Context Crumb
Compresses long files, prompt inputs, and MCP catalog descriptions into denser context for LLM agents while preserving the useful signal.
ContextCrumb
Shake the crumbs out of bloated context.
Before / After - Playground - Install - Quick Start - CLI - Agent + MCP - Model
LLM context gets messy fast: meeting notes, logs, issue threads, docs, transcripts, and tool descriptions all pile up until the useful signal is buried under filler.
ContextCrumb is a token-level compressor for LLM and agent workflows. It looks at text word by word and removes low-signal tokens while keeping the surviving text in the original order.
That is the idea behind the name: the context is still there, but the loose crumbs are shaken off before they reach your model. Less bloat in the prompt. More room for the parts that matter.
No install needed. Paste text, compare the kept context, and see what gets shaken off.
Before / After
ContextCrumb is not a summarizer. It does not rewrite your document into a new explanation. It keeps the source sequence and deletes expendable words.
Original
ContextCrumb is designed for coding agents, MCP tools, and prompt pipelines that need to read a large local text file before sending it to an LLM. It prints only the compressed text by default, so an agent can capture stdout and use it as shortened context.
Compressed
ContextCrumb designed coding agents, MCP tools, prompt pipelines need read large local text file before sending LLM. Prints compressed text default, agent capture stdout use shortened context.
Same order. Less padding. More room for the next file.
Why ContextCrumb?
| Use case | What changes |
|---|---|
| Agent file loading | Read long notes, docs, transcripts, and logs before they hit the context window. |
| Prompt pipelines | Shrink natural-language inputs without hand-writing summarizers. |
| MCP catalogs | Compress verbose tool/resource descriptions while preserving names and schemas. |
| Local workflows | Run ONNX inference by default, with cached model files after first download. |
| Trust-building | Use diff and inspect to see what was kept, deleted, and saved. |
Best fit: docs, notes, transcripts, issue threads, logs, research context, and other natural-language files. For source code where exact syntax matters, prefer raw file loading or use a conservative keep ratio.
Install
pip install contextcrumb
Optional extras:
pip install "contextcrumb[mcp]"
pip install "contextcrumb[serve]"
pip install "contextcrumb[torch]"
ContextCrumb uses the ONNX backend by default, so normal users do not need PyTorch or Transformers installed. Model files are cached locally after the first download.
Quick Start
from contextcrumb import ContextCompressor
compressor = ContextCompressor()
result = compressor.compress(
"ContextCrumb deletes low-value words while preserving useful context.",
)
print(result.text)
print(result.stats)
Read and compress a file:
from contextcrumb import ContextCompressor
compressor = ContextCompressor()
result = compressor.compress_file("notes.txt")
print(result.text)
print(result.stats["token_keep_ratio"])
CLI
The main agent-friendly command is load:
contextcrumb load notes.txt
It prints only compressed text by default, which makes it easy for agents, hooks, shell scripts, and prompt pipelines to capture stdout and move on.
Useful commands:
contextcrumb load notes.txt --json
contextcrumb diff notes.txt
contextcrumb inspect notes.txt
contextcrumb stats
diff marks deleted tokens like this:
kept words [-deleted words-] kept words
Agent + MCP
ContextCrumb includes an optional MCP stdio adapter for agent clients that can run Python tools through uvx.
pip install "contextcrumb[mcp]"
Published-package MCP config:
{
"mcpServers": {
"contextcrumb": {
"command": "uvx",
"args": [
"--from",
"contextcrumb[mcp]",
"contextcrumb-mcp"
]
}
}
}
The MCP server exposes:
compress_text
compress_file
ContextCrumb also ships contextcrumb-shrink, an MCP proxy that compresses verbose catalog descriptions before an agent sees them while forwarding tool names, schemas, calls, results, and resource contents unchanged.
Model
Model weights and a hosted demo are public on Hugging Face:
- Model: ymao20/contextcrumb-32m
- Playground: contextcrumb-32m-demo
Roadmap
Planned for later:
- Public docs for advanced compression modes and service deployment.
- JavaScript or TypeScript client.
- Hosted API experiments.
- npm publishing.
Development
uv pip install --python .\.venv\Scripts\python.exe -e ".[dev,mcp]"
.\.venv\Scripts\python.exe -m pytest
.\.venv\Scripts\python.exe -m build
Release notes are tracked in CHANGELOG.md.
License
Apache-2.0. See LICENSE.
Related Servers
Kone.vc
sponsorMonetize your AI agent with contextual product recommendations
SPAIK AI ROI
Predict and track AI ROI using Monte Carlo simulations, real-time industry benchmarks, and ML-powered insights.
UsageWall
Live LLM pricing as an MCP server — six read-only tools to list, compare, estimate cost, and find the cheapest model across OpenAI, Anthropic, Google, DeepSeek, Mistral, xAI and Meta.
PostalForm MCP
Mail real letters from agents: PDF → checkout → status.
Gmail MCP Server
An MCP server for interacting with Gmail and Google Calendar, enabling context-aware email and event management.
MCPal
Lightweight MCP server for native desktop notifications with action buttons, text replies, and LLM-aware icons.
Vedit-MCP
Perform basic video editing operations using natural language commands. Requires ffmpeg to be installed.
Qonto
Interact with the Qonto Business API to manage finances, transactions, and account information using API credentials.
MIE - Memory Intelligence Engine
Persistent knowledge graph MCP server that gives AI agents shared memory across sessions and providers. Stores facts, decisions, entities, and events with typed relationships.
AppleScript MCP
Execute AppleScript to gain full control of your Mac.
IWE
Knowledge graph MCP server for searching, reading, and refactoring hierarchical markdown documents