Modern AI coding agents face a major performance bottleneck: context window bloat. When an agent explores a repository to answer questions or write code, it routinely reads full files containing thousands of lines of code. This behavior wastes tokens, degrades model attention, and increases latency.
The Mechanics of Static AST Parsing
CostAffective-MCP resolves this by applying compiler-level static analysis directly within your workspace. Using tree-sitter, the server builds a concrete syntax tree (CST) for every source file. Instead of looking at code as unstructured text, CostAffective analyzes its grammar.
When a coding agent queries a symbol definition, the server maps the query directly to AST coordinates (StartLine, EndLine) and extracts only the relevant code range:
// Instead of sending a 1000-line database.go file
// CostAffective sends only the exact structural scope:
func (m *Manager) GetSession(id string) (*Session, error) {
m.mu.RLock()
defer m.mu.RUnlock()
session, ok := m.sessions[id]
if !ok {
return nil, ErrSessionNotFound
}
return session, nil
}AST Scope Invalidation & Watchdogs
Rather than rebuilding the entire index every time you save, CostAffective runs an internal watcher. This watchdog monitors file descriptors for modifications and triggers AST scans only for the affected scopes. This maintains index consistency in under 8ms, ensuring the agent always works with current, accurate codebase structures.
Key Architectural Gains
- Grammar-Aware Filtering: Separates functions, methods, struct interfaces, and variables.
- Reduced Context Payload: Drops unrelated comments, imports, and utility functions, compressing inputs by up to 82%.
- Deterministic Lookups: Ensures agents find definition ranges instantly, bypassing fuzzy text-matching issues.