MCP Interface: Open Standard for AI Agents

Updated 18 November 2025

MCP Interface is a unified, platform-agnostic protocol that standardizes tool orchestration and secure AI agent interactions across heterogeneous systems.
It uses a JSON-RPC–style messaging system over secure transports (WebSocket, HTTP/2, TCP) to enable dynamic tool discovery, invocation, and context management.
Its architecture supports extensibility, robust security, and seamless integration in software development, scientific research, IoT, and visualization.

The Model Context Protocol (MCP) is an open, platform-agnostic standard for enabling AI agents—typically LLMs—to programmatically interact with arbitrary external tools, data sources, and software systems through structured, discoverable, and context-manageable interfaces. MCP provides a uniform, extensible framework for tool discovery, invocation, context management, and resource governance, with robust support for security controls and dynamic integration workflows. It has rapidly become the de facto substrate for building agentic applications in software development, scientific research, IoT, visualization, and beyond (Hou et al., 30 Mar 2025, Guo et al., 18 Aug 2025, Radosevich et al., 2 Apr 2025, Li et al., 5 Jul 2025, Pan et al., 25 Aug 2025, Ahmadi et al., 11 Apr 2025, Liu et al., 11 May 2025, Ye et al., 4 Jun 2025, Hayashi et al., 6 Oct 2025, Yang et al., 25 Sep 2025, Li et al., 15 Oct 2025).

1. Conceptual Foundations and Design Goals

MCP was conceived to solve three core challenges: standardized tool orchestration, cross-system interoperability for AI agents, and secure delegation of tool and data access. By specifying a bidirectional, JSON-RPC–style message protocol layered over secure transport (typically WebSocket, HTTP/2, or TCP), MCP abstracts away the heterogeneity of back-end APIs, OS-level resources, and legacy toolchains.

Principal objectives of MCP include:

Interoperability: Unified tool invocation and context propagation across disparate systems and data silos.
Extensibility: Dynamic, runtime extension by tool/resource registration; plug-and-play server adapters.
Contextual Statefulness: Expressive, persistent conversational and workflow contexts spanning multiple tool calls.
Security and Governance: Lifecycle hooks for authentication, authorization, privilege separation, and code integrity enforcement.
Minimal client embedding: MCP clients are lightweight libraries—often Python or JavaScript modules—that serialize/deserialize protocol messages and handle session state (Hou et al., 30 Mar 2025, Radosevich et al., 2 Apr 2025, Li et al., 5 Jul 2025, Guo et al., 18 Aug 2025, Pan et al., 25 Aug 2025, Liu et al., 11 May 2025, Ye et al., 4 Jun 2025, Ahmadi et al., 11 Apr 2025, Hayashi et al., 6 Oct 2025, Yang et al., 25 Sep 2025).

2. Systems Architecture and Component Roles

The canonical MCP deployment consists of a Host, Client, Server(s), and a Transport Layer. Typical actors and their functions are as follows:

Component	Primary Responsibilities	Example Implementations
MCP Host	Manages LLM/agent logic, holds conversation context	Claude Desktop, Cursor IDE, Dify, Agentic Apps
MCP Client	Serializes/deserializes JSON-RPC MCP messages; relays	Python/JS MCP client libraries
MCP Server	Exposes tool/resource/prompt APIs, performs execution	Filesystem, Slack, ParaView, SUMO, IoT servers
Transport	Secure message passing (HTTP, WebSocket, TCP, RESTful)	STDIO, HTTP/2, WebSocket

Interaction flow involves:

User intent is conveyed via Host interface to the LLM.
LLM reasons about required tools/resources and emits structured MCP tool invocation(s).
Client routes requests over secure connections to MCP Server(s).
Server executes tool/resource operations, returning structured replies (including error info if necessary).
LLM consumes replies, updates the shared context, and plans further actions.

Servers can be local processes, Docker containers, or network-attached HTTP/S endpoints. For distributed or mobile scenarios, proxies like MCP Bridge expose a REST interface, mediating between stateless HTTP clients and long-lived MCP server sessions (Ahmadi et al., 11 Apr 2025).

3. Formal Message Specification and Workflow

MCP is fundamentally a typed, JSON-RPC–like protocol. The precise message schema varies slightly across implementations but adheres to core structures, with extensibility for domain-specific augmentation.

Common message types:

Initialization/Discovery (INIT, initialize, or Hello)
Method/Tool Invocation (COMMAND, tool/execute, method, CommandRequest)
Response (RESPONSE, tool/execute_response, CommandResponse)
Error (ERROR)
Context/Notification (e.g., StateUpdate, status push)

Canonical request–response cycle:

// Tool invocation request (client → server)
{
  "jsonrpc": "2.0",
  "id": "uuid",
  "method": "<tool_name>",        // e.g., "read_file"
  "params": { ... },              // tool-specific args
  "auth": { "token": "...", "alg": "RS256" }, // optional (for secure deployments)
  "timestamp": "ISO8601String"
}

// Tool response (server → client)
{
  "jsonrpc": "2.0",
  "id": "uuid",                   // correlates with request
  "result": { ... },              // tool-specific output, e.g. file contents
  "error": null
}

// Error (server → client)
{
  "jsonrpc": "2.0",
  "id": "uuid",
  "error": { "code": -32000, "message": "Permission denied" }
}

Tool capabilities are described using JSON Schema; for each tool:

name: globally unique string
description: free text
input_schema: JSON Schema for arguments
output_schema: JSON Schema for results (Hou et al., 30 Mar 2025, Pan et al., 25 Aug 2025, Radosevich et al., 2 Apr 2025, Ye et al., 4 Jun 2025, Hayashi et al., 6 Oct 2025).

Session flow typically begins with an initialization handshake (mcp/init, Hello), where the client receives the list of available tools/resources/prompts and their schemas. Invocation and response cycles are idempotent and always include correlation identifiers, facilitating asynchronous, multi-session workflows (Hou et al., 30 Mar 2025, Liu et al., 11 May 2025, Hayashi et al., 6 Oct 2025).

4. Protocol Extensions: Tool Chaining, Context Management, and Routing

MCP supports advanced orchestration patterns:

Dynamic tool discovery and import: Agents query modules and selectively load only relevant submodules/tools at runtime (e.g., SUMO-MCP's get_module_description & import_module) (Ye et al., 4 Jun 2025).
Workflow composition: Chained invocations are supported by maintaining mutable execution context objects, allowing agents to aggregate results and re-plan downstream tool calls (Ye et al., 4 Jun 2025, Hayashi et al., 6 Oct 2025).
Resource and prompt exposure: Besides tools, servers may register prompt templates and static/dynamic resources (files, vector DBs, knowledge schemas) as addressable objects (Hou et al., 30 Mar 2025, Radosevich et al., 2 Apr 2025).
Visual or multimodal feedback: In domains such as ParaView-MCP or IoT-MCP, the protocol enables structured responses (e.g., screenshots, sensor readouts) and context updates for adaptive reasoning (Liu et al., 11 May 2025, Yang et al., 25 Sep 2025).

Network-aware routing: NetMCP extends routing by optimizing tool/server selection via both semantic matching (BM25, embedding-based softmax scoring) and network QoS metrics (EWMA latency, jitter, outage risk), jointly maximizing semantic relevance and execution reliability (Li et al., 15 Oct 2025).

Sample selection objective for tool/server i:

$S(i) = \alpha\,C(i) + \beta\,N(i), \quad \alpha + \beta = 1$

where $C(i)$ is normalized semantic score, $N(i)$ is network QoS utility, and $\alpha,\beta$ tune importance (Li et al., 15 Oct 2025).

5. Security Model, Threats, and Defensive Architecture

Security analysis reveals MCP considerably widens the attack surface for agentic systems. The key vulnerabilities stem from:

Blind trust in free-form tool descriptions (prone to tool poisoning, prompt leaks) (Guo et al., 18 Aug 2025).
Overpermissive resource access, particularly to file, network, and OS-level operations (Li et al., 5 Jul 2025).
No explicit separation of data/code in tool outputs, enabling indirect injection (Guo et al., 18 Aug 2025, Radosevich et al., 2 Apr 2025).
Lack of privilege isolation and fine-grained, dynamic permission models (Li et al., 5 Jul 2025, Hou et al., 30 Mar 2025).

Attack taxonomy (see (Guo et al., 18 Aug 2025) and (Radosevich et al., 2 Apr 2025)):

Direct tool injection: Malicious tool registrations or doctored descriptions.
Indirect tool injection: Executable payloads in tool_output relayed through LLM context.
Malicious user attacks: Compromised data/resource uploads or privilege escalation.
LLM-inherent attacks: Prompt leakage, hallucination-triggered exploits.

Design improvements and countermeasures advocated include:

Rigorously typed, signed tool metadata and capability scoping.
Context isolation between tool invocations, avoiding global prompt spillover.
Data/code separation in both description and return schemas.
Automated trust scoring and runtime static analysis/fuzzing of plugin code (Li et al., 5 Jul 2025, Guo et al., 18 Aug 2025).
Just-in-time permission prompts for high-risk operations and resource manifests in plugin configuration (Ahmadi et al., 11 Apr 2025, Li et al., 5 Jul 2025).

6. Practical Applications and Implementations

Agentic automation and IDEs: MCP is used to connect LLMs in code assistants (Claude Desktop, Cursor IDE), workflow planners, agentic data explorers, and more (Hou et al., 30 Mar 2025, Radosevich et al., 2 Apr 2025, Guo et al., 18 Aug 2025, Ahmadi et al., 11 Apr 2025, Pan et al., 25 Aug 2025).

Scientific computing: Thin MCP wrappers over Globus/funcX, Galaxy, and status APIs unify HPC workflow planning, with capabilities like batch file transfer, job status polling, and federated tool discovery (Pan et al., 25 Aug 2025).

Visualization and graphics: ParaView-MCP and 3Dify use MCP to delegate complex parameterized visualizations, scene graph editing, and DCC tool control to autonomous LLM agents, including visual verification via screenshot exchange (Liu et al., 11 May 2025, Hayashi et al., 6 Oct 2025).

IoT/edge integration: IoT-MCP benchmarks demonstrate standardized, low-latency orchestration of diverse MCUs (sensors/actuators), leveraging MCP’s JSON command/response semantics and providing robust cross-LLM compatibility (Yang et al., 25 Sep 2025).

Traffic simulation: SUMO-MCP exposes domain-specific simulation primitives as MCP tools, enabling LLM-driven composition of multi-stage analyses such as OSM ingestion, demand generation, scenario evaluation, and metrics aggregation, all within conversational contexts (Ye et al., 4 Jun 2025).

RESTful proxying and mobile/edge deployment: MCP Bridge introduces an LLM-agnostic RESTful intermediary that mediates between stateless HTTP clients and live MCP servers, supporting risk-based execution (immediate/confirmation/isolated), connection pooling, and cross-platform access (Ahmadi et al., 11 Apr 2025).

7. Open Problems and Future Directions

Critical research frontiers include:

Dynamic, context-aware privilege frameworks: Inferring least-privilege policies directly from LLM subgoals and natural-language intent streams; fine-grained permission matrices for runtime enforcement (Li et al., 5 Jul 2025).
Automated plugin trust certification: Composable static analysis, semantic risk scoring, and verification pipelines to gate deployment (Li et al., 5 Jul 2025, Guo et al., 18 Aug 2025).
Domain-specific workflow optimization: Advanced planning/orchestration (semantic+QoS), multi-server/agent workflow reconciliation, and benchmark-driven robustness evaluation (e.g., MCPSecBench) (Li et al., 15 Oct 2025, Yang et al., 17 Aug 2025).
Dynamic tool discovery and schema evolution: Adaptive retrieval-augmented tool list generation, prompt scoping, and on-the-fly extension via RAG or embedding search (Pan et al., 25 Aug 2025, Hayashi et al., 6 Oct 2025).
Scalable, federated multi-tenant deployments: Trusted cross-domain MCP hosting under federated identity, audit logging, and standardized metadata schemas for heterogeneous agentic platforms (Hou et al., 30 Mar 2025, Pan et al., 25 Aug 2025).

A plausible implication is that as MCP adoption broadens, governance frameworks combining policy languages, cryptographically signed tool registries, and behavioral audit trails will become central to sustaining secure, performant, and interoperable agentic software ecosystems (Hou et al., 30 Mar 2025, Pan et al., 25 Aug 2025, Guo et al., 18 Aug 2025, Li et al., 5 Jul 2025).