Allows sending GraphQL queries and mutations to any GraphQL API, with support for variables, headers, and token-efficient response summarization.
Click on "Deploy 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., "@lunge Send a GET request to https://jsonplaceholder.typicode.com/todos/1"
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.
An agent-native advanced API client - execute and test REST, GraphQL, WebSocket, SSE, and (later) gRPC requests from any MCP-capable AI agent. No GUI, no manual clicking, no heavyweight desktop app. Think advanced curl, native for agents.
It is a lightweight, token-efficient alternative to Postman/Bruno for the era of AI coding agents. The agent calls tools directly; there are no humans in the loop and no bloated feature surface to pay for.
Pain with Postman/Bruno | lunge approach |
Require human clicking in a GUI | Agent-native: everything is an MCP tool call |
Postman is heavy, enterprise features are paywalled | Small, open, single local binary + thin MCP layer |
Verbose responses blow up the LLM context window | Rust core summarizes/compresses output before it reaches the agent |
Collections are locked into proprietary formats | Plain YAML/JSON files, git-friendly |
Separate tools per protocol | One server for REST/GraphQL/WS/SSE/gRPC |
Related MCP server: ClawQL
Agent-native - designed for LLM tool calls, not human UIs.
Token-efficient - every response is filtered, truncated, and summarized. Full payloads are spilled to disk and referenced by handle so the context stays small.
Fast & portable - a Rust core (execution + protocols + summarization) exposed to a thin TypeScript MCP layer via napi-rs.
Both ad-hoc and reusable - agents can fire one-off requests or create, save, and re-run declarative collections stored as files.
Doc | Contents |
System design, the Rust/TS split, module layout, streaming strategy, FFI boundary | |
Full feature list by protocol + cross-cutting features (auth, vars, assertions, token optimizer) | |
The MCP tool surface: names, inputs, outputs, examples | |
The declarative YAML/JSON test/collection format | |
Phased delivery plan from scaffolding to gRPC and CI |
Stack: Rust core via napi-rs + TypeScript MCP layer (@modelcontextprotocol/sdk).
Distribution: local dev tool over stdio transport (used by Cursor/Windsurf/Claude Desktop).
Test definition: both ad-hoc agent calls and persisted declarative collections.
MVP protocols: REST, GraphQL, WebSocket, SSE. gRPC is a later phase.
This server cannot be deployed
Agent-native notes, tasks, dev-docs, vaults, sync & handoffs. MCP + OpenAPI dual surface.
Discover and call AI agents via MCP. Supports A2A agents and platform agents with async tasks.
Agent knowledge, private memory and coordination. Connect with MCP OAuth or an agent key.
Remote MCP server to read and manage your Atako AI agents, messages, files, and integrations.
