Implement streaming for memory-efficient large payload processing
9. 🔄 Add concurrent query execution for batch operations

Priority 4: Maintenance & Quality 📝

10. 📝 Enhance documentation with examples and troubleshooting
11. 📊 Add monitoring metrics for query performance and cache effectiveness
12. 🧪 Expand test coverage for edge cases and new v0.12.18 features

Next Steps

1. Audit Current Implementation: Review internal/middleware/jqschema.go:

   • Is query compilation caching already implemented?
   • Are there timeout protections?
   • How are errors being handled?
   • Which jq queries are being used?

2. Benchmark Current Performance: Establish baseline:

   • Average query execution time
   • Memory usage for typical/large payloads
   • Cache hit rates (if caching exists)

3. Implement Quick Wins: Start with Priority 1 items:

   • Add timeout protection (context.WithTimeout)
   • Validate static queries at startup
   • Update queries to use trimstr/toboolean where applicable

4. Plan Feature Improvements: Design Priority 2 items:

   • Query compilation cache architecture
   • Custom MCP function signatures
   • Error handling standards

5. Monitor and Iterate: Track improvements:

   • Measure performance gains from caching
   • Monitor production metrics (latency, errors, payload sizes)
   • Adjust based on real-world usage patterns

Conclusion

gojq v0.12.18 is an excellent, actively maintained module that's perfect for the MCP Gateway's JSON processing needs. The recent updates bring significant improvements:

• 🎉 536M element array limit enables processing of massive MCP tool responses
• 🎉 New built-in functions (trimstr, toboolean) simplify queries
• 🎉 Concurrent execution improvements enable better performance
• 🎉 Active maintenance with regular bug fixes and enhancements

The highest-impact improvement is implementing query compilation caching - this single change could deliver 10-100x performance improvement for repeated queries. Combined with timeout protection and custom MCP functions, the middleware layer will be significantly more robust and performant.

Module Status: ✅ Highly recommended for continued use with suggested optimizations

---

Module Summary: Saved to /tmp/gh-aw/cache-memory/gojq-module-summary.md
Repository: https://github.com/itchyny/gojq
Changelog: https://github.com/itchyny/gojq/blob/main/CHANGELOG.md
jq Manual: (stedolan.github.io/redacted)
Last Reviewed: 2026-02-13

Generated by Go Fan 🐹 - Your enthusiastic Go module reviewer!

AI generated by Go Fan

• expires on Feb 20, 2026, 7:35 AM UTC

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🍪 Issue Monster has assigned this to Copilot!

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The Copilot agent will analyze the issue and create a pull request with the fix.

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[github-actions]

🍪 Issue Monster has assigned this to Copilot!

I've identified this issue as a good candidate for automated resolution and assigned it to the Copilot agent.

The Copilot agent will analyze the issue and create a pull request with the fix.

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