mcp-coco: Skill for Claude Code

CocoIndex

CocoIndex is a Python library for building incremental data processing pipelines with declarative target states. Think spreadsheets or React for data pipelines: declare what the output should look like based on current input, and CocoIndex automatically handles incremental updates, change detection, and syncing to external systems.

Overview

CocoIndex enables building data pipelines that:

• Automatically handle incremental updates: Only reprocess changed data
• Use declarative target states: Declare what should exist, not how to update
• Support any Python types: No custom DSL -- use dataclasses, Pydantic, NamedTuple
• Provide function memoization: Skip expensive operations when inputs/code unchanged
• Sync to multiple targets: PostgreSQL, SQLite, LanceDB, Qdrant, SurrealDB, Apache Doris, file systems, Kafka

Key principle: TargetState = Transform(SourceState)

When to Use This Skill

Use this skill when building pipelines that involve:

• Document processing: PDF/Markdown conversion, text extraction, chunking
• Vector embeddings: Embedding documents/code for semantic search
• Database transformations: ETL from source DB to target DB
• Knowledge graphs: Extract entities and relationships from data
• LLM-based extraction: Structured data extraction using LLMs
• File-based pipelines: Transform files from one format to another
• Incremental indexing: Keep search indexes up-to-date with source changes
• Streaming pipelines: Kafka-based real-time data processing

Quick Start: Creating a New Project

Initialize Project

cocoindex init my-project
cd my-project

This creates: main.py, pyproject.toml, .env, README.md.

Add Dependencies

# For vector embeddings with PostgreSQL
dependencies = ["cocoindex>=1.0.0", "sentence-transformers", "asyncpg"]

# For LLM extraction
dependencies = ["cocoindex>=1.0.0", "litellm", "instructor", "pydantic>=2.0"]

See references/setup_project.md for complete examples.