Token Saving - Linksee Memory

Every token costs money and context window space. read_smart is an MCP tool that caches file content and returns only what changed — saving 50-99% tokens on re-reads.

The problem

A typical agent session reads the same files multiple times:

Read src/index.ts to understand the codebase (900 tokens)
Make changes
Read src/index.ts again to verify (900 tokens)
Read it again after another change (900 tokens)

2,700 tokens for one file, but the content barely changed between reads.

The solution

read_smart maintains a per-file snapshot with content hashes:

Read	Status	Tokens	Savings
1st	`first_read` — full content	900	—
2nd (no change)	`unchanged` — chunk list only	50	94%
3rd (1 function changed)	`modified` — diff only	200	78%
4th (no change)	`unchanged`	50	94%
Total		1,200	vs 3,600 naive

AST-aware chunking

Unlike line-based diffing, read_smart splits files into semantic chunks:

TypeScript / JavaScript

Parsed via @babel/parser into top-level declarations:

chunk: "createPool" (lines 7-25)
chunk: "getConnection" (lines 27-45)
chunk: "closePool" (lines 47-55)

If you add a new function at line 7, only that new chunk is “changed” — getConnection and closePool keep their identity (even though their line numbers shifted).

Python

Split by top-level def and class blocks using indentation analysis.

Markdown

Split by h2 and h3 headings.

Other files

Fixed 100-line windows.

Token estimation

Token count is estimated at 0.3 tokens per character — a blended rate that works for both English and Japanese text.

Cache storage

Snapshots are stored in the file_snapshots table in the same SQLite database as memories. No external services required.

Column	Purpose
`path`	Absolute file path
`mtime_ms`	Last modification time
`sha256`	Full file hash
`chunks_json`	Array of chunk definitions with individual hashes
`total_tokens`	Estimated token count

​The problem

​The solution

​AST-aware chunking

​TypeScript / JavaScript

​Python

​Markdown

​Other files

​Token estimation

​Cache storage