How do I use Semantic Cache MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Semantic Cache MCP Search for code semantically related to the user authentication flow" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Semantic Cache MCP by CoderDayton

Reduce Claude Code token usage by 80%+ with intelligent file caching.

Semantic Cache MCP is a Model Context Protocol server that eliminates redundant token consumption when Claude reads files. Instead of sending full file contents on every request, it returns diffs for changed files, suppresses unchanged files entirely, and intelligently summarizes large files — all transparently through 12 purpose-built MCP tools.

Features

80%+ Token Reduction — Unchanged files cost ~0 tokens; changed files return diffs only
Three-State Read Model — First read (full + cache), unchanged (message only, 99% savings), modified (diff, 80–95% savings)
Semantic Search — Hybrid BM25 + HNSW vector search via local ONNX embeddings (configurable model, default BAAI/bge-small-en-v1.5), no API keys, works offline
Batch Embedding — batch_smart_read pre-scans all new/changed files and embeds them in a single model call (N calls → 1)
Content Hash Freshness — BLAKE3 hash detects when mtime changes but content is identical (touch, git checkout) — returns cached instead of re-reading
Grep — Regex/literal pattern search across cached files with line numbers and context
Semantic Summarization — 50–80% token savings on large files, structure preserved
DoS Protection — Write size, edit size, and match count limits enforced at every boundary

Installation

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

Option 1 — uvx (always runs 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.

GPU Acceleration (Optional)

For NVIDIA GPU acceleration, install with the gpu extra:

uv tool install "semantic-cache-mcp[gpu]"
# or with uvx: uvx "semantic-cache-mcp[gpu]"

Then set EMBEDDING_DEVICE=gpu in your MCP config env block. Falls back to CPU automatically if CUDA is unavailable.

Custom Embedding Models

Any HuggingFace model with an ONNX export works — set EMBEDDING_MODEL in your env config:

"env": {
  "EMBEDDING_MODEL": "nomic-ai/nomic-embed-text-v1.5"
}

If the model isn't in fastembed's built-in list, it's automatically downloaded and registered from HuggingFace Hub on first startup (ONNX file integrity is verified via SHA256). See env_variables.md for model recommendations.

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 (80%+ token savings)

Tools

Core

Tool	Description
`read`	Smart file reading with diff-mode. Three states: first read (full + cache), unchanged (99% savings), modified (diff, 80–95% savings). Use `offset`/`limit` for line ranges.
`write`	Write files with cache integration. `auto_format=true` runs formatter. `append=true` enables chunked writes for large files. Returns diff on overwrite.
`edit`	Find/replace using cached reads — three modes: full-file, scoped to a line range, or direct line replacement. `dry_run=true` previews. `replace_all=true` handles multiple matches. Returns unified diff.
`batch_edit`	Up to 50 edits per call with partial success. Each entry can be find/replace, scoped, or line-range replacement. `auto_format=true` and `dry_run=true` supported.

Discovery

Tool	Description
`search`	Semantic/embedding search across cached files by meaning — not keywords. Seed cache first with `read` or `batch_read`.
`similar`	Finds semantically similar cached files to a given path. Start with `k=3–5`. Only searches cached files.
`glob`	Pattern matching with cache status per file. `cached_only=true` filters to already-cached files. Max 1000 matches, 5s timeout.
`batch_read`	Read 2+ files in one call. Supports glob expansion in paths, priority ordering, token budget, and per-file diff suppression for unchanged files. Pre-scans and batch-embeds all new/changed files in a single model call. Set `diff_mode=false` after context compression.
`grep`	Regex or literal pattern search across cached files with line numbers and optional context lines. Like ripgrep for the cache.
`diff`	Compare two files. Returns unified diff plus semantic similarity score. Large diffs are auto-summarized to stay within token budget.

Management

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

Tool Reference

read path="/src/app.py"
read path="/src/app.py" diff_mode=true         # default
read path="/src/app.py" diff_mode=false        # full content (use after context compression)
read path="/src/app.py" offset=120 limit=80    # lines 120–199 only

Three states:

State	Response	Token cost
First read	Full content + cached	Normal
Unchanged	`"File unchanged (1,234 tokens cached)"`	~5 tokens
Modified	Unified diff only	5–20% of original

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

# Mode A — find/replace: searches 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: search only within line range (shorter old_string suffices)
edit path="/src/app.py" old_string="pass" new_string="return x" start_line=42 end_line=42

# Mode C — line replace: replace entire range, no old_string needed (maximum 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

# 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

search query="authentication middleware logic" k=5
search query="database connection pooling" k=3

similar path="/src/auth.py" k=3
similar path="/tests/test_auth.py" k=5

glob pattern="**/*.py" directory="./src"
glob pattern="**/*.py" directory="./src" cached_only=true

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

Glob expansion: src/*.py expanded inline (max 50 files per glob)
Priority ordering: priority paths read first, remainder sorted smallest-first
Token budget: stops reading new files once max_total_tokens reached; skipped files include est_tokens hint
Unchanged suppression: unchanged files appear in summary.unchanged with no content (zero tokens)
Batch embedding: pre-scans all new/changed files and embeds them in a single model call before reading — N model calls reduced to 1
Context compression recovery: set diff_mode=false when Claude needs full content after losing context

diff path1="/src/v1.py" path2="/src/v2.py"

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)
`MAX_CONTENT_SIZE`	`100000`	Max bytes returned by read operations
`MAX_CACHE_ENTRIES`	`10000`	Max cache entries before LRU-K eviction
`EMBEDDING_DEVICE`	`cpu`	Embedding hardware: `cpu`, `cuda` (GPU), `auto` (detect)
`EMBEDDING_MODEL`	`BAAI/bge-small-en-v1.5`	FastEmbed model for search/similarity (options)
`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",
        "EMBEDDING_DEVICE": "cpu",
        "EMBEDDING_MODEL": "BAAI/bge-small-en-v1.5"
      }
    }
  }
}

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

How It Works

┌─────────────┐     ┌──────────────┐     ┌──────────────────┐
│  Claude     │────▶│  smart_read  │────▶│  Cache Lookup    │
│  Code       │     │              │     │  (VectorStorage) │
└─────────────┘     └──────────────┘     └──────────────────┘
                           │
         ┌─────────────────┼─────────────────┐
         ▼                 ▼                 ▼
   ┌──────────┐     ┌──────────┐     ┌──────────────┐
   │Unchanged │     │ Changed  │     │  New / Large │
   │  ~0 tok  │     │  diff    │     │ summarize or │
   │  (99%)   │     │ (80-95%) │     │ full content │
   └──────────┘     └──────────┘     └──────────────┘

Performance

Measured on this project's 30 source files (~136K tokens). Benchmarks run on a standard dev machine (CPU embeddings).

Token Savings

Phase	Scenario	Savings
Cold read	First read, no cache	0% (baseline)
Unchanged re-read	Same files, no modifications	99.1%
Content hash	Touch files (mtime changed, content identical)	99.1%
Small edits	~5% of lines changed in 30% of files	98.1%
Batch read	All files via `batch_read`	99.1%
Search	5 queries × k=5, previews vs full reads	98.4%
Overall (cached)	Phases 2–6 combined	98.8%

Operation Latency

Operation	Time
Unchanged read (single file)	2 ms
Unchanged re-read (29 files)	25 ms
Batch read (29 files, diff mode)	35 ms
Cold read (29 files, incl. embed)	2,554 ms
Write (200-line file)	47 ms
Edit (scoped find/replace)	48 ms
Semantic search (k=5)	4 ms
Semantic search (k=10)	5 ms
Find similar (k=3)	49 ms
Grep (literal)	1 ms
Grep (regex)	2 ms
Embedding model warmup	206 ms
Single embedding (largest file)	47 ms
Batch embedding (10 files)	469 ms

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.

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

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.

License

MIT License — use freely in personal and commercial projects.

Credits

Built with FastMCP 3.0 and:

FastEmbed — local ONNX embeddings (configurable, default BAAI/bge-small-en-v1.5)
SimpleVecDB — HNSW vector storage with FTS5 keyword search
Semantic summarization based on TCRA-LLM (arXiv:2310.15556)
BLAKE3 cryptographic hashing for content freshness
LRU-K frequency-aware cache eviction

Semantic Cache MCP