Caliper

Semantic code analysis and transformation tool for Go, inspired by Coccinelle. Caliper runs as an MCP server, exposing AST-level pattern matching, control flow analysis, and code transformation to LLM-powered tools.

Install

Homebrew

brew install trendvidia/tap/caliper

This installs both binaries: caliper (the MCP server) and caliper-analyze.

From source

Caliper ships two binaries — the MCP server, and a single-shot CLI for CI use:

# MCP server, for LLM-driven interactive use
go install github.com/trendvidia/caliper/cmd/caliper-mcp@latest

# Single-shot analyzer, emits SARIF or JSON and exits
go install github.com/trendvidia/caliper/cmd/caliper-analyze@latest

Prebuilt binaries are attached to releases; see Getting Started.

Claude Code MCP Configuration

Register Caliper as an MCP server using the claude mcp add command:

# Available across all your projects
claude mcp add --scope user caliper -- $(brew --prefix)/bin/caliper

# Or for a single project only
claude mcp add caliper -- $(brew --prefix)/bin/caliper

If you used go install, the path is $(go env GOPATH)/bin/caliper-mcp; for a prebuilt binary, use its full path.

After adding, restart Claude Code for the MCP server to be loaded.

For team-wide configuration, add a .mcp.json file to your project root:

{
  "mcpServers": {
    "caliper": {
      "command": "caliper",
      "args": []
    }
  }
}

Tools

Caliper exposes 26 MCP tools across seven categories.

Bug Detection

Tool	Description
find_nil_deref	Nil pointer dereferences via guard tracking (error/ok/nil checks)
find_resource_leak	Missing cleanup on all code paths (files, connections, locks)
find_unchecked_error	Errors assigned but never checked before overwrite
find_error_swallow	Explicitly discarded errors (_ = f(), f, _ := os.Open()) with severity tiers
find_lock_violation	Mutex discipline: missing unlock, double lock, RLock/RUnlock pairs
find_defer_in_loop	Defer inside for/range loops (accumulates until function returns)
find_context_misuse	context.Background() when parent context available (outside deferred cleanups), context.TODO() in production

Concurrency Analysis

Tool	Description
find_race_condition	Data races from goroutine closure captures, with sync.WaitGroup/Once/channel/mutex awareness
find_goroutine_leak	Goroutines with infinite loops but no termination mechanism
find_slice_append_race	Concurrent append to shared slices (silent data corruption)

Security

Tool	Description
find_sql_injection	String concatenation into SQL queries
find_taint_flow	Trace untrusted data (HTTP requests) to sensitive sinks (SQL, exec) across function boundaries

Pattern Matching & Exploration

Tool	Description
find_pattern	Structural AST pattern matching with metavariables ($x.Close(), if $err != nil { ... }), plus inside/not_inside enclosing-context filters (go, defer, callback_of:<pattern>, call:<pattern>)
find_type_usage	All usages of a type: instantiation, argument, return, field, type assertion
find_call_sites	All callers of a function via call graph analysis (CHA/RTA/VTA)
check_path_property	Generic CFG path properties: always-before, always-after, never-between, dominates
check_interface	Verify a type satisfies an interface, report missing methods and signature mismatches
list_equivalences	Show semantic equivalences used in pattern matching

Cross-Method Lifecycle Rules

Tool	Description
check_paired_calls	Type-scoped pairing: a $x.Start() in any method of a type requires a $x.Stop() somewhere in its method set (or a companion type it constructs, e.g. widget→renderer)
find_stale_capture	Fields copied in a create-style method (CreateRenderer) and never re-read in a refresh-style method (Refresh) — stale-snapshot bugs

Code Transformation

Tool	Description
apply_transform	AST-level rewrite: match a pattern, replace with another, preserving metavariable bindings
migrate_api	Multi-rule API migrations with import path updates
add_missing_cleanup	Auto-insert missing defer statements for acquired resources

Pattern Language & Debugging

Tool	Description
run_patterns	Load and execute .gcl rule files against a codebase
explain_match	Debug why a pattern did/didn't match at a specific location
explain_finding	Re-run analysis at a location, show control-flow trace with source annotations

Output Formats

All analysis tools support a format parameter:

Format	Use Case
compact	Default. Short keys, relative paths, minimal tokens for LLM consumption
summary	Severity counts only — triage and overview
grouped	Findings grouped by file — human readability
sarif	SARIF v2.1.0 — GitHub Code Scanning and CI integration

Additional output parameters:

• max_findings — limit output to N findings
• min_severity — filter by info, warning, or error

Consecutive identical findings in the same file are automatically grouped with a count and line range (e.g., "contain.go:106-120:3", count: 15).

Inline Suppression

Suppress specific findings with comments:

_, _ = os.Open("file.txt") //caliper:ignore error_swallow

f.Close() //caliper:ignore  // suppresses all rules on this line

LLM-Effective Usage

Caliper's MCP tools are designed so that an LLM never writes formal logic — each tool encapsulates the formal reasoning (CTL model checking, path quantifiers, CFG traversal) behind an intent-based interface.

What works well via LLM

Capability	How the LLM uses it
Pattern finding	"find all Close() calls" → find_pattern with $x.Close()
Type exploration	"where is *sql.DB used?" → find_type_usage
Interface check	"does MyWidget satisfy fyne.Widget?" → check_interface
Refactoring	"rename OldFunc to NewFunc" → apply_transform
Bug detection	"any resource leaks?" → find_resource_leak
Error hygiene	"find discarded errors" → find_error_swallow
Migration	"upgrade from v1 to v2 API" → migrate_api with rules
Validation	"test this pattern" → explain_match + iterate
Triage findings	"explain this nil deref" → explain_finding
Policy enforcement	"run our team's rules" → run_patterns

What's better left to humans

• Authoring complex .gcl rule files with absent/contains logic
• Writing .gsp semantic patches with multi-rule dependencies
• Configuring CI gates and choosing severity thresholds
• Defining organizational coding policies

LLM as Policy-to-Rules Translator

The most powerful workflow combines both: humans write policy documents, the LLM translates them into enforceable rules.

Given a policy document (e.g., POLICIES.md) and access to the codebase via MCP tools, an LLM can:

1. Read the policy — understand natural language requirements ("all database connections must be closed", "never log PII fields", "context.Context must be first parameter")

2. Explore the codebase — use find_pattern and find_type_usage to learn naming conventions, package structure, and existing patterns

3. Generate .gcl rules — translate each policy into pattern rules:

   # Generated by LLM from POLICIES.md § 3.2
   version = 1
   name = "db-safety"
   patterns = {
     open_db: { pattern = "$db, $err := sql.Open($args...)" }
     close_db: { pattern = "$db.Close()" }
   }
   rules = [{
     name = "unclosed-db-connection"
     severity = "error"
     match = "@open_db"
     absent = "@close_db"
     message = "Database connection opened but never closed"
   }]
   

4. Validate the rules — use run_patterns to test against the codebase, check for false positives, refine patterns

5. Generate CI configuration — produce .caliper.pxf config, GitHub Actions workflow, or other CI gate setup

This workflow means:

• Policy authors write natural language (in a wiki, RFC, or markdown file)
• LLM translates to machine-enforceable rules (one-time or on policy change)
• CI runs the rules on every PR (no LLM in the loop)
• Developers see actionable findings with clear messages tied back to policy

Example: Full Policy Enforcement Pipeline

POLICIES.md (human-authored)
    ↓
LLM reads policies + explores codebase (via MCP tools)
    ↓
patterns/*.gcl (LLM-generated, human-reviewed)
    ↓
.caliper.pxf (points to pattern files)
    ↓
CI: caliper check --diff (runs on every PR)
    ↓
SARIF → GitHub Code Scanning (findings in PR review)

The LLM acts as a bridge between human intent and machine enforcement. It only needs to run once (or when policies change) — the resulting .gcl files are deterministic and run without an LLM.

Configuration

Caliper uses PXF format (protowire) for configuration. Create .caliper.pxf in your project root:

severity = {
  resource_leak: "error"
  unchecked_error: "warning"
}
rules {
  enable = ["resource_leak", "unchecked_error"]
}
engine {
  skip_generated = true
  build_tags = ["integration"]
}
patterns = ["patterns/*.gcl"]

# Extend find_unchecked_error's allowlist with project-local APIs whose
# error returns are known not to occur in practice. Entries support `*`
# globs. See docs/getting-started.md for the full reference.
infallible {
  methods = ["github.com/myorg/mylib.Logger.*"]
  writers = ["github.com/myorg/mylib.NopWriter"]
}

License

MIT