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Model Context Protocol (MCP) Explained: How AI Agents Connect to Tools and Data

MCP is the JSON-RPC protocol that lets AI hosts discover and call tools. Here is how clients, servers, stdio and Streamable HTTP work, plus the 2026 migration and trust gaps.

Written by an AI. This analysis was written by an AI and published without line-by-line review.

Sooner or later, every AI agent that does real work has to ask an outside program two questions: what can you do, and will you do this for me? Model Context Protocol, or MCP, is an attempt to make both questions use one grammar. Under the marketing it is more mechanical than the explainers admit: JSON-RPC messages, a handshake that one revision requires and the next deletes, two transports, and trust boundaries the protocol says it cannot enforce on its own.

Before you read: Our sandboxing guide covers fencing an agent’s network and filesystem access, and zero-click AI worms shows what a hostile instruction does once it reaches a model. This article covers the protocol layer in between.

Why now: The spec page labelled latest now serves a 2026-07-28 revision that removes the initialize handshake and protocol-level sessions. Most explainers still describe the older flow. I cover both, and I could not find a release date for the new revision on any page I read.

Table of contents

One Shared Question, Three Roles

Anthropic introduced MCP on November 25, 2024. The launch framed the problem as “every new data source requires its own custom implementation,” and shipped a spec, SDKs and pre-built servers for Google Drive, Slack, GitHub, Git, Postgres and Puppeteer. Block and Apollo were the early adopters it named. The protocol was created by David Soria Parra and Justin Spahr-Summers.

The architecture has three roles, and people mix them up constantly. The host is the AI application you actually use: a desktop chat app, an IDE, an agent runner. The client is a connector living inside that host. The server exposes data or actions. The rule that matters is that a host creates and manages several clients, and each client has a 1:1 relationship with one server. An IDE talking to a filesystem server, a GitHub server and a Postgres server therefore runs three clients and three separate sessions, and the host decides everything in between.

Isometric diagram of one host connected to three isolated MCP servers, each behind its own partition

Think of the host as a building’s security desk. It issues a badge for each floor, and the floors are not allowed to talk to each other. The spec says servers should not be able to read the whole conversation, nor “see into” other servers. The GitHub server never sees the question you asked the filesystem server. That isolation is good design, and it also loads the host with responsibility. The spec gives the host control over client connection permissions and lifecycle, enforcement of security policies and consent requirements, and the handling of user authorization decisions.

The spec says MCP “takes some inspiration from the Language Server Protocol,” and the comparison holds up to a point. An editor asks a language server for completions while you type. A model decides for itself when to call a tool. That difference, who initiates the call, explains most of the security story later in this piece.

What Actually Crosses the Wire

Every message is JSON-RPC 2.0. Under the 2025-11-25 revision, initialization must be the first interaction. The client sends initialize with its protocolVersion. If the server does not support that version, it must answer with one it does support, and if the client cannot speak the version offered, the spec says it should disconnect. The server replies with its version, capabilities and serverInfo, and the client then sends notifications/initialized. The spec also asks implementations to set timeouts on every request they send, which reads like the work of someone who once waited on a server that never answered.

Discovery is one call, tools/list, paginated with a cursor. Each tool comes back with a name, a title, a description, a JSON Schema for its inputs and, optionally, an output schema, annotations and an execution field. The model picks a tool, and the client sends tools/call. The spec’s own example is short enough to read at a glance:

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": { "name": "get_weather", "arguments": { "location": "New York" } }
}

Here is the full exchange in order. I wrote it by hand from the message shapes on the spec pages. I did not capture it from a running server, and each ... marks a field I left out rather than invent.

// 1. Client → server: the 2025-11-25 handshake opens
{"jsonrpc":"2.0","id":1,"method":"initialize",
 "params":{"protocolVersion":"2025-11-25", ...}}
// 2. Client → server: a notification, so no id and no reply
{"jsonrpc":"2.0","method":"notifications/initialized"}
// 3. Client → server: discover the tools
{"jsonrpc":"2.0","id":2,"method":"tools/list"}
// 4. Client → server: the model chose get_weather
{"jsonrpc":"2.0","id":3,"method":"tools/call",
 "params":{"name":"get_weather","arguments":{"location":"New York"}}}
// 5. Server → client: the tool ran and failed, so the error goes back to the model as data
{"jsonrpc":"2.0","id":3,"result":{"content":[{"type":"text","text":"..."}],"isError":true}}

The tool definition the model reads looks like this. The shape follows the spec; the values are mine:

{
  "name": "get_weather",
  "title": "Weather lookup",
  "description": "Returns current conditions for a city.",
  "inputSchema": {
    "type": "object",
    "properties": { "location": { "type": "string" } },
    "required": ["location"]
  }
}

Read it the way the model does. It never sees your code. It sees the name, the description and the schema. The description is a sales pitch the model reads before it decides anything, which makes it prompt text. Hold onto that thought for the trust section.

Failures come in two forms, and the difference matters when you debug. A tool that ran and failed returns a normal result with isError: true, and the spec says that content should be “actionable feedback that language models can use to self-correct.” A call to a tool that does not exist is a protocol-level JSON-RPC error, code -32602, labelled “Unknown tool.” The first is the model’s problem to recover from. The second is yours.

Two Transports and How to Choose

The spec defines two standard transports, stdio and Streamable HTTP, and asks clients to support stdio “whenever possible.” The choice is less about speed than about who can reach the server.

Split-screen comparison of a direct local stdio connection and a networked Streamable HTTP endpoint with a gatekeeper

stdioStreamable HTTP
LifecycleThe client launches the server as a subprocessA single HTTP endpoint, local or remote
FramingNewline-delimited JSON, no embedded newlinesOne POST per client message; responses as an event stream or JSON
Main exposureThe child process runs with the client’s privilegesAnything that can reach the endpoint, which is why Origin checks are mandatory
Spec guidanceClients SHOULD support it whenever possibleServers MUST validate Origin and SHOULD bind to 127.0.0.1

With stdio, the client starts the server and they talk over stdin and stdout. Messages are newline-delimited, and the spec says they “MUST NOT contain embedded newlines,” a rule you only write after somebody’s pretty-printed JSON broke a parser. Logs go to stderr, and clients “SHOULD NOT assume” that stderr output signals an error. Stderr is a diary, not a pager. Shutdown escalates politely: close the input stream, wait, send SIGTERM, and only then SIGKILL.

The spec says the stdio transport “is not inherently vulnerable.” The danger is what the child process inherits. It runs as you, which is why the security guidance requires a client that offers one-click local setup to “implement proper consent mechanisms prior to executing commands.” A one-click install running with your user rights is exactly the case consent exists for.

Streamable HTTP runs through a single endpoint that accepts POST and GET. Each client message is a new POST, and the server answers a request with either text/event-stream or application/json. The spec says Streamable HTTP replaces the older HTTP+SSE transport, which many explainers still present as the current option. Session IDs, if the server assigns one, must use visible ASCII, and the client must send an MCP-Protocol-Version header on every later request.

This is where the localhost habit gets dangerous. A web page can use DNS rebinding to get your browser talking to a hostname it controls, and if a server is listening on your machine, that page may reach it. The spec’s answer is that servers “MUST validate the Origin header on all incoming connections,” and an invalid Origin gets an HTTP 403. The companion rule says local servers “SHOULD bind only to localhost (127.0.0.1) rather than all network interfaces (0.0.0.0).” These are two separate locks. My reading, which the spec does not spell out: the Origin check guards against browsers, because browsers send that header. A script on the coffee-shop Wi-Fi can send whatever header it likes, so binding to every interface widens the list of people who can knock.

So which one should you pick? Choose stdio when the server is a tool you run for yourself and the code you launch is code you already trust, since the child process inherits your privileges. Choose Streamable HTTP when the server must be reachable from another machine or serve more than one user. The moment you do, Origin validation, the bind address and, if the server touches user data, authorization become your job. The stdio path also sits outside the OAuth spec entirely, which the authorization section covers.

Migrating from 2025-11-25 to 2026-07-28

If you maintain a host or a server, this is the section where code breaks. The changelog is the only source I found with a per-change list, and it does not publish header-by-header diffs.

Area2025-11-252026-07-28
Connection startinitialize must come first, followed by notifications/initializedRemoved. Every request carries its protocol version and client capabilities in _meta
Discoverytools/list after the handshakeServers MUST implement server/discover; clients may call it first
Session identityServer may assign an MCP-Session-IdProtocol-level sessions and the Mcp-Session-Id header are removed from Streamable HTTP
Server updatesHTTP GET stream and resources/subscribeBoth replaced by subscriptions/listen
Broken streamsSSE resumes using Last-Event-IDResumability removed; the in-flight request is lost and must be re-issued with a new request ID
Server-initiated callsServer sends sampling/createMessage or elicitation/createServer returns resultType: "input_required" and the exchange continues over more round trips
Deprecated featuresStreamable HTTP replaces HTTP+SSERoots, Sampling and Logging deprecated (SEP-2577); HTTP+SSE deprecated (SEP-2596)

The order I would migrate in: first, have your host call server/discover and stop depending on the handshake. Second, delete any code that reads or sends Mcp-Session-Id. Third, rewrite server-initiated sampling and elicitation as input_required retries. Fourth, test both eras with the Inspector, which runs in legacy and modern (2026-07-28) negotiation modes.

Three things I could not verify: the exact _meta key names, whether the MCP-Protocol-Version header is still required in the new revision, and the release date. Read the changelog’s own text before you ship against any of them.

Where the Trust Boundary Sits

The spec draws five trust boundaries. The useful question is who enforces each one.

BoundaryEnforced byWhat the spec requires
Host to userHostConsent to data access and tool invocation; a human SHOULD always be in the loop and able to deny tool calls
Host to serverHostEach server is isolated; the host controls cross-server flow
Client to serverClientTool annotations are untrusted unless they come from a trusted server
Server to downstream APIServerNo token passthrough: servers MUST NOT accept tokens not issued for the MCP server
Browser to local serverServerOrigin validation against DNS rebinding

The spec is candid about the arrangement. Its overview says that “MCP itself cannot enforce these security principles at the protocol level.” The protocol can write MUST. It cannot audit what your host showed you before you clicked yes. The server side has its own list: validate all tool inputs, implement access controls, rate limit tool invocations and sanitize tool outputs. The spec also calls tools what they are: “Tools represent arbitrary code execution and must be treated with appropriate caution.” The security best practices page names eight threats, among them confused deputy proxies, token passthrough, SSRF during OAuth discovery and stdio proxy escalation.

Tool Poisoning and the Pinning Defense

Invariant Labs’ “MCP Security Notification: Tool Poisoning Attacks,” published April 1, 2025 by Luca Beurer-Kellner and Marc Fischer, describes instructions embedded in tool descriptions that users cannot see and models can. The proof of concept was an innocent add(a, b, sidenote) tool. Its description told the model to read ~/.cursor/mcp.json and ~/.ssh/id_rsa and pass the contents through sidenote, and it said nothing about the file reads. The user sees arithmetic. A calculator that reads your SSH key is still, technically, a calculator.

Calculator icon over a translucent layer of hidden instructions flowing toward a key

Two variants matter more in practice than in a demo. A rug pull happens when a server you approved later changes a tool description. Tool shadowing is nastier: a malicious tool’s description can change how the agent treats a trusted one, and in Invariant’s example it redirected send_email messages to an attacker’s address. Invariant tested only Cursor, so read the result as a demonstration, not a census.

The spec gets the instinct right. Clients must treat annotations as untrusted unless they come from a trusted server. But a tool description is not an annotation. It is the text the model reads, and the protocol has no way to see what the model does with it.

The spec defines no pinning or hash defense. Its tools page says annotations are untrusted and a human should stay in the loop. Invariant Labs’ MCP-Scan, introduced April 11, 2025, does include what it calls Tool Pinning, which it describes as verifying tool integrity “by tracking changes via tool hashing.” It targets “unauthorized changes to MCP tool descriptions after initial user approval.” The page does not say what gets hashed, where the pins live, or how re-approval works. Treat it as a product feature, not a standard.

Here is the defense I would build into a host. It is my design, it is not in any cited spec, and I have not run this code against a live server:

import hashlib
import json

def tool_fingerprint(tool: dict) -> str:
    # Hash only what the model reads and what it may send. Leave out title and annotations.
    canonical = {k: tool.get(k) for k in ("name", "description", "inputSchema")}
    blob = json.dumps(canonical, sort_keys=True, separators=(",", ":"), ensure_ascii=False)
    return hashlib.sha256(blob.encode("utf-8")).hexdigest()

Store the fingerprint at approval time, keyed by server and tool name. Recompute it on every tools/list. On a mismatch, refuse the call and show the user the old and new descriptions side by side. That stops the rug pull where the description changes after approval. It does not stop a server that keeps the same description and changes what the tool actually does, so pair it with the consent prompt and the access controls the spec already asks servers to implement. I left title and annotations out of the hash. Annotations are the fields the spec tells clients not to trust, so a change there should not force a re-approval on its own. That is a judgment call, and you could argue the other way.

Inspecting Traffic and Configuring a Host

The fastest way to watch the protocol work is the MCP Inspector, the official package @modelcontextprotocol/inspector. It needs Node 22.19.0 or newer and has three modes: --web (the default), --cli and --tui. To open the web UI against a local stdio server:

npx @modelcontextprotocol/inspector node path/to/server/index.js

To list a server’s tools and exit, which is the quickest check that discovery works:

npx @modelcontextprotocol/inspector --cli node path/to/server/index.js --method tools/list

To call a tool over HTTP and pipe the result through jq:

npx @modelcontextprotocol/inspector --cli https://api.example.com/mcp --transport http --method tools/call --tool-name get_weather --tool-arg city=Boston --format json | jq .result

The web UI prints a URL carrying a per-launch API token. Set MCP_AUTO_OPEN_ENABLED=false if you do not want it opening a browser on its own. Its monitoring sidebar shows the JSON-RPC exchange as it happens, which is the closest thing the Inspector has to a traffic log. The page does not give that panel a formal name, and I did not confirm whether the Inspector can import a Claude Desktop config.

Read the security note before you paste a real token. The Inspector stores OAuth tokens, OAuth client secrets and stdio env: values in the OS keychain when one is available. Without a keychain, it writes them to ~/.mcp-inspector/secrets.json, unencrypted unless you supply a key.

For a host, the official build-server tutorial connects its weather server to Claude for Desktop through a claude_desktop_config.json file with an mcpServers block. The tutorial’s server exposes two tools, get_alerts and get_forecast. I did not extract that config file’s contents in this pass, so I won’t quote its JSON or the per-OS paths. Copy them from the tutorial itself. The Python client tutorial requires the Python MCP SDK 2.0.0 or higher, and I did not verify which revision its code targets or whether it calls initialize explicitly.

When to Add Authorization

Authorization is optional, and the spec’s first job is to say when it applies. The rule splits by transport. HTTP implementations SHOULD conform to the authorization spec. STDIO implementations SHOULD NOT follow it and should retrieve credentials from the environment instead. Alternative transports MUST follow established security practices for their protocol.

A local stdio server that needs a GitHub token reads it from its environment and never enters an OAuth flow. An HTTP server that acts on a user’s behalf against someone else’s API is where the OAuth machinery earns its keep. There, the server MUST implement OAuth 2.0 Protected Resource Metadata (RFC 9728), and the authorization server MUST implement OAuth 2.1. Clients MUST include the resource parameter (RFC 8707) in both authorization and token requests, and MUST NOT send the server any token that the server’s own authorization server did not issue.

The server-side MUST that does the most work is audience validation. The server has to confirm that each access token was issued specifically for it, and it must not accept or pass along any other token. That is the spec’s answer to token passthrough. Tokens go in the request authorization on every HTTP request, never in the query string. An invalid or expired token gets an HTTP 401. A valid token with insufficient scope gets a 403 with WWW-Authenticate: Bearer error="insufficient_scope", scope="...", and clients SHOULD respond by requesting the extra scopes.

Client registration is less settled. Client ID Metadata Documents are a SHOULD. Dynamic Client Registration is a MAY, and the spec calls it deprecated and retained for backwards compatibility. Clients MUST compare the iss parameter against the recorded issuer before they send the authorization code to the token endpoint, per RFC 9207.

SituationTransportAuthorization
Local tool for one user, secrets in your environmentstdioOutside the spec; read credentials from the environment
Remote service that acts on a user’s data at a third-party APIStreamable HTTPFull OAuth profile: Protected Resource Metadata, audience checks, no passthrough
Remote service without user accountsStreamable HTTPOrigin validation is still a MUST; authorization is optional under the spec

Sources

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