Pincher FAQs

Question 1

How does Pincher handle codebase changes?

Accepted Answer

Pincher features incremental re-indexing, automatic file change detection, and transparent resolution of stale symbol IDs. This means your codebase intelligence stays up-to-date in real-time without requiring manual re-indexing during development sessions.

Question 2

What types of queries can I perform with Pincher?

Accepted Answer

Pincher supports powerful query capabilities including Cypher-like graph queries for understanding code relationships, and FTS5 BM25 full-text search across names, signatures, and docstrings. It also offers fast byte-offset symbol retrieval for exact code excerpts.

Question 3

What is Pincher and what problem does it solve?

Accepted Answer

Pincher is a codebase intelligence server designed for LLM agents. It indexes your projects into three co-located layers (symbol store, knowledge graph, full-text search) to enable highly efficient, precise, and cost-effective querying by AI models, overcoming the limitations of large context windows.

Question 4

How does Pincher improve LLM agent performance and cost-efficiency?

Accepted Answer

Pincher provides 15 specialized tools and an HTTP REST API for LLMs to query code efficiently. Every response includes real-time token usage, savings, latency, and cost-avoided metrics, allowing agents to fetch only necessary code snippets, significantly reducing token consumption and processing time.

Question 5

Does Pincher require cloud dependencies or a specific LLM?

Accepted Answer

No. Pincher is a single Go binary with no cloud dependencies, offering complete privacy and local operation. It's designed to work with 'any LLM' via MCP stdio or HTTP REST, making it highly flexible for various AI development setups.

Pincher

Pincher

Key Features

Use Cases

Key Features

Use Cases