by Lap-Platform · MCP Server · ★ 212
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Your agents are guessing at APIs. Give them the actual Agent-Native spec. 1500+ API's Ready To-Use skills, Compile any API spec into a lean, agent-native format. 10× smaller. OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman.
| Stars | 212 |
| Forks | 14 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 35.8/100 |
| Last Updated | 2026-03-26 |
| Created | 2026-02-08 |
| Platforms | claude-code, cli, mcp, python |
| Est. Tokens | ~490k |
Looking for a LAP alternative? If you're comparing LAP with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
One command gives AI agents instant codebase context. ~250 tokens replaces 50,000+ tokens of exploration. Auto
Open-source context engine that catches AI hallucinations and cuts your token bill 70–95%. The only AI helper
A unified CLI for discovering and invoking tools across OpenAPI, MCP, GraphQL, gRPC, and JSON-RPC
Search for the agentic web.
MCP Server for Facebook ADs Library - Get instant answers from FB's ad library
CTX - Context Runtime Engine for Coding Agents
Explore other popular mcp server tools:
LAP is Your agents are guessing at APIs. Give them the actual Agent-Native spec. 1500+ API's Ready To-Use skills, Compile any API spec into a lean, agent-native format. 10× smaller. OpenAPI, GraphQL, AsyncA. It is categorized as a MCP Server with 212 GitHub stars.
LAP is primarily written in Python. It covers topics such as agent-experience, ai, ai-agents.
You can find installation instructions and usage details in the LAP GitHub repository at github.com/Lap-Platform/LAP. The project has 212 stars and 14 forks, indicating an active community.
LAP is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to LAP on Agent Skills Hub include stacklit, entroly, uxc. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.