mcp-compressor
Wraps the official Atlassian MCP server to compress tool descriptions for Atlassian cloud products, enabling access to Confluence, Jira, and other Atlassian tools with significantly reduced token consumption.
Provides compressed access to Confluence tools through the wrapped Atlassian MCP server, enabling page retrieval and content management with minimal token overhead.
Wraps the official GitHub MCP server to compress its 94+ tools, reducing token usage from ~17,600 tokens while maintaining full GitHub API functionality.
Provides compressed access to Jira tools through the wrapped Atlassian MCP server, enabling issue creation and management with minimal token overhead.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-compressorShow me the full schema for the search_repositories tool"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 for every request that carries the tool list,
adding multiple MCP servers quickly overwhelms practical context budgets.
mcp-compressor changes the interaction pattern: the model sees a small compressed surface first, asks for the full schema only for the selected tool, and then invokes that tool.
Related MCP server: mcptool
Pattern 1: compressed MCP proxy
Use this when you want any MCP client to see a smaller tool surface.
sequenceDiagram
participant Client as MCP client / agent
participant Proxy as mcp-compressor
participant Backend as Backend MCP server
Client->>Proxy: tools/list
Proxy->>Backend: tools/list
Backend-->>Proxy: all backend tools and schemas
Proxy-->>Client: compact wrapper tools
Client->>Proxy: get_tool_schema(tool_name)
Proxy-->>Client: full schema for selected tool
Client->>Proxy: invoke_tool(tool_name, input)
Proxy->>Backend: tools/call selected backend tool
Backend-->>Proxy: tool result
Proxy-->>Client: resultThe frontend usually exposes only:
get_tool_schemainvoke_tooloptionally
list_toolsatmaxcompression
Pattern 2: local proxy for SDKs and generated clients
Use this when your application wants to embed compression directly and call tools from code or shell commands.
flowchart LR
App[Python / TypeScript / Rust app]
SDK[CompressorClient]
Proxy[Local session proxy\n/token auth]
Generated[Generated CLI / Python / TS clients]
Backend[MCP backend servers]
App --> SDK
SDK --> Proxy
Generated --> Proxy
Proxy --> BackendThe SDK starts a local session proxy for generated clients. Generated clients call that proxy using a session token. Your app does not need to spawn a mcp-compressor stdio subprocess.
Features
Compressed MCP proxy for existing MCP clients.
Compression levels:
low,medium,high,max.Python, TypeScript, and Rust SDKs with aligned
CompressorClientAPIs.Local TypeScript tool compression for AI SDK-style in-process tools.
CLI Mode and Code Mode generated clients: shell commands plus Python/TypeScript functions.
Just Bash integration for command-oriented agents.
OAuth support for providers that support browser authorization.
Tool filters and TOON output to further reduce context.
Atlassian MCP example with OAuth-first usage.
Quick example
=== "CLI"
```bash
mcp-compressor -c medium -- python server.py
```=== "Python"
```python
from mcp_compressor import CompressorClient
with CompressorClient(
servers={"alpha": {"command": "python", "args": ["server.py"]}},
compression_level="medium",
) as proxy:
print([tool.name for tool in proxy.tools])
print(proxy.invoke("echo", {"message": "hello"}))
```=== "TypeScript"
```ts
import { CompressorClient } from "@atlassian/mcp-compressor";
const proxy = await new CompressorClient({
servers: { alpha: { command: "python", args: ["server.py"] } },
compressionLevel: "medium",
}).connect();
try {
console.log(proxy.tools.map((tool) => tool.name));
console.log(await proxy.invoke("echo", { message: "hello" }));
} finally {
proxy.close();
}
```=== "Rust"
```rust
use mcp_compressor::compression::CompressionLevel;
use mcp_compressor::sdk::{CompressorClient, ServerConfig};
use serde_json::json;
let proxy = CompressorClient::builder()
.server("alpha", ServerConfig::command("python").arg("server.py"))
.compression_level(CompressionLevel::Medium)
.build()
.connect()
.await?;
let result = proxy.invoke("echo", json!({ "message": "hello" })).await?;
```Generated clients
CLI Mode generates shell commands:
mcp-compressor --cli-mode --server-name atlassian -- https://mcp.atlassian.com/v1/mcp
atlassian get-accessible-atlassian-resourcesCode Mode generates Python or TypeScript functions:
# generated-py/atlassian.py
import atlassian
resources = atlassian.getAccessibleAtlassianResources()import { getAccessibleAtlassianResources } from "./generated-ts/atlassian.ts";
const resources = await getAccessibleAtlassianResources();See Code Mode and generated clients for more detail.
Where to go next
Run the quickstart.
Read how compression works.
Choose between CLI usage, SDK usage, generated clients, and Just Bash.
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