Provides structural source code intelligence for TypeScript/TSX projects, enabling agents to list files, inspect files, find symbols and references, rename symbols, and run type checks.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Programmatic Rename UserCard to ProfileCard"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A structural programming interface for source code, designed for AI coding agents.
Today, agents edit code as text: search, read files, make edits, re-check, and hope they didn't miss a reference.
Programmatic gives agents structural code intelligence, turning operations like "rename this symbol" into semantic operations instead of string manipulation.
Status: Phase 1. Programmatic currently provides a TypeScript/TSX structural engine and an MCP server for AI coding agents.
import { openProject } from "@programmatic/typescript";
const project = openProject("./example-project");
project.inspectFile("src/UserCard.tsx");
project.findSymbol("UserCard");
project.findReferences("UserCard");
project.renameSymbol("UserCard", "ProfileCard");
project.check();
project.save();list_files
inspect_file
find_symbol
find_symbols
find_references
rename_symbol
checkAn agent can use these directly instead of reconstructing code structure through grep, file reads, and manual edits.
Related MCP server: TypeScript LSP MCP
Traditional
Read → Search → Inspect → Edit → Re-read → Fix → Type-check
Programmatic
Agent → Structural operation → TypeScript Language ServiceProgrammatic uses the TypeScript Language Service for semantic resolution and reference finding, then applies minimal text edits to preserve the original source.
Renames understand scopes, imports, exports, JSX, and references while leaving comments, strings, and unrelated identifiers untouched.
Initial evaluation against text-based Claude Code workflows on Zod v3.23.8:
Metric | Programmatic | Raw text |
Pass rate | 100% | 88% |
Avg. tokens | 133k | 560k |
Avg. calls | 4.4 | 11 |
Avg. time | 14s | 79s |
Avg. cost | $0.096 | $0.280 |
For these rename tasks, the raw workflow used approximately 4.2× more tokens, 2.5× more tool calls, 5.6× more time, and 2.9× more cost.
These are initial results from a single repository and model.
See docs/mcp-hands-on-eval.md for the methodology and docs/battle-test-report.md for engine testing.
npm install
npm run build
npm testAI coding agent
↓
Programmatic MCP
↓
Programmatic API
↓
TypeScript Language Service
↓
TypeScript / TSXSee docs/api.md and docs/architecture-decision.md.
MIT
This server cannot be deployed
Code intelligence for coding agents: semantic, AST, graph, and full-text search. 279+ languages.
Coding agents in multi-service codebases routinely rebuild existing helpers, trust stale type definitions, and modify API contracts without knowing who consumes them. Carrick solves this by indexing your entire TypeScript ecosystem across service and repository boundaries. By integrating deeply with the TypeScript compiler, Carrick traces every route, type, and cross-service call while recording function behaviour so agents search by intent rather than name. Delivered via MCP for AI agents and LSP for IDEs, Carrick ensures models see existing endpoints and utilities before generating new code. The scanner is source-available and runs from your CLI or CI pipeline.
Stateless TS/JS compiler facts for agents: references, imports, impact. No repo index or OAuth.
Codebase graphs, caller impact analysis, and recorded project context for AI coding agents.
