The open standard turning a fragmented AI integration landscape into a coherent, vendor-neutral ecosystem.
The Model Context Protocol is an open standard introduced by Anthropic in November 2024 for connecting AI models to the tools, data sources, and services they need to operate in production environments.
Before MCP, every integration between a language model and an external system required custom engineering work. Each tool needed its own connector. Teams shipping AI features spent more time on plumbing than on the features themselves. Anthropic described this as the N×M integration problem: ten applications connecting to one hundred tools required up to one thousand custom integrations.
MCP solves this by defining a single, standardized interface through which any AI model can communicate with any compatible service. A tool built once for MCP works across every model and platform that supports the protocol.
The shift to MCP is widely viewed as one of the most consequential architectural changes in applied AI since the arrival of large language models themselves.
Universal standard
One interface connects any AI model to any compatible service - no custom connectors, no per-tool maintenance.
Linear scaling
Integration effort no longer grows quadratically. Ten apps, one hundred tools - no longer one thousand custom integrations.
Ecosystem coherence
Compared to HTTP for the web and USB-C for hardware - MCP turns a fragmented landscape into coherent infrastructure.
Vendor-neutral
Governed under the Linux Foundation with backing from Google, Microsoft, AWS, Cloudflare, and Bloomberg.
Production-ready
Already deployed across hundreds of Fortune 500 companies within twelve months of launch.
Industry consensus
Anthropic, OpenAI, Google, Microsoft, and GitHub - the full stack behind a single open standard.
Within twelve months, MCP grew from a launch announcement to the fastest-adopted AI infrastructure standard in history. Boston Consulting Group described it as “a deceptively simple idea with outsized implications.”
Jensen Huang of NVIDIA stated in late 2025 that the work on MCP has completely revolutionized the AI landscape. Integration complexity for enterprise AI now rises linearly rather than quadratically.
MCP launched by Anthropic
Open standard introduced for connecting AI models to tools, data sources, and services.
OpenAI commits
OpenAI announced full MCP support across its products.
Microsoft & GitHub join
Both joined the protocol steering committee at Build 2025.
Google DeepMind follows
Google DeepMind adopted MCP, cementing cross-industry consensus.
Linux Foundation governance
Anthropic donated MCP to the Agentic AI Foundation - vendor-neutral, open infrastructure.
Every AI application built on MCP needs services to call. The companies building those services are positioned to capture a structural share of the AI infrastructure stack.
Each tool in the SOMA marketplace is exposed as a fully MCP-compatible service, accessible to any model or agent through the standard protocol. The first tool, SOMARIZER, delivers context compression. Additional tools targeting other layers of the AI infrastructure stack are in active development.
As MCP adoption continues to accelerate, the demand for high-quality services running behind the protocol grows with it. SOMA is designed to meet that demand from the start - with an architecture that produces continuously improving services and a platform engineered to scale across multiple categories of infrastructure.
MCP-native from day one
Every SOMA service exposes a standard MCP interface. No custom connectors. No per-model configuration.
Open competition
Each service operates as a competition between independent providers. The best implementation always wins and is served automatically.
A platform built to scale
SOMARIZER is the first service. More tools targeting distinct layers of AI infrastructure are in active development.
SOMA is a token compression layer for AI agents and LLM pipelines. It reduces the number of tokens sent to the model - cutting inference costs without degrading output quality. SOMA specializes in two compression types: Context Compression, which strips redundancy from long inputs before they reach the model, and CoT Compression, which condenses an agent's accumulated session history so multi-step workflows stop resending tokens the model doesn't need.
Context Compression removes redundancy from long prompts, documents, and RAG inputs before they reach the model. The model sees a shorter, semantically equivalent input - which means fewer tokens billed and more usable space inside the context window. For high-volume AI workloads, the savings compound with every request.
Agents accumulate context as they work: every reasoning step, tool call, file read, and result gets appended to the session history - and resent to the model on every subsequent call. CoT Compression intercepts that session log before each call, scores each unit by relevance to the current task, removes redundancy and noise, and reconstructs a compact prompt that retains the state the model actually needs. The agent keeps its full working memory; the model stops paying for the parts that don't matter.
SOMA runs as a decentralized competitive network. Contributors submit compression implementations that are continuously benchmarked on both compression ratio and output fidelity. Only implementations that reduce tokens while maintaining quality earn rewards - so the network self-selects for compression that actually works.
SOMA runs as a proxy between your agent and its LLM provider. Integration is a single command: point your agent at the SOMA endpoint instead of calling the provider directly. SOMA compresses the context and forwards the request to the model of your choice - the response flows back unchanged. No infrastructure changes, no model retraining, no changes to your agent's logic. Get an API key on the SOMA Access page and you can be compressing on the next request.