What can you do with this server?

Semantic Cache MCP is a Model Context Protocol server that reduces AI model token usage by 80%+ through intelligent file caching, semantic search, and efficient file operations. Token-Efficient File Reading: * read — Automatic three-state responses: full content (first read), unchanged marker (~0 tokens on cache hit), or unified diff (modified, 80-95% savings) * batch_read — Read multiple files under a token budget with glob expansion, priority ordering, unchanged suppression, and batch embedding File Modifications: * write — Create or replace files with cache refresh, overwrite diffs, append support, and optional auto-formatting * edit — Targeted edits via three modes: find/replace, scoped (line-range bounded), or line-range replacement * batch_edit — Apply multiple edits to a single file in one call with partial success reporting * delete — Delete a file/symlink with cache eviction and dry-run preview Search & Discovery: * search — Semantic (meaning-based) hybrid BM25 + HNSW vector search across cached files (no API keys, works offline) * similar — Find semantically related files via nearest-neighbor lookup * grep — Exact regex or literal string search with line numbers and context * glob — Discover files by pattern with cache coverage indicators * diff — Compare two files with a unified diff and semantic similarity score Cache Management & Diagnostics: * stats — View token savings, hit rates, tool call counts, embedding model performance, and memory usage * clear — Reset all cache entries to force cold re-seeding Key Technical Features: * BLAKE3 content hashing detects unchanged files even when timestamps change * Local ONNX embeddings (default: BAAI/bge-small-en-v1.5); supports custom HuggingFace models * LRU-K cache eviction with up to 10,000 entries * Optional GPU acceleration (NVIDIA CUDA) * DoS protection via configurable write/edit size limits and match count caps * Can block native file tools to force all I/O through semantic-cache for maximum savings

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.

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Semantic Cache MCP

by CoderDayton

Overview Schema Related Servers Score Discussions

Python

Local

Cut your MCP client's token usage by 98% on cached reads. Respond in milliseconds.

Semantic Cache MCP is a Model Context Protocol server that replaces redundant full-file reads with marker hits, unified diffs, and semantic summaries. Thirteen tools (read, read_image, batch_read, write, edit, edit_preview, batch_edit, search, grep, glob, delete, clear, stats) route every file operation through one cache-aware layer, so an MCP-capable agent skips files it has already seen.

Why this exists

In order of impact:

1. Reads stop costing tokens. The first read seeds the cache. Re-reads of unchanged files return a 5-token marker (mtime match, no disk I/O). Modified files return a unified diff. Files larger than the budget collapse to a semantic skeleton that preserves structure rather than slicing at a byte offset.

2. Search and grep run on the cache, not the disk. Semantic search (hybrid BM25 + HNSW), 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. Local FastEmbed is the default embedding provider; OpenAI-compatible endpoints are opt-in.

Installation

Add to Claude Code settings (~/.claude.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": "Snowflake/snowflake-arctic-embed-m-v2.0"
}

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.

OpenAI-Compatible Embeddings

Local FastEmbed remains the default. To route embeddings through an OpenAI-compatible provider instead, enable it in the MCP env block. Defaults target Ollama:

"env": {
  "OPENAI_EMBEDDINGS_ENABLED": "true",
  "OPENAI_BASE_URL": "http://localhost:11434/v1",
  "OPENAI_API_KEY": "ollama",
  "OPENAI_EMBEDDING_MODEL": "nomic-embed-text"
}

Run ollama pull nomic-embed-text first if the model is not installed. For hosted OpenAI, set OPENAI_BASE_URL=https://api.openai.com/v1, use a real OPENAI_API_KEY, and choose an embedding model such as text-embedding-3-small. OPENAI_EMBEDDING_DIMENSIONS is optional; leave it unset to infer the returned vector size.

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)

Tools

Core

Tool	Description
`read`	Single-file cache-aware read. Returns full content on first read, unchanged markers on cache hits, diffs on modifications, and supports `offset`/`limit` for targeted 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 semantic search — find code by meaning when you don't know the exact symbol to grep for. 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.

Tool Reference

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

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

Automatic 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

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.

search query="authentication middleware logic" k=5
glob pattern="**/*.py" directory="./src" cached_only=true
grep pattern="class Cache" path="src/**/*.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)
`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 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)
`OPENAI_EMBEDDINGS_ENABLED`	`false`	Use OpenAI-compatible remote embeddings instead of local FastEmbed
`OPENAI_BASE_URL`	`http://localhost:11434/v1`	OpenAI-compatible base URL; default targets Ollama
`OPENAI_API_KEY`	`ollama`	API key for the remote embedding provider
`OPENAI_EMBEDDING_MODEL`	`nomic-embed-text`	Remote embedding model name
`OPENAI_EMBEDDING_DIMENSIONS`	(inferred)	Optional requested/expected remote embedding dimension
`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 │────▶│ 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  │
 └──────────┘    └──────────┘

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

Performance

Measured on this project's 43 source files (168,614 tokens), CPU embeddings, i9-13900K, commit 5cd7100. Reproducible via --json output for CI diffing.

Token savings — 98.5% overall (phases 2–6)

Phase	Scenario	Savings
Overall (cached, phases 2–6)	Aggregate token reduction	98.5%
Unchanged re-read	mtime match — fast path skips disk I/O	98.9%
Content hash	mtime drifted, BLAKE3 still matches	98.9%
Batch read	All files via `batch_read`, 200K budget	98.9%
Search previews	5 queries × k=5, previews vs full reads	98.3%
Small edits	Real ~5% line changes in 30% of files	97.3%
Cold read	First read, no cache (baseline)	0%

Latency — unchanged reads ~1 ms; repeat searches ~10 µs

Operation	p50	Notes
Single unchanged read (fast path)	1.1 ms	mtime + cache hit; no disk I/O
Single diff read (changed file)	1.0 ms	hash check + unified diff
Search k=5 (cache hit)	< 0.01 ms	in-session LRU; 2,000×+ vs cold
Search k=5 (cache miss)	5.6 ms	embed query + hybrid BM25/HNSW
Edit (scoped find/replace)	3.3 ms	uses cached content
Grep (literal `def` )	1.4 ms	FTS5 over cached corpus
Grep (regex)	2.1 ms	regex compiled once
Batch read (43 files, diff mode)	40.2 ms	one ONNX inference for all new/changed files
Unchanged re-read (43 files)	26.9 ms	whole-corpus pass
Cold read (43 files, total)	1,990 ms	includes disk I/O, tokenisation, embedding
Write (200-line file)	49.1 ms	creates + caches + embeds
Single embedding (largest file)	47 ms	ONNX, single thread
Model warmup (one-time)	195 ms	startup only

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 ≥ 2.6.0 — HNSW vector storage with FTS5 keyword search, atomic delete_collection, and opt-in embedding persistence (store_embeddings=True)
Semantic summarization based on TCRA-LLM (arXiv:2310.15556)
BLAKE3 cryptographic hashing for content freshness
LRU-K frequency-aware cache eviction

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

5dRelease cycle

18Releases (12mo)

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Why this exists

Installation

GPU Acceleration (Optional)

Custom Embedding Models

OpenAI-Compatible Embeddings

Block Native File Tools (Recommended)

CLAUDE.md Configuration

Tools

Core

Discovery

Management

Tool Reference

Configuration

Environment Variables

Safety Limits

MCP Server Config

How It Works

Performance

Token savings — 98.5% overall (phases 2–6)

Latency — unchanged reads ~1 ms; repeat searches ~10 µs

Documentation

Contributing

License

Credits

Maintenance

Resources

Tools

Appeared in Searches

Latest Blog Posts

MCP directory API