compresh-mcp
Provides production-grade context compression for LLM agent conversations with Q-protective ranking, epistemic markers, and semantic store, reducing token usage while preserving equivalence.
README
compresh-mcp
MCP server for Compresh — production-grade context compression for LLM agent conversations.
Compresh adds Q-protective ranking, epistemic marker classification, and depth-aware adaptation on top of the open-source
tulbasecompression core. This is the paid tier distribution.
Architecture (0.2.0+)
- Local: bundled
tulbase(MIT, vendored ascompresh_mcp.tulbase) runs on every turn for fast compression + cold-storage support (fetch_compressed/list_compressedMCP tools). - Server: TUL 1.0 layers (Q-protective ranking, epistemic markers, semantic store) run on Compresh infrastructure via the
/v1/tul1endpoint, gated by your Compresh API key + tier. - Degraded mode: if
/v1/tul1is unreachable, compresh-mcp transparently falls back to the local tulbase result so compression never blocks.
Previously (0.1.0, yanked), TUL 1.0 classifiers shipped inside the client package. That distribution leaked paid features into the local install and is no longer recommended — install 0.2.0 or later. See CHANGELOG.md for the migration note.
What's the difference vs tulbase-mcp?
| Feature | tulbase-mcp (free, open-source) |
compresh-mcp (paid) |
|---|---|---|
| Base LexRank summarization | ✅ | ✅ |
| Modality elision (code, terminal, JSON, stack traces) | ✅ | ✅ |
| Cold storage + fetch_compressed | ✅ | ✅ |
| Protection Zone (Claim 1e) | ✅ | ✅ |
| Q-protective sentence ranking (Q1–Q4 categorization) | ❌ | ✅ |
| Epistemic markers (VR/HR/CR/UC) | ❌ | ✅ |
| Semantic store (cross-turn Q3 dedup) | ❌ | ✅ |
| Saving telemetry to Compresh dashboard | ❌ | ✅ |
| Multi-device sync (planned) | ❌ | ✅ |
In Compresh's bench (Compresh-bench v1, 600-turn multi-model), Q-protective ranking adds 5–12 percentage points of equivalence preservation vs base LexRank at the same token savings — Pareto improvement.
Pricing
| Plan | Period | Saving-share |
|---|---|---|
| Starter (free + budget loaded) | pay-as-you-go | 30% |
| Pro Quarterly ($18) | 3 mo | 20% |
| Pro Semi-Annual ($33) | 6 mo | 16% |
| Pro Annual ($60) | 1 yr | 12% |
| Anonymous / free / local LLM | — | 0% (free, tulbase only) |
Every new user: $30 free credit (90-day expiry), $10 minimum top-up (charged $7.50 with a permanent 25% discount on top-ups).
Saving-share is the cut Compresh takes on the savings measured against the user's chosen model. The base value comes from the actual model price (when known) or the provider family's cheapest model; anonymous / free-model usage falls back to a flat $0.20 / 1M saved input tokens.
See compre.sh/pricing for the canonical pricing page.
Installation
pip install compresh-mcp
On first run, you'll be prompted for your Compresh API key. If you don't have an account, your browser opens to compre.sh/signup automatically.
MCP client configuration
Claude Code (~/.claude/mcp.json)
{
"mcpServers": {
"compresh": {
"command": "compresh-mcp",
"env": {
"COMPRESH_API_KEY": "sk-comp_...",
"COMPRESH_API_BASE": "https://api.compre.sh"
}
}
}
}
Cursor (~/.cursor/mcp.json)
Same structure as Claude Code.
Cowork
Cowork → Settings → Tools → MCP servers → Add:
- Command:
compresh-mcp - Environment:
COMPRESH_API_KEY=sk-comp_...
Tools exposed
Same four tools as tulbase-mcp, with enhanced behavior:
compress— Q-protective compression by default (protection_mode="balanced")fetch_compressed,list_compressed,stats— same interface
Plus paid-tier extras:
usage— current cycle budget, free credit balance, savings metrics
License
Business Source License 1.1 — see LICENSE. Production use permitted with valid Compresh API key. License automatically converts to MIT after 4 years (Year 2030).
Patents
Q-protective sentence ranking + Protection Zone are covered by TR-TPMK patent application 2026/007305 (Compresh Ltd, May 2026). A valid Compresh subscription grants implementation license.
Status
v0.1.0 — first public release, May 2026. Active development. APIs may
change before v1.0. Issues and pull requests welcome.
Links
- compre.sh — product site
- Documentation — full reference
- GitHub — source
- Issues — bug reports
- tulbase (open core, MIT) — standalone
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.