How does sctx compare?

The short version: sctx provides file-targeted, action-filtered context injection. Instead of loading all instructions all the time, it delivers only the entries that match what the agent is doing right now.

Tool	Scope	Format	Delivery
AGENTS.md	Directory	Unstructured prose	Always loaded
MCP	External tools & data	RPC protocol	On demand via server
.cursorrules	Project root	Monolithic prompt	Always loaded
sctx	Per-file, per-action	Declarative YAML	JIT, glob-matched

AGENTS.md

AGENTS.md is becoming the standard project-level manifest for AI coding agents, providing a dedicated place for build commands and coding conventions.

The distinction: AGENTS.md is directory-scoped and largely unstructured prose. Developers must write natural language conditional logic ("If you are editing a SQL file, do X"). As the file grows, agents struggle to parse it — attention dilutes and the model quietly ignores the instruction that mattered most.

Structured Context improves on this with declarative YAML glob-matching (**/*.sql), ensuring the LLM only ever reads the context applicable to the file it is actively touching.

Model Context Protocol (MCP)

Anthropic's MCP is an open-source client-server protocol that standardizes how AI systems integrate with external tools and data sources.

The distinction: MCP is an active RPC protocol (like a USB-C cable for AI tools), whereas sctx is a static declarative file format. MCP connects the agent to the environment, while sctx dictates the rules of engagement for the codebase. They're complementary — an MCP server could be built to dynamically serve sctx contexts to an agent.

IDE-specific rules (.cursorrules / .windsurfrules)

Project-root or directory-level markdown files where developers drop system prompts and stylistic preferences for AI IDEs like Cursor or Windsurf.

The distinction: These rules are generally monolithic. If a .cursorrules file contains React, Python, and SQL guidelines, the AI is burdened with all of them simultaneously during any edit. sctx's action-filtering (on: read vs on: edit vs on: create) and precise file-path scoping offer a level of granularity that these files lack.

Example: monolithic rules vs. targeted context

A typical .cursorrules file loads everything at once:

# .cursorrules

When editing Python files, use snake_case for all identifiers.
When editing SQL models, use the incremental strategy macro.
When editing React components, prefer named exports.
When creating any file, add a license header.

The agent sees all four instructions regardless of which file it touches. With Structured Context, each instruction only appears when it's relevant:

# AGENTS.yaml

context:
  - content: "Use snake_case for all identifiers"
    match: ["**/*.py"]
    on: edit

  - content: "Use the incremental strategy macro"
    match: ["models/**/*.sql"]
    on: edit

  - content: "Prefer named exports"
    match: ["src/components/**/*.tsx"]
    on: edit

  - content: "Add a license header"
    on: create

When the agent edits models/revenue.sql, it sees one instruction instead of four. At scale — dozens of conventions across a monorepo — the difference in signal-to-noise ratio is significant.