Everything MCP
Free forevermodelcontextprotocol · Dev Tools
20k installs
Everything MCP is the reference implementation of the Model Context Protocol, purpose-built to demonstrate and validate every core capability the spec defines: tools, resources, prompts, and sampling. If you're building an MCP client, testing a new agent integration, or simply trying to understand what a fully-featured MCP server can expose to an AI model, this is the canonical example to point at. Rather than guessing how a protocol feature behaves in practice, developers can connect to Everything MCP and see live, working demonstrations of resource subscriptions, prompt templates, tool invocation patterns, and sampling requests — all in one place.
For teams evaluating whether to invest in MCP-based architecture, Everything MCP functions as a sandbox and compliance checker. It's commonly used to smoke-test new AI agent frameworks (Claude, GPT, Gemini, or any other model-agnostic client) against the full breadth of the protocol before wiring up production data sources. QA and platform engineers rely on it to confirm that a given MCP client correctly handles edge cases like nested resources, dynamic prompt arguments, and sampling callbacks — the kind of detail that's easy to miss when only testing against a single-purpose server like a filesystem or database connector.
On BusinessMCP.com, Everything MCP slots naturally into the developer-tools layer of a unified MCP + business-intelligence stack. Rather than standing up a separate testing environment, you can connect Everything MCP once through your hosted MCP endpoint at /api/mcp (secured with a Bearer mcph_* key) and let any AI agent probe protocol behavior alongside your real production servers — filesystem access, GitHub, databases, ad platforms, and more — all surfaced through the same managed dashboard. This is especially useful during onboarding: before granting an agent access to sensitive company data sources, run it against the Everything MCP reference server to confirm it correctly negotiates tools, resources, and prompts, then flip it over to your live integrations with confidence.
Because BusinessMCP is cookieless and GDPR-friendly by design, using Everything MCP for protocol testing and developer education carries no privacy overhead — it's a clean, side-effect-free way to validate agent behavior without touching customer data. Engineering teams building custom MCP servers for internal tools often keep an instance of Everything MCP running as a living spec reference, comparing their own implementation's tool schemas and sampling responses against the canonical example. Combined with BusinessMCP's business-intelligence dashboard, you get visibility into which agents are calling which protocol features, making it easier to debug integration issues across Claude, GPT, and Gemini clients from a single pane of glass.
Whether you're a solo developer prototyping your first MCP client, a platform team writing conformance tests, or an enterprise standardizing on MCP as the interface layer between AI agents and business systems, Everything MCP gives you a dependable, spec-accurate reference point. Host it alongside your other MCP servers on BusinessMCP.com and it becomes part of the same unified endpoint, the same auth model, and the same dashboard — no separate infrastructure required for testing versus production.
Just say it in a thread
No configs, no docs. Once connected, these are the kinds of messages your agents act on.
"Return the set of demo resources available on the server, including static and dynamic examples — and give me the highlights."
"Fetch the content of a specified reference resource by uri for me, then post a summary in the thread."
"Invoke one of the reference tools with sample arguments to inspect response formatting and flag anything that needs my approval."
What teams use it for
- Smoke-test a new AI agent framework (Claude, GPT, Gemini) against the full MCP spec before connecting production data sources
- Validate that a custom-built MCP client correctly handles resource subscriptions, dynamic prompts, and sampling callbacks
- Onboard new engineers to MCP concepts using live, working examples of every protocol feature
- Benchmark a company's in-house MCP server implementation against the canonical reference for spec accuracy
- Debug agent integration issues by isolating protocol-level behavior from business-data complexity
Agent-callable tools
list_resources
Return the set of demo resources available on the server, including static and dynamic examples.
read_resource
Fetch the content of a specified reference resource by URI.
call_tool
Invoke one of the reference tools with sample arguments to inspect response formatting.
list_prompts
List all example prompt templates exposed by the server.
get_prompt
Retrieve a specific prompt template populated with example arguments.
request_sampling
Trigger a sampling request to demonstrate model-generated completions within the protocol flow.
subscribe_resource
Subscribe to updates on a demo resource to test change-notification handling.
echo_test
Send a simple echo payload to verify basic tool invocation and response round-tripping.
Your data stays yours
Credentials live in your vault. We route requests — we never store, log, or train on your data.
Works with every AI
Connect once — portable across Claude, GPT, Gemini, and every local agent you run.
Pairs well with
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Frequently asked questions
What makes Everything MCP different from a typical connector like GitHub or Filesystem MCP?
Those servers expose real-world tools and data; Everything MCP instead demonstrates every protocol feature — tools, resources, prompts, and sampling — in a controlled reference environment for testing and learning.
Can I run Everything MCP alongside my company's production MCP servers on BusinessMCP?
Yes — it connects through the same hosted /api/mcp endpoint and Bearer mcph_* key as your other servers, so any AI agent can reach it and your live integrations from one unified connection.
Is Everything MCP suitable for non-technical users?
It's primarily aimed at developers and QA engineers validating agent or client behavior; business users typically won't interact with it directly but benefit from the reliability it helps ensure.
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