Semantic Cache MCP

Name: Semantic Cache MCP
Author: CoderDayton

by CoderDaytonversion 0.7.0i★ –iRuns on your computer

Registry nameio.github.CoderDayton/semantic-cache-mcp

Install

Overview

Description from the maker (original English)

File cache for coding agents: re-reads cost ~40 tokens, changed files return diffs.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

claude mcp add semantic-cache -- uvx semantic-cache-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "semantic-cache": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "semantic-cache-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account CoderDayton, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32435 servers in this category
Published to the registry more recently than 72% of the other 32435 servers in this category
Basic checks equal to or better than 100% of the other 32435 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Semantic Cache MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.7.0) reached the registry 14 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Apify MCP Server

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs on your computer★ 440MITUpdated 14 months ago

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Semantic Cache MCP

Stop paying for the same file twice.

An MCP file cache for AI coding agents: a re-read costs ~40 tokens instead of the whole file.

Coding agents read the same files over and over: before an edit, after an edit, after every context compaction. A built-in Read sends the whole file each time. Semantic Cache MCP knows what your agent already holds. Measured on one 7,891-token file:

Read	Built-in `Read`	Semantic Cache MCP
First read	7,891 tokens	full file, plus a hash
Re-read, file unchanged	7,891 tokens	77 tokens
Re-read after a one-line edit	7,891 tokens	137 tokens (a diff)

Across a 48-file project it saves 98.7% of re-read tokens, and an unchanged read takes 0.17 ms. It also gives your agent grep, BM25 search and glob over the cache, atomic edits with dry_run, and token-budgeted batch reads. Three runtime dependencies, no API keys, no GPU.

Quick start

You need uv. In Claude Code:

claude mcp add semantic-cache --scope user -- uvx semantic-cache-mcp

Any other client (Cursor, Claude Desktop, and others that take mcpServers JSON):

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

Configs for VS Code, Codex and OpenCode, and how to turn off the built-in file tools so the agent uses the cache: [docs/clients.md](docs/clients.md).

How it works

Every file the server sends comes with a content_hash. The agent passes it back as known_hash on the next read:

flowchart LR

R["read(path, known_hash)"] --> M{"hash matches the file now?"} M -- yes --> U["unchanged ~40 tokens"] M -- "no, older version" --> D["diff of changed lines"] M -- "no hash" --> F["full file + content_hash"]

No stale content. Every read checks the file on disk first. unchanged only answers when the hash matches the current bytes.
Forgetting is safe. After a compaction, the agent reads without the hash and gets the full file back.
Large files become maps. A file over max_size returns an outline of its definitions, not a blind cut.
Everything stays local. The cache is a SQLite file. One background daemon serves every client on your machine.

Tools

read · batch_read · read_image · write · edit · batch_edit · edit_preview · delete · warm · grep · search · glob · stats · clear

Parameters and examples: [docs/tools.md](docs/tools.md).

FAQ

Is the search "semantic" in the embeddings sense? No. search is BM25 keyword ranking and grep is exact. No model to download, no GPU, no API key.

Does it send my code anywhere? No. The only network request is a one-time download of the o200k tokenizer file, checked against a SHA-256 hash.

Does it work on Windows? Yes. CI runs on Linux and macOS.

Documentation

Guide	Covers
Clients	Setup for each client, turning off built-in file tools
Tools	Every tool, with parameters and examples
Environment Variables	Every setting, with defaults
Performance	Benchmarks and methodology
Architecture	Storage, algorithms, the shared daemon
Security	Threat model, size limits, path handling
Troubleshooting	Common issues, debug logging
Advanced Usage	Python API

Contributing

git clone https://github.com/CoderDayton/semantic-cache-mcp.git

cd semantic-cache-mcp uv sync --extra dev uv run pytest

See CONTRIBUTING.md. Issues and pull requests are welcome.

License

MIT.

Semantic Cache MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Apify MCP Server

README from the maker

Quick start

How it works

Tools

FAQ

Documentation

Contributing

License