semantic-cache-mcp 0.5.0

Lightweight MCP server for semantic file caching with 80%+ token reduction

Semantic Cache MCP

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Cut your MCP client's token usage by about 98% on cached reads, and get answers back in milliseconds.

Semantic Cache MCP is a Model Context Protocol server that puts every file operation behind one cache. The first read of a file seeds the cache and returns a content hash. After that, an unchanged file comes back as a short unchanged reply instead of the whole file, a changed file comes back as a unified diff, and a file that is too large is summarized down to its structure. Search and grep run over the same cached files, so the agent searches what it already read instead of going back to disk. Thirteen tools (read, read_image, batch_read, write, edit, edit_preview, batch_edit, search, grep, glob, delete, clear, stats) share that one cache-aware layer.

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Why this exists

1. Reads stop costing tokens. The first read seeds the cache and hands back a content_hash. Send that hash back on the next read (as known_hash) and the server replies unchanged without resending the file. A modified file returns a unified diff with the changed line numbers. A file larger than the budget collapses to a structure-preserving summary instead of a blind cut at a byte offset.

2. Search and grep run on the cache, not the disk. Keyword search (BM25), glob, and grep all read from the same indexed corpus that read/batch_read populate. An in-session result LRU collapses repeated queries to sub-millisecond hits.

3. Mutations are bounded by default. write, edit, and batch_edit enforce size and match limits, support dry_run, can run formatters, and refresh the cache atomically.

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Installation

Add to Claude Code settings (~/.claude.json):

Option 1: uvx (always runs the latest version):

{
  "mcpServers": {
    "semantic-cache": {
      "command": "uvx",
      "args": ["semantic-cache-mcp"]
    }
  }
}

Option 2: uv tool install:

uv tool install semantic-cache-mcp

{
  "mcpServers": {
    "semantic-cache": {
      "command": "semantic-cache-mcp"
    }
  }
}

Restart Claude Code.

Block Native File Tools (Recommended)

Disable the client's built-in file tools so all file I/O routes through semantic-cache.

Claude Code: add to ~/.claude/settings.json:

{
  "permissions": {
    "deny": ["Read", "Edit", "Write"]
  }
}

OpenCode: add to ~/.config/opencode/opencode.json:

{
  "$schema": "https://opencode.ai/config.json",
  "permission": {
    "read": "deny",
    "edit": "deny",
    "write": "deny"
  }
}

CLAUDE.md Configuration

Add to ~/.claude/CLAUDE.md to enforce semantic-cache globally:

## Tools

- MUST use `semantic-cache-mcp` instead of native I/O tools (98% token savings on cached reads)

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Tools

Core

Tool	Description
read	Single-file cache-aware read. Returns full content plus a content_hash on the first read, a short unchanged reply when you pass back a matching known_hash, and a diff when the file changed. Supports offset/limit for targeted line recovery.
read_image	Pass-through for image files. Returns an MCP image content block (base64 + mime) so vision models can see the pixels; sidecar metadata holds size and mime. Format verified by magic bytes (PNG, JPEG, GIF, TIFF, BMP, WebP), not by extension. Bypasses the semantic cache. Capped at 5 MiB (SCMCP_MAX_IMAGE_BYTES).
delete	Single-path delete for one file or symlink, with cache eviction and dry_run=true. Intentionally does not support globs, recursive delete, or real-directory delete.
write	Full-file create or replace with cache refresh. Returns creation status or an overwrite diff, supports append=true, and can run formatters.
edit	Single-file exact edit using cached content. Supports scoped and line-range replacement plus dry_run=true. For multiple edits to the same file, prefer batch_edit.
batch_edit	Multiple exact edits in one file with partial success reporting. Preferred over repeated edit calls on the same file: single response, atomic, faster on large files.
edit_preview	Read-only probe that returns match count, line numbers, and small context snippets for a candidate old_string. Use before a costly edit to confirm anchor uniqueness.

Discovery

Tool	Description
search	Cache-only BM25 keyword search that ranks cached files by relevance to a query. Seed likely files first with batch_read.
glob	File discovery plus cache coverage. Use it to find candidates, then pass those paths into batch_read.
batch_read	Multi-file cache-aware read for seeding and retrieval. Handles globs, priorities, token budgets, unchanged suppression, and diff/full routing.
grep	Cache-only exact search with regex or literal matching, line numbers, and optional context. Best for symbols and exact strings.

Management

Tool	Description
stats	Cache metrics, session usage (tokens saved, tool calls), and lifetime aggregates.
clear	Reset all cache entries.

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Tool Reference

The table above is the authoritative tool map. This section only shows the common call shapes.

read: single file, automatic caching

read path="/src/app.py"                        # automatic: full, unchanged, or diff
read path="/src/app.py" offset=120 limit=80    # lines 120 to 199 only

Three states, picked for you:

State	Response	Token cost
First read	Full content plus a content_hash	Normal
Unchanged	unchanged: true, returned when you pass back a matching known_hash	A few tokens
Modified	Unified diff only	5 to 20% of original

write: create or overwrite files

write path="/src/new.py" content="..."
write path="/src/new.py" content="..." auto_format=true
write path="/src/large.py" content="...chunk1..." append=false   # first chunk
write path="/src/large.py" content="...chunk2..." append=true    # subsequent chunks

edit: find/replace with three modes

# Mode A: find/replace, searches the entire file
edit path="/src/app.py" old_string="def foo():" new_string="def foo(x: int):"
edit path="/src/app.py" old_string="..." new_string="..." replace_all=true auto_format=true

# Mode B: scoped find/replace, searches only within the line range (a shorter old_string works)
edit path="/src/app.py" old_string="pass" new_string="return x" start_line=42 end_line=42

# Mode C: line replace, swaps the whole range with no old_string needed (most token savings)
edit path="/src/app.py" new_string="    return result\n" start_line=80 end_line=83

Mode selection:

Mode	Parameters	Best for
Find/replace	old_string + new_string	Unique strings, no line numbers known
Scoped	old_string + new_string + start_line/end_line	Shorter context when read gave you line numbers
Line replace	new_string + start_line/end_line (no old_string)	Maximum token savings when line numbers are known

batch_edit: multiple edits in one call

# Mode A: find/replace, [old, new]
batch_edit path="/src/app.py" edits='[["old1","new1"],["old2","new2"]]'

# Mode B: scoped, [old, new, start_line, end_line]
batch_edit path="/src/app.py" edits='[["pass","return x",42,42]]'

# Mode C: line replace, [null, new, start_line, end_line]
batch_edit path="/src/app.py" edits='[[null,"    return result\n",80,83]]'

# Mixed modes in one call (object syntax also supported)
batch_edit path="/src/app.py" edits='[
  ["old1", "new1"],
  {"old": "pass", "new": "return x", "start_line": 42, "end_line": 42},
  {"old": null, "new": "    return result\n", "start_line": 80, "end_line": 83}
]' auto_format=true

batch_read: multiple files with a token budget

batch_read paths="/src/a.py,/src/b.py" max_total_tokens=50000
batch_read paths='["/src/a.py","/src/b.py"]' priority="/src/main.py"
batch_read paths="/src/*.py" max_total_tokens=30000

• Expands simple globs, honors priority, enforces max_total_tokens, and reports skipped paths with recovery hints.
• Unchanged files are collapsed into the summary instead of repeating content.

discovery: search, glob, grep

search query="authentication middleware logic" k=5
glob pattern="**/*.py" directory="./src" cached_only=true
grep pattern="class Cache" path="src/**/*.py"

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Configuration

Environment Variables

Variable	Default	Description
LOG_LEVEL	INFO	Logging verbosity (DEBUG, INFO, WARNING, ERROR)
TOOL_OUTPUT_MODE	compact	Response detail (compact, normal, debug)
TOOL_MAX_RESPONSE_TOKENS	0	Global response token cap (0 = disabled)
TOOL_TIMEOUT	30	Seconds before tool call times out (auto-resets executor)
MAX_CONTENT_SIZE	100000	Max bytes returned by read operations
MAX_CACHE_ENTRIES	10000	Max cache entries before W-TinyLFU eviction
SEMANTIC_CACHE_DIR	(platform)	Override cache/database directory path

See docs/env_variables.md for detailed descriptions, model selection guidance, and examples.

Safety Limits

Limit	Value	Protects Against
MAX_WRITE_SIZE	10 MB	Memory exhaustion via large writes
MAX_EDIT_SIZE	10 MB	Memory exhaustion via large file edits
MAX_MATCHES	10,000	CPU exhaustion via unbounded replace_all

MCP Server Config

{
  "mcpServers": {
    "semantic-cache": {
      "command": "uvx",
      "args": ["semantic-cache-mcp"],
      "env": {
        "LOG_LEVEL": "INFO",
        "TOOL_OUTPUT_MODE": "compact",
        "MAX_CONTENT_SIZE": "100000"
      }
    }
  }
}

Cache location: ~/.cache/semantic-cache-mcp/ (Linux), ~/Library/Caches/semantic-cache-mcp/ (macOS), %LOCALAPPDATA%\semantic-cache-mcp\ (Windows). Override with SEMANTIC_CACHE_DIR.

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How It Works

┌──────────┐     ┌────────────┐     ┌──────────────────────────┐
│  Claude  │────▶│ smart_read │────▶│ stat() + cache lookup    │
│   Code   │     │            │     │ (BEFORE any disk read)   │
└──────────┘     └────────────┘     └──────────────────────────┘
                        │
       ┌────────────────┼─────────────────┬──────────────────┐
       ▼                ▼                 ▼                  ▼
 ┌──────────┐    ┌──────────┐      ┌──────────┐      ┌────────────┐
 │ mtime    │    │ mtime    │      │ Changed  │      │ New /      │
 │ match    │    │ drift,   │      │ content  │      │ Large      │
 │ FAST     │    │ hash     │      │ → diff   │      │ → summary  │
 │ PATH     │    │ match    │      │ (80-95%) │      │  or full   │
 │ ~5 tok   │    │ ~5 tok   │      └──────────┘      └────────────┘
 │ (99%)    │    │ (99%)    │
 │ ~1 ms    │    │ ~1 ms    │
 │ no I/O   │    │ +update  │
 └──────────┘    └──────────┘

Every read also returns a content_hash. Hand it back as known_hash on your next read and the server answers unchanged from that fact alone, with no guess about what it already sent you.

search works the same way. An in-session LRU keyed on (query, k, directory) returns warm hits in ~10 µs; misses fall through to BM25 keyword search. Every cache mutation (put, clear, delete_path, update_mtime) bumps the LRU, so callers never see a result that predates a write.

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Performance

Measured on this project's 40 source files (177,509 tokens), i9-13900K, with the corpus held fixed across all phases. Reproducible via --json output for CI diffing.

Token savings: 98.9% overall (phases 2 to 6)

Phase	Scenario	Savings
Overall (cached, phases 2 to 6)	Aggregate token reduction	98.9%
Unchanged re-read	mtime match, fast path skips disk I/O	99.1%
Content hash	mtime drifted, BLAKE3 still matches	99.1%
Batch read	All files via batch_read, 200K budget	99.1%
Search previews	5 queries × k=5, previews vs full reads	99.7%
Small edits	Real ~5% line changes in 30% of files	97.8%
Cold read	First read, no cache (baseline)	0%

Latency: unchanged reads ~0.9 ms; repeat searches < 0.01 ms

Operation	p50	Notes
Single unchanged read (fast path)	0.9 ms	mtime + cache hit; no disk I/O
Single diff read (changed file)	0.7 ms	hash check + unified diff
Search k=5 (cache hit)	< 0.01 ms	in-session LRU; hundreds× vs cold
Search k=5 (cache miss)	1.5 ms	BM25 keyword search
Edit (scoped find/replace)	2.4 ms	uses cached content
Grep (literal def )	1.3 ms	FTS5 over cached corpus
Grep (regex)	3.7 ms	regex compiled once
Batch read (40 files, diff mode)	26.0 ms	chunk + tokenize new/changed files
Unchanged re-read (40 files)	18 ms	whole-corpus pass
Cold read (40 files, total)	125 ms	no embedding model, pure disk I/O plus tokenisation
Write (200-line file)	1.8 ms	creates + caches (no embed)

Run benchmarks yourself:

uv run python benchmarks/benchmark_token_savings.py    # token savings
uv run python benchmarks/benchmark_performance.py      # operation latency

See docs/performance.md for full benchmarks and methodology.

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Documentation

Guide	Description
Architecture	Component design, algorithms, data flow
Performance	Optimization techniques, benchmarks
Security	Threat model, input validation, size limits
Advanced Usage	Programmatic API, custom storage backends
Troubleshooting	Common issues, debug logging
Environment Variables	All configurable env vars with defaults and examples

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Contributing

git clone https://github.com/CoderDayton/semantic-cache-mcp.git
cd semantic-cache-mcp
uv sync
uv run pytest

See CONTRIBUTING.md for commit conventions, pre-commit hooks, and code standards.

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License

MIT License. Use it freely in personal and commercial projects.

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Credits

Built with FastMCP 3.2+ and:

• SQLite with FTS5 for keyword (BM25) full-text search, vendored as a small built-in store
• Semantic summarization based on TCRA-LLM (arXiv:2310.15556)
• BLAKE3 cryptographic hashing for content freshness
• W-TinyLFU frequency-aware cache eviction