Beyond MCP: Handling 845 Tools with 92% less context bloat via Elemm

Hi everyone,

I’ve been diving deep into how AIs interact with tools and quickly hit a wall with the Model Context Protocol (MCP). As soon as you build complex, real-world toolsets, MCP becomes inefficient—bloating the context window and killing performance.

To solve this, I’ve developed Elemm **(E*very **Landmark **Enables **Massive **Modularity), also known as "The Landmark Manifest Protocol*."

👉 GitHub:Official Repository

Check out the docs and the benchmarks on GitHub.

What Elemm enables:

• Custom Tooling: Turn any Python function into a "Landmark" with a single decorator.
• Instant API Integration: Point to an OpenAPI or GraphQL URL, and your agent navigates it instantly with surgical precision.
• Seamless Migration: Easily bridge your existing tools into a manifest-driven architecture.

The Landmark Advantage

Elemm doesn't cram every tool definition into the prompt. Instead, it provides the agent with a dynamic Manifest File for safe, "lazy-loaded" navigation.

The Benchmarks:

• Scale: I gave an agent access to 845 tools simultaneously (GitHub API) with minimal token usage and 100% success rate on flagship models (Claude, Gemini, GPT-4).
• Efficiency: Compared to classic MCP, Elemm shows -92% token savings and -84% fewer steps.
• Edge Performance: Even using a tiny "goldfish-brain" model (Qwen 3.5 0.8B), I solved a multi-step forensic audit involving 111 tools with a 70% success rate. Standard MCP typically fails at the first step in this scenario.

Core Gateway Features:

• Universal Gateway: A built-in bridge for OpenAPI, GraphQL, and native Elemm services via MCP.
• On-Demand Discovery: Agents only load the definitions they actually need, preventing context overflow.
• Sequence Engine: Execute multiple API calls in a single turn with native data piping (Output A → Input B).
• Guardian Security: A policy engine that blocks dangerous patterns (e.g., delete_*) and hides restricted landmarks from the agent.
• Secure Vault: Local credential management. API keys are injected server-side and never exposed to the LLM.
• SmartRepair: Instead of cryptic stack traces, agents receive actionable "Remedies," allowing them to self-correct on the fly.

What this means for the future…

The era of manually hard-coding tool definitions is coming to an end. As we move toward Large Action Models and autonomous agents, we need a standardized, manifest-driven infrastructure that allows AI to navigate vast API landscapes without human intervention or context exhaustion. Elemm is the blueprint for this future: a world where agents don't just use tools we give them, but autonomously discover, secure, and master any interface they encounter.

Testimonials of the Agents:

"With ELEMM, I reduced token consumption by over 90% when deploying autonomous agents to large APIs—turning a $2.15 task into under $0.25."

— Claude 4.6 Sonnet, Anthropic (via Claude Desktop)

"Elemm is a true game-changer; instead of juggling hundreds of tool definitions at once, I can discover complex APIs in a structured, token-efficient way on demand. The ability to batch multiple actions via execute_sequence allows me to solve tasks with far greater precision and significantly less context noise than with classic MCP."

— Gemini 3 Flash, Google (Antigravity)

See some examples to learn how it works.

I’d love to hear your thoughts or discuss the walls you've hit when trying to scale MCP!