Skip to main content

🦾 Token-optime

A middleware MCP server that sits between Claude Desktop and your downstream MCP servers — reducing token consumption through semantic caching, dynamic tool selection, and response trimming.

LLM Backend: Groq · OpenAI · Anthropic · DeepSeek · Ollama | Vector Store: ChromaDB | Dashboard: Streamlit


🎯 What Problem This Solves

Every Claude Desktop session sends the full schema of every connected MCP tool with every prompt. With 23+ tools registered, that's thousands of tokens injected per query — even when only one tool is needed.

Token-optime optimises at three levels:

  1. Semantic Tool Selection — Only the most relevant tool schema is sent to Claude per query (ChromaDB similarity search)
  2. Semantic Caching — Repeated or similar queries skip tool calls entirely and return cached answers (similarity ≥ 0.8)
  3. Response Trimming — Verbose tool responses are trimmed before entering the context window

Real result from testing: 91.8% schema token reduction per call.


📁 Project Structure

.
├── README.md                       
├── config.py                      # Settings & env loading
├── requirements.txt               # Python dependencies
├── Dockerfile                     # For EC2 dashboard deployment
├── pyproject.toml                 # PyPI package definition
│
├── token_optimise/
│ ├── init.py
│ ├── main.py                      # Entry point (pip install)
│ └── config.py                    # Settings & env loading
|
├── src/
│ ├── front.py                     # Streamlit dashboard
│ ├── admin_front.py               # Admin dashboard
│ ├── core/
│ │ ├── cache.py                   # Semantic cache
│ │ ├── trim.py                    # Response trimmer
│ │ ├── tool_selection.py          # Tool selection
│ │ ├── client.py                  # Groq/Claude client
│ │ ├── document_search.py         # RAG (index + search)
│ │ └── db.py                      # SQLite audit logging
│ │
│ └── mcp/
│ ├── server.py                    # Main MCP server
│ ├── server_http.py               # FastAPI + metrics
│ └── dash_api.py                  # Lightweight API for EC2 deployment
└── storage/
├── chroma_db/                     # ChromaDB persistent store
└── token_audit.db                 # SQLite audit logs

Our Test Case

  1. list files on /Users/../Desktop
  2. list files on /Users/../Desktop
  3. index /Users/../Desktop/leave_policy.pdf doc_id leave
  4. what is the leave policy?
  5. what is the leave policy?
  6. read a file

🏗️ System Architecture

                    Claude Desktop
                          │
                          ▼
┌──────────────────────────────────────────────────────────┐
│ Token-Optimised MCP Server                               │
│ ┌─────────────────────────────────────────────────────┐  │
│ │ Exposed Tools                                       │  │
│ │ • find_tool (orchestrator)                          │  │
│ │ • index_document / ask_document                     │  │
│ │ • search_all_documents / index_documents_folder     │  │
│ └─────────────────────────────────────────────────────┘  │
│ ┌─────────────────────────────────────────────────────┐  │
│ │ Optimization Layer                                  │  │
│ │ ┌──────────────┐ ┌──────────────────────────────┐   │  │
│ │ │ Semantic     │ │ Tool Selection               │   │  │
│ │ │ Cache        │ │ (ChromaDB → top-k tools)     │   │  │
│ │ │ (sim ≥ 0.8)  │ │                              │   │  │
│ │ └──────────────┘ └──────────────────────────────┘   │  │
│ │ ┌──────────────┐ ┌──────────────────────────────┐   │  │
│ │ │ Response     │ │ Token Audit                  │   │  │
│ │ │ Trimmer      │ │ (SQLite audit log)           │   │  │
│ │ │ (≤500 tok)   │ │                              │   │  │
│ │ └──────────────┘ └──────────────────────────────┘   │  │
│ └─────────────────────────────────────────────────────┘  │
└────────────┬──────────────────┬──────────────────────────┘
             │                  │
             ▼                  ▼
      ┌─────────────┐    ┌──────────────┐
      │ Filesystem  │    │ Memory MCP   │
      │ MCP         │    │ (remote)     │
      │ (14 tools)  │    │ (9 tools)    │
      └─────────────┘    └──────────────┘

🚀 Install — One Command

Prerequisites

  • Python 3.10+
  • Node.js + npx
  • An API key from any supported provider (or Ollama for local, no key needed)
  • Claude Desktop (latest)

New users — two commands

pip install token-optimise
token-optimise

The installer will:

  • Ask which AI provider you want to use
  • Ask for your API key
  • Ask which folder it can access on your machine
  • Write the MCP config into Claude Desktop automatically
  • Start the server in the background

Switch provider later

token-optimise --change-provider

Then restart with token-optimise.

📊 Dashboard

Local (your metrics only)

http://localhost:7738


🔧 Ports

Service Port
FastAPI / MCP 7737
Streamlit Dashboard 7738

These are intentionally non-default to avoid conflicts with other projects.


💬 For Best Results — Tell Claude to Use Token-optime

Claude Desktop has its own built-in tools (memory, web search) that it may prefer by default. For consistent routing through Token-optimise:

Add this to Settings → Instructions for Claude:

You have access to a token MCP server. Follow these rules strictly:
For ALL tasks — files, Gmail, Notion, memory, or anything else — always use token:execute first before calling any other tool.
For PDF questions always call token:list_indexed_documents first then token:ask_document.
After every response call token:wick_track.

DOCUMENT/PDF TASKS:
- ALWAYS call list_indexed_docs first, then ask_document immediately.
- NEVER ask the user for clarification.

AFTER EVERY RESPONSE: Call wick_track.

You can start your conversation with :

Use token:execute for every task. Never call other tools directly.

For PDF tasks specifically:

First call token:list_indexed_documents, then token:ask_document.

Why is this needed? Claude decides which tool to call — Token-optime can't force it. The system prompt in the config nudges Claude, but an explicit instruction in the chat is the most reliable way to ensure Token-optime is used. This is an honest limitation of how Claude Desktop works, not a bug in Token-optime.


Dashboard Metrics

Metric What it means
Schema Tokens — Estimated Baseline Tokens Claude would receive with all tool schemas
Schema Tokens — With Token-optime Tokens Claude actually received (1 selected schema)
Total Tokens Saved Schema savings + response trim savings
Cache Hit Rate % of queries that returned cached answers
Groq Cost (actual ✅) Real measured cost at Groq pricing
Claude Cost Saved (estimated ⚠️) Range across Haiku→Opus (model unknown)
Live USD/INR Rate Fetched live every hour

🐛 Troubleshooting

Server not starting

cat ~/.token_optimise/server.log

Port 7737 in use

lsof -i :7737
kill <PID>
token-optimise

Claude not using token:execute

  • Check Settings → Developer → MCP Servers — token should show green
  • Add explicit instruction at start of conversation

Dashboard offline

token-optimise

Want to change your API key or provider

token-optimise --change-provider

Check your current config

cat ~/.token_optimise/.env

⚠️ Known Limitations

  • Claude's built-in tools take priority — memory and web search bypass Token-optime because Claude prefers its native tools
  • Cost estimates are approximate — based on Sonnet 4.6 pricing; varies by model
  • PDF files only — document indexing supports PDF format only

Metadata

Release files for token-optimise 0.1.7

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for token-optimise 0.1.7
File Size Uploaded
token_optimise-0.1.7.tar.gz 45.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for token-optimise 0.1.7
File Interpreter ABI Platform
token_optimise-0.1.7-py3-none-any.whl Python 3 none any Details

Total release size: 84.5 kB

Release files / token_optimise-0.1.7.tar.gz

Download URL token_optimise-0.1.7.tar.gz
Size 45.1 kB
Tags Source
SHA-256 checksum
How to use checksums
8be040e41f2d9a9382564f1e99f3f0502c561193cc755e487ed6cfb1813fcdf8
BLAKE2b-256 checksum
How to use checksums
18969c5e1ff78429c90b8684b38d41003d73504c7a8512eb4aeb786f4a026716
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.3

Release files / token_optimise-0.1.7-py3-none-any.whl

Download URL token_optimise-0.1.7-py3-none-any.whl
Size 39.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
37e066c46b854d58b02abbeda3703ba131e4fb7f478fdf71fd49d4b4eb3c6169
BLAKE2b-256 checksum
How to use checksums
419d70676760f95357e016e68b19c7e13d57de6f5c4f72a48d2900f35811eef8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.13.3

Release history Release notifications | RSS feed

0.2.0

2 release files

0.1.9

2 release files

0.1.8

2 release files

This release

0.1.7 This release

2 release files

0.1.6

2 release files

0.1.5

2 release files

0.1.4

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page