CC Token Saver MCP

Reduce your Claude Code token usage by delegating simple tasks to a local LLM.

The MCP server exposes your local LLM as tools that Claude Code can call for:

• Code snippet generation and simple refactoring
• Unit test generation (pytest, unittest, jest, and more)
• Code explanation at beginner, developer, or expert level
• Documentation writing and code reviews
• Summarizing long texts, logs, and READMEs
• Generating Conventional Commits messages from diffs
• Text translation with markdown formatting preserved
• Listing and switching between locally available models

Claude Code routes simple, self-contained subtasks to the local LLM first, only spending premium tokens on complex reasoning and multi-step workflows.

Installation

From Debian/Ubuntu package

apt install python3-cc-token-saver-mcp

From source

pip install fastmcp openai python-dotenv
git clone https://github.com/Vitexus/cc_token_saver_mcp.git

Configuration

Local LLM

Create a .env file in the directory where you launch the server:

# Local LLM Configuration
OPENAI_API_KEY=none
OPENAI_BASE_URL=http://localhost:1234/v1
LOCAL_MODEL_NAME=qwen2.5-7b-instruct
LOCAL_LLM_TEMPERATURE=0.7
LOCAL_LLM_MAX_TOKENS=-1

Variable	Default	Description
OPENAI_API_KEY	none	Key sent to the local endpoint (usually ignored)
OPENAI_BASE_URL	http://localhost:1234/v1	OpenAI-compatible API base URL
LOCAL_MODEL_NAME	qwen2.5-7b-instruct	Model name to request
LOCAL_LLM_TEMPERATURE	0.7	Sampling temperature
LOCAL_LLM_MAX_TOKENS	-1	Max tokens per response (-1 = no limit)

Claude Code MCP config

If installed from the Debian package, add to ~/.claude.json:

"mcpServers": {
  "cc-token-saver": {
    "type": "stdio",
    "command": "cc-token-saver-mcp"
  }
}

If running from source, point to server.py instead:

"mcpServers": {
  "cc-token-saver": {
    "type": "stdio",
    "command": "python",
    "args": ["<path>/cc_token_saver_mcp/server.py"]
  }
}

Real-world example (Ollama + Debian package)

"mcpServers": {
  "cc-token-saver": {
    "type": "stdio",
    "command": "/usr/bin/cc-token-saver-mcp",
    "args": [],
    "env": {
      "OPENAI_API_KEY": "none",
      "OPENAI_BASE_URL": "http://localhost:11434/v1",
      "LOCAL_MODEL_NAME": "qwen2.5-coder:7b",
      "LOCAL_LLM_TEMPERATURE": "0.7",
      "LOCAL_LLM_MAX_TOKENS": "-1"
    }
  }
}

This snippet comes from a real ~/.claude.json running the Debian package against a local Ollama instance (ollama pull qwen2.5-coder:7b). LM Studio users only need to change OPENAI_BASE_URL to http://localhost:1234/v1.

Tools

query_local_llm

Send a prompt to the local LLM.

Parameter	Type	Default	Description
prompt	str	required	The user prompt
system_message	str	helpful assistant	System message
temperature	float	env default	Override temperature
max_tokens	int	env default	Override max tokens

query_local_llm_with_context

Send a prompt with additional context (e.g. a code snippet).

Parameter	Type	Default	Description
prompt	str	required	The task/question
context	str	required	Additional context
task_type	str	general	code_review, documentation, refactor, general
system_message	str	auto	Override system message

list_available_models

List all models installed in the local Ollama / LM Studio instance. No parameters.

switch_model

Switch the active model for all subsequent calls in the current session.

Parameter	Type	Description
model_name	str	Model identifier (e.g. qwen2.5-coder:7b-16k)

summarize_text

Produce a concise summary of a long text to save tokens on large file contexts.

Parameter	Type	Default	Description
text	str	required	Text to summarize
max_words	int	150	Target summary length
focus	str	""	Aspect to emphasise (e.g. "security issues")

generate_commit_message

Generate a Conventional Commits message from a git diff output.

Parameter	Type	Default	Description
diff	str	required	Output of git diff --staged
extra_context	str	""	Motivation or ticket reference

generate_unit_tests

Generate boilerplate unit tests for a given code snippet.

Parameter	Type	Default	Description
code	str	required	Source code to test
framework	str	pytest	Test framework (unittest, jest, go test, …)
extra_instructions	str	""	Additional guidance

explain_code

Explain what a piece of code does in plain language.

Parameter	Type	Default	Description
code	str	required	Code to explain
audience	str	developer	beginner, developer, or expert

translate_text

Translate text to another language, preserving markdown and code-block structure.

Parameter	Type	Default	Description
text	str	required	Text to translate
target_language	str	required	Language name (e.g. Czech, German)
preserve_formatting	bool	True	Keep markdown structure intact

CLAUDE.md Integration

You can instruct Claude Code to automatically delegate commit message generation to the local LLM by adding a rule to ~/.claude/CLAUDE.md:

## Commit Message Generation

When creating a git commit, ALWAYS generate the commit message via the local LLM — never write it manually:

1. Run `git diff --staged` to get the staged diff.
2. Call `mcp__cc-token-saver__generate_commit_message` with that diff (and an `extra_context` string if there is a ticket number or motivation).
3. Use the returned message verbatim in `git commit -m "..."`.

This applies to every commit in every project.

With this rule in place, simply say "commit this" — Claude Code calls cc-token-saver, generates a Conventional Commits message from the staged diff, and commits without spending premium tokens on message writing.

The screenshot above shows the full flow: Claude Code ran git diff --staged, called cc-token-saver, ran two shell commands, and produced the commit — all triggered by a single "commit this" instruction.

Estimated Savings

Prices based on Claude Sonnet 4.6 ($3/MTok input, $15/MTok output) — the default Claude Code model.

Direct savings per day (moderate developer, ~5 h active)

Task offloaded to local LLM	Times/day	Output tokens saved	Saving/day
Commit messages	8	~100 tok	$0.012
Code explanations	5	~450 tok	$0.034
Unit test generation	3	~750 tok	$0.034
Quick snippets / one-liners	10	~350 tok	$0.053
Text summarisation	2	~150 tok	$0.005
Total	~28	~10 700 tok/day	~$0.14

Indirect savings — context bloat

Every token Claude generates is added to the conversation history and re-sent as input on every subsequent turn. Offloading ~10 000 tokens/day across a 20-turn session avoids re-sending them repeatedly:

10 000 tok × 20 turns × $3/MTok ≈ $0.60/day

Monthly totals (22 working days)

Profile	Active hrs/day	Tokens saved/month	$/month
Light (2–3 h)	2–3	~200 K output + 1 M input	$3–8
Moderate (5–6 h)	5–6	~500 K output + 3 M input	$10–20
Heavy (8+ h)	8+	~1 M output + 6 M input	$20–40

Biggest single win: commit message generation via CLAUDE.md — fully automatic, zero extra effort, adds up to ~40 free commits per week.

Examples

License

MIT