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ACP – Agent Context Protocol
The intent layer above MCP — 64–97% fewer tokens
ACP sits above MCP as an intent-resolution layer. Send intent, get back a scoped manifest: only relevant tools, auth injected server-side (never in context), execution ordering declared. Benchmark results (50 runs/scenario, tiktoken cl100k_base): • 373 → 111 tokens (-70%) for a standard query • 9,223 → 241 tokens (-97.4%) with 50 tools, 2 relevant • 1,431 → 359 tokens (-74.9%)
Built an open protocol layer that sits above MCP: instead of dumping every
tool's auth tokens + schemas into the agent's context, the agent sends intent
and gets back a scoped manifest — only relevant tools, auth injected
server-side, execution ordering declared.
Benchmark numbers (50 runs/scenario, tiktoken cl100k_base):
- Standard query: 373 → 111 tokens (-70%)
- 50 tools, 2 relevant: 9,223 → 241 tokens (-97.4%)
- Complex multi-step: 1,431 → 359 tokens (-74.9%)
Key points:
- Auth never enters agent context (injected server-side)
- Execution ordering is declared in the manifest, not left to the agent
- It's a protocol spec (CC BY 4.0), not a library — works with any framework
Go server + Python adapters for LangGraph, CrewAI, OpenAI Agents SDK.
go install github.com/Clawdlinux/ninevigil-...https://github.com/Clawdlinux/ni...
About ACP – Agent Context Protocol on Product Hunt
“The intent layer above MCP — 64–97% fewer tokens”
ACP – Agent Context Protocol was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #129 on the daily leaderboard. ACP sits above MCP as an intent-resolution layer. Send intent, get back a scoped manifest: only relevant tools, auth injected server-side (never in context), execution ordering declared. Benchmark results (50 runs/scenario, tiktoken cl100k_base): • 373 → 111 tokens (-70%) for a standard query • 9,223 → 241 tokens (-97.4%) with 50 tools, 2 relevant • 1,431 → 359 tokens (-74.9%)
On the analytics side, ACP – Agent Context Protocol competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how ACP – Agent Context Protocol performed against the three products that launched closest to it on the same day.
Who hunted ACP – Agent Context Protocol?
ACP – Agent Context Protocol was hunted by Shreyansh Sancheti. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ACP – Agent Context Protocol including community comment highlights and product details, visit the product overview.
Shreyansh Sancheti