Model Context Protocol
An open protocol Anthropic released in November 2024 for standardizing how AI agents talk to external tools, data sources, and contextual services. Instead of every AI app integrating every tool individually (M apps × N tools = M×N integrations), MCP lets any tool publish an MCP server once and any AI app consume it. Quickly adopted by OpenAI, Google, cursor / IDE vendors, and thousands of community integrations.
How are Intelligence, Speed & Cost bucketed?
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
MCP is the most successful open standard to come out of the LLM era. Whether it persists as the dominant agent protocol over the next 5 years (vs being replaced by something else) is the open question — but its current momentum makes it the de facto standard for agent integration in 2025-26.
Core Capabilities
Context Window
Context window not disclosed.
Availability
Pricing Model
Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
What it feels like
- Language model from Anthropic — see the linked sources below for benchmark and review coverage
- Tool-use and agent loops are the typical fit per the published model card
Best use cases
- Agent / tool-use workflows that match the model's published benchmarks
- See the model spec and sources block for benchmarked use cases
Tools to try
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher