MCP: Model-Context-Protocol for AI Tool Interaction

• MCP is an open, extensible protocol standardizing agent–tool interactions to support context-rich reasoning and dynamic tool invocation.
• It employs JSON-RPC 2.0 and structured schemas for clear definition of requests, responses, and error handling to enhance interoperability.
• MCP is applied in domains like IoT, manufacturing, finance, and computer vision, while addressing security challenges such as tool poisoning and privilege escalation.

The Model-Context-Protocol (MCP) is an open, extensible protocol standardizing agent–tool interaction for artificial intelligence systems, with particular emphasis on orchestrating context-rich reasoning and dynamic tool invocation. Conceived to resolve longstanding interoperability barriers, MCP provides a universal, language-agnostic schema for negotiating capabilities, invoking external functions, and integrating data sources into LLM-based and agentic workflows. It is widely adopted in domains ranging from manufacturing and IoT to finance, computer vision, and multi-agent orchestration, but has also been subject to systematic security analysis due to its broad attack surface and dynamic execution model.

1. Formal Definition and Schema

At its core, MCP operationalizes a tripartite architecture: the Host (AI application/agent runtime), the Client (protocol orchestrator), and the Server (tool/resource provider). MCP uses JSON-RPC 2.0 as its serialization and transport substrate. Every operation consists of a context—comprising semantic features, metadata, and hash-based integrity proofs—exchanged as part of standardized message forms:

• Request: { "jsonrpc": "2.0", "id": <id>, "method": <method>, "params": <P> }
• Response: { "jsonrpc": "2.0", "id": <id>, "result": <R> }
• Error: { "jsonrpc": "2.0", "id": <id>, "error": <E> }
• Notification: { "jsonrpc": "2.0", "method": <method>, "params": <P> }

The protocol supports structured feature categories:

• Tools: Executable operations, each described by a JSON schema (ToolDescriptor) specifying input/output types, parameter constraints, and a natural-language description.
• Resources: Data endpoints referenced by context keys, supporting connection to databases, files, or external APIs.
• Prompts: Parameterized template instructions for agent invocation and context augmentation.

Context objects are typically modeled as hierarchical key-value stores, supporting persistent memory, tiered storage, and schema-bound value checking. Agent–server compatibility is enforced by matching declared tool schemas and runtime validation, although large-scale audits have revealed frequent schema divergence in practice, especially in vision domains (Tiwari et al., 26 Sep 2025).

2. Protocol Workflow, Architectural Patterns, and Lifecycle

The canonical MCP interaction unfolds as:

1. Discovery: The client queries an MCP server for available tools, resources, and prompts. Capability negotiation and schema exchange occur at this stage.
2. Planning: The agent (often an LLM) determines, based on user input and discovered metadata, which external tool(s) to invoke, the associated arguments (parsed from NL input), and the order of execution.
3. Invocation: The client serializes each tool invocation as an MCP request, forwarding it to the server. If the server is stateless, each request contains all necessary context; if stateful, memory slots may be versioned or persist across invocations.
4. Response Handling and Context Update: The server returns results, error messages, or notifications, which the agent processes to update its context and determine subsequent actions.

MCP distinguishes itself from legacy agent architectures by decoupling:

• Skill/capability modeling: No OWL, AAS, or hard-coded semantic models required (Silva et al., 12 Jun 2025).
• Reasoning logic from execution: Models may be frozen/fine-tuned independently of tool availability (Liu et al., 3 May 2025).
• Static from dynamic integration: New tools can be registered or removed at runtime with only declarative schema modifications.

Typical deployment lifecycles encompass creation (registration, code/manifest verification), operation (sandboxed execution, notification handling), and update (authenticated rollout, code–policy migration)—each phase with well-defined state transitions and security checkpoints (Hou et al., 30 Mar 2025).

3. Application Domains and Case Studies

MCP’s versatility is evidenced by its penetration into diverse areas:

• Wireless and IoT: The IoX framework demonstrates LLMs querying wireless "expert" models via MCP, achieving up to 98% task accuracy without LLM retraining (Liu et al., 3 May 2025), while IoT-MCP attains 100% task success and <250 ms latency for microcontroller-based sensing and actuation (Yang et al., 25 Sep 2025).
• Manufacturing: MCP bypasses heavyweight ontology-based skill modeling by exposing operational functions as self-describing callable tools, supporting dynamic process planning and error correction in industrial automation scenarios (Silva et al., 12 Jun 2025).
• Financial Markets: In multi-scale, systemic-risk modeling, MCP agents communicate over standardized message types, integrating wavelet-decomposed time series and transfer entropy networks (Bhandari, 10 Jul 2025).
• Multi-Agent Orchestration: MCP provides primitives for context sharing, dynamic task allocation, conflict resolution, and synchronization barriers, enabling scalable, contextually-coherent MAS with documented efficiency and solution quality gains (Krishnan, 26 Apr 2025).
• Computer Vision: Protocol-level audits show MCP enables compositional pipelines for detection, segmentation, and tracking, but high rates of schema-format failures and privilege escalation necessitate runtime validation and schema extension (Tiwari et al., 26 Sep 2025).

4. Security Threats and Ecosystem Risks

MCP's unconstrained dynamism substantively enlarges the attack surface:

• Tool Poisoning: Malicious servers embed adversarial instructions in tool descriptions, which LLMs may naively execute (Jamshidi et al., 6 Dec 2025, Song et al., 31 May 2025).
• Shadowing: Attacker tools contaminate context, manipulating argument extraction or output routing even when benign tools are invoked.
• Rug Pulls: Trusted server descriptors are mutated post-approval, bypassing static checks and facilitating time-of-use exploitation.
• Privilege Escalation: Over-privileged or insufficiently sandboxed plugins enable command injection, arbitrary file/network access, and data theft (Li et al., 5 Jul 2025, Errico et al., 25 Nov 2025).
• Supply Chain Risk: Inadequate registry hygiene (e.g. unchecked manifest updates, account hijacking, affix-squatting) amplifies the risk of mass compromise (Li et al., 18 Oct 2025).
• Cryptography Misuse: Ad hoc developer-enforced cryptographic layers result in frequent violations—weak hashes, static keys/seeds, lack of integrity protection—impacting nearly 20% of crypto-enabled servers (Yan et al., 3 Dec 2025).

Empirical studies, including MCPSecBench (Yang et al., 17 Aug 2025), MCPSafetyScanner (Radosevich et al., 2 Apr 2025), and MICRYSCOPE (Yan et al., 3 Dec 2025), document >85% attack success on at least one major platform, pervasive lack of schema and integrity validation, and significant user inability to distinguish or avoid malicious servers.

Threat Class	Example Attack	Mitigation/Detection (from cited papers)
Tool Poisoning	Credential exfil via descriptor	Manifest signing, semantic vetting (Jamshidi et al., 6 Dec 2025)
Shadowing	Output leakage via context contamination	Context/descriptor audit, runtime guardrails (Jamshidi et al., 6 Dec 2025)
Rug Pull	Post-deployment tampering	Code/signature checks, version pinning (Li et al., 18 Oct 2025)
Privilege Escalation	Command injection via unsanitized inputs	JIT permissions, sandboxing, RBAC (Li et al., 5 Jul 2025, Errico et al., 25 Nov 2025)

5. Security Benchmarks, Standards, and Mitigation Frameworks

To address systemic vulnerabilities, researchers have developed:

• MCPSecBench: An extensible security benchmark integrating attacks, prompt corpora, and validation routines to systematically test major MCP providers. Results show >85% compromise rates, with core vulnerabilities affecting all tested environments (Yang et al., 17 Aug 2025).
• MCPSafetyScanner: An agentic auditing tool for scanning MCP servers for known vulnerabilities via adversarial prompt generation and rule-based diagnosis (Radosevich et al., 2 Apr 2025).
• Layered Defense Frameworks: Protocol-level signing (RSA/ECDSA) of descriptors/servers, semantic vetting (LLM-on-LLM auditing), and runtime behavioral guardrails shown to reduce unsafe tool invocation by ~70%, albeit with latency trade-offs (1–2 s overhead) (Jamshidi et al., 6 Dec 2025).

Best-practice recommendations include:

• Automated, fine-grained privilege management—just-in-time scopes based on intent, not manifest (Li et al., 5 Jul 2025).
• Containerized server execution with explicit resource gating and audit logging (Errico et al., 25 Nov 2025).
• Cryptography hardening—built-in AEAD protocol extensions, runtime key/IV generation, and avoidance of legacy primitives (Yan et al., 3 Dec 2025).
• Centralized governance—curated registries, code signing, periodic re-vetting, DLP/Audit integration, and anomaly-based behavioral policies (Errico et al., 25 Nov 2025, Hou et al., 30 Mar 2025).

6. Ecosystem Scale, Adoption, and Empirical Studies

Large-scale measurement reveals:

• Ecosystem Size: Over 67,000 unique MCP servers catalogued across major registries by mid-2025, with Python (≈66%) and JavaScript/TypeScript (≈20%) dominating codebase languages. Only ≈49% of listed projects are valid or non-superficial (Guo et al., 29 Sep 2025, Li et al., 18 Oct 2025).
• Server Quality: High prevalence of dependency monocultures (≥90% of Java servers on spring-boot/core/web), stagnant maintenance, and widespread lack of input validation—particularly in Go/Rust servers—produce substantial supply-chain risk (Guo et al., 29 Sep 2025).
• Registry Hygiene: Frequent hijackable, dead, or redirected repositories, affix-squatting, and lack of credential screening are observed in both centralized and decentralized markets (Li et al., 18 Oct 2025).
• Protocol Convergence: SSE (Server-Sent Events) and JSON-RPC dominate as transport paradigms. Most clients are 1:1 (single server), although multi-server capabilities steadily increase.

Protocols are beginning to offer extensions for explicit memory modeling, semantically grounded schema annotations, and runtime validator hooks to address interoperability and compositionality weaknesses, especially within vision/multimodal workflows (Tiwari et al., 26 Sep 2025).

7. Research Directions and Open Questions

Open challenges are:

• Building efficient, context-aware, and cryptographically secure permission and provenance frameworks adaptive to agentic, dynamic tool usage (Errico et al., 25 Nov 2025, Yan et al., 3 Dec 2025).
• Standardizing cross-registry validation, continuous code-signing, and decentralized trust models to cope with registry fragmentation (Li et al., 18 Oct 2025, Guo et al., 29 Sep 2025).
• Developing formal verification techniques for complex, non-deterministic, multi-agent workflows—compositional security guarantees over sequences of language-driven tool invocations remain unsolved (Krishnan, 26 Apr 2025).
• Adapting MCP for ultra-low-latency, multi-tenant IoT and edge deployments (resource-awareness, protocol compression) (Yang et al., 25 Sep 2025).
• Integrating quantum-safe cryptographic agility, federated learning orchestration, and differential privacy constraints in next-generation agent systems (Chhetri et al., 26 Aug 2025).

MCP represents a foundational substrate for open, interoperable, and semantically rich agent–tool ecosystems. Realizing its promise will require protocol-level security hardening, governance, and continued multidisciplinary research across protocol engineering, cybersecurity, software ecosystems, and machine reasoning.