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mcp-compressor MCP Server

atlassian-labs/mcp-compressor

Score: 83.0 ↑ 10.9% Rank #1304 MCP Server
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An MCP server wrapper for reducing tokens consumed by MCP tools, available in typescript, python, and rust

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Overview

atlassian-labs/mcp-compressor is a Rust MCP server licensed under Apache-2.0. An MCP server wrapper for reducing tokens consumed by MCP tools, available in typescript, python, and rust

Ranked #1304 out of 24846 indexed tools.

In the top 6% of all indexed tools.

Has 11 contributors.

Ecosystem

Rust Apache-2.0

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 131

131 stars → growing community

Freshness 25% 17d ago

Last commit 17d ago → recently updated

Issue Health 25% 73%

8/11 issues closed → decent issue management

Contributors 10% 11

11 contributors → active community

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 14
Description Good
License Apache-2.0

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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AgentRank score for atlassian-labs/mcp-compressor
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Matched Queries

"mcp server""mcp-server"

From the README

# mcp-compressor

`mcp-compressor` helps agents use large MCP servers without spending huge amounts of context on tool descriptions and schemas.

It can run as a CLI MCP proxy, or be embedded directly from Python, TypeScript, or Rust.

- **Documentation**: <https://atlassian-labs.github.io/mcp-compressor/>
- **Repository**: <https://github.com/atlassian-labs/mcp-compressor/>
- **Blog**: <https://www.atlassian.com/blog/developer/mcp-compression-preventing-tool-bloat-in-ai-agents/>

## Why mcp-compressor?

A powerful MCP server can expose dozens or hundreds of tools. Each tool has a name, description, input schema, and sometimes large nested JSON Schema details. Sending all of that to a model up front can waste thousands of tokens before the agent has done any useful work.

For example, large public MCP servers can spend thousands or tens of thousands of tokens on tool descriptions alone. That creates three problems:

- models spend context on tools they may never call,
- cost increases 
Read full README on GitHub →

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