Introducing Model Context Protocol (MCP) and Function Calling
Function calling lets an LLM directly call predefined tools, while MCP (Model Context Protocol) standardizes how LLMs find, use, and manage many tools across different models and providers. MCP acts as a universal layer to decouple tools from the AI, making systems more modular, scalable, and easier to update without touching core agent code.
Function calling (native/provider-specific):
How it works: You define tools (functions) in the prompt, and the LLM outputs structured data (usually JSON) to call them, running the logic within the model’s environment or a linked backend.
Pros: Simple for basic use, fast for single-model setups, lightweight.
Cons: Vendor-specific (OpenAI’s differs from Google’s), requires code changes for new tools, less modular, harder to scale.
MCP:
How it works: A standardized protocol where tools are hosted on separate MCP servers. The LLM communicates with these servers (via a host/client) to discover, request, and use tools, handling the logic externally.
Pros: Universal (works across models), highly modular (add tools without agent code changes), scalable, better governance, reusable infrastructure.
Cons: More complex setup, introduces network latency, newer with a smaller ecosystem.
A quick comparison:
Architecture: Function calling is often integrated within the LLM’s process; MCP creates a distinct layered architecture with separate servers.
Flexibility: MCP is far more flexible, acting like an app store for AI tools, while function calling is like built-in OS features.
Use case: Use native function calling for quick, simple projects with one model. Use MCP for complex, scalable systems needing many tools, cross-model compatibility, or independent tool updates (e.g., enterprise AI agents).
MCP works by introducing a standard communication protocol between models and tools. It uses a client-server architecture made up of four parts:
MCP hosts: User-facing applications where model interactions happen (like Claude Desktop or code editors).
MCP clients: Middleware that manages communication between the host and backend tools.
MCP servers: Services that expose tools through the MCP protocol.
Data sources: The underlying systems (databases, APIs, or files) that tools access.
Each tool advertises its functionality using a structured description, so compatible models can understand how to interact with it. This allows developers to define a tool once and have it work with any model that supports MCP.
By shifting from a many-to-many integration model to a one-to-many architecture, MCP simplifies development and reduces the overhead of supporting multiple models or tools in an application.
How LLM Function-Calling Works
Function-calling follows a predictable flow:
A user submits a request (e.g., “What’s the weather in Seattle?”).
The LLM detects that it needs external data.
It selects the appropriate function from a predefined list.
It fills in the parameters using a structured format like JSON.
The application executes the API call.
The response is passed back to the LLM, which incorporates it into the final answer.
This approach gives LLMs access to real-time information or services without training on that data. From a development perspective, it’s like giving the model a cookbook of functions it can call when needed.
However, function-calling implementations vary between providers like OpenAI, Meta, and Google. There’s no standard format for function definitions or return values, which creates compatibility issues. Supporting multiple models often means maintaining duplicate logic for each.
Function-calling also doesn’t natively support chaining functions together in multi-step workflows. Developers must build that logic themselves, which can quickly become a bottleneck in more advanced applications.
MCP vs. LLM Function Calling: The Key Differences
1. Responsibility: Instruction Generation vs Execution Orchestration
Function-calling is responsible for translating natural language prompts into formalized, machine-readable instructions. The LLM interprets the user’s intent, selects the right function from a list, and fills in its parameters using a structured format such as JSON. This phase ends once the LLM emits a valid function call instruction.
MCP takes over from there. Its role is to handle execution: discovering the correct tool, invoking it, managing responses, and returning structured results to the application or model. It abstracts away the execution layer, enabling consistency across different backends. By separating instruction generation from execution, MCP allows LLMs to focus on language understanding and intent detection while offloading operational concerns to a dedicated orchestration layer.
2. Position in the LLM Integration Pipeline
The function-calling mechanism sits at the front end of the integration pipeline. It’s the interface where language models convert user input into calls that external systems can understand. This stage is highly vendor-specific: each LLM provider defines its own syntax and function-call format, resulting in a fragmented ecosystem of JSON schemas and response styles.
MCP sits at the back end of the pipeline. Once a function call has been generated, MCP translates that into a protocol-compatible request, routes it to the right tool, and handles the result. It acts as an abstraction layer between the model’s output and the execution logic, allowing applications to support a growing number of tools without having to hard-code logic for each combination of model and service.
3. Standardization vs Vendor-Specific Behavior
Function-calling today lacks a universal standard. Each LLM platform, such as OpenAI, Anthropic (Claude), Google (Gemini), and Meta (Llama), uses a different structure to represent function calls. Parameters like function names, argument formats, and response handling vary significantly. This inconsistency creates overhead for developers who want to support multiple models: the same function must often be defined multiple times in slightly different formats.
MCP addresses this issue by enforcing a consistent, vendor-neutral protocol for how tools expose their capabilities. It defines a shared structure for describing functions (including their names, inputs, and outputs) and a standard format for invoking them. This makes it possible to describe a tool once and use it across any model that supports MCP, reducing duplication and promoting reuse.
4. Scope of Control and Flexibility
Function-calling provides high control but low flexibility. The function interfaces are tightly defined: each has fixed inputs and expected outputs, and the LLM is constrained to operate within that structure. This is useful for precision tasks like extracting data, categorizing text, or calling APIs with strict formatting requirements. However, this rigidity becomes a limitation in open-ended, multi-step, or dynamic workflows.
MCP introduces greater flexibility by layering context, constraints, and instructions. Rather than locking the model into a narrow function format, MCP enables developers to guide model behavior through layered context, such as regulatory requirements, user preferences, or brand guidelines. This allows the system to support more natural, adaptive conversations while still aligning with business rules. MCP also enables chaining of tools and conditional execution.
5. Scalability Across Tools and Models
Function-calling does not scale well in large, multi-tool, multi-model environments. Every new model introduced may require slightly different function definitions or response-handling logic. Similarly, adding new tools means updating function schemas and integration code for each supported model. As the number of combinations increases, so does the integration complexity.
MCP scales more efficiently by flipping the integration model. Instead of building one-off connections between each model and tool, MCP defines a shared protocol that sits between them. Tools are exposed through MCP servers using a consistent interface. MCP-compatible clients and hosts can then access any tool without needing to know its internal implementation.
6. Role in Enterprise System Interoperability
Function-calling was designed for simplicity and works well for individual tasks that require structured output. But it doesn’t offer features for broader enterprise integration, such as managing state across sessions, enforcing compliance rules, or coordinating multiple systems in a workflow. Developers must build custom orchestration and compliance layers on top if they want to use function-calling in regulated or mission-critical environments.
MCP is built for enterprise interoperability from the ground up. Its layered context model allows developers to inject organizational policies, domain knowledge, and user-specific data directly into the model’s execution environment. This ensures that outputs remain compliant and aligned with enterprise goals. MCP also integrates well with systems like CRMs, ERPs, and workflow automation tools.
7. Use Cases
Function-calling works best in scenarios where tasks are clearly scoped, inputs and outputs are predictable, and system integration is straightforward. These use cases benefit from the model’s ability to return structured responses within predefined formats:
Data extraction: Extracting specified fields from unstructured text, such as pulling names, dates, or product IDs from customer messages.
Text classification: Categorizing support tickets, documents, or user queries into predefined buckets (e.g., “Billing,” “Technical Support”).
API Integration: Calling external services to fetch or update data (e.g., getting weather information, stock prices, or triggering workflows).
Form-filling and submission: Converting user input into structured forms for automated processing, such as insurance claims or account creation.
These tasks require precision and consistency, and function-calling ensures that the LLM stays within clearly defined operational boundaries.
MCP is well-suited for use cases that involve dynamic, multi-step workflows, long-term context, or interaction with multiple enterprise systems. It is best in situations where flexibility, context-awareness, and control are essential:
Domain-specific assistants: Assistants that provide expert guidance in regulated domains like finance or healthcare, incorporating rules, user profiles, and institutional knowledge via layered context.
Regulatory compliance tools: Ensuring LLM responses comply with industry or legal requirements by injecting relevant constraints into the conversation flow.
Enterprise workflow automation: Coordinating actions across multiple systems (e.g., CRM, ERP, ticketing) in response to LLM-generated instructions, with built-in orchestration and result handling.
Brand-aligned customer interactions: Enforcing tone, terminology, and messaging guidelines in customer-facing applications, while maintaining flexibility in how the LLM engages.
These use cases require accurate execution as well as adaptability, transparency, and maintainability as systems scale.
MCP Pros and Cons
Pros
Flexible interaction design: MCP supports layered context management, allowing systems to guide LLMs with regulatory rules, brand voice, user-specific data, and task constraints. This enables dynamic and nuanced interactions that go beyond simple prompt-response behavior.
Scalable architecture: MCP’s one-to-many integration model decouples tools from models. Tools are defined once and can be reused across any MCP-compatible model, reducing duplication and accelerating development as new tools or models are added.
Enterprise integration: MCP is built to operate in complex enterprise environments. It enables LLMs to interact with diverse systems like CRMs, ERPs, or compliance engines while maintaining traceability, governance, and structured outputs aligned with business requirements.
Multi-step workflow support: Unlike function-calling, MCP can support chaining and orchestration across multiple tools. This is especially useful for workflows that span several steps, such as regulatory checks, data aggregation, or conditional logic handling.
Context-aware output control: By layering structured context, MCP allows developers to shape model behavior without hardcoding rules or sacrificing creativity, balancing control and flexibility.
Cons
Higher complexity: Implementing MCP requires careful design of context layers, tool descriptions, and the infrastructure needed to support the protocol. This adds a layer of complexity compared to simpler function-calling setups.
Increased development overhead: Because MCP involves orchestrating multiple components (hosts, clients, servers, data sources), it demands more setup and maintenance, especially in early phases.
More resource intensive: The additional context handling and orchestration logic may increase computational costs and latency, particularly in multi-step or stateful interactions.
LLM Function-Calling Pros and Cons
Pros
Predictable output: Function-calling constrains the model to return structured outputs that match predefined function signatures. This improves reliability, especially in systems that require consistent formatting, like API integrations or classification tasks.
Simple integration path: Function-calling aligns well with traditional software development practices. Developers define functions with specified inputs and outputs, and the LLM calls them when appropriate. This makes initial integration straightforward.
Task-specific optimization: Function-calling is suitable for well-bounded tasks, such as extracting fields from text, routing requests, or returning structured answers. These scenarios benefit from the precision of rigid function definitions.
Vendor tooling support: Major LLM providers (OpenAI, Anthropic, Google, Meta) support function-calling with tools and documentation, making it easier to get started with minimal infrastructure.
Cons
Limited flexibility: Because function-calling is built around static function schemas, it struggles with tasks that require nuanced context management, creativity, or open-ended dialogue.
Poor multi-step workflow support: Function-calling does not natively support chaining functions or maintaining context across steps. Developers must build external orchestration logic to manage multi-stage workflows.
Scalability friction: Supporting multiple models or expanding the number of functions increases complexity. Each LLM may require a slightly different format for function definitions, leading to duplicated effort and fragile integrations.
No standardization: The lack of a universal schema for function calls means that each LLM vendor has its own format. This creates barriers to interoperability and reuse across platforms.
LLM Function Calling and MCP: A Complementary Relationship
Function-calling and MCP are not competing paradigms; they serve different purposes and can work together effectively in a layered architecture. Using both in combination enables more powerful and maintainable LLM-based systems, especially in complex enterprise environments.
Function-calling is best suited for the first phase of the interaction: interpreting user prompts and generating structured instructions. It allows the LLM to convert natural language into actionable requests, such as API calls, with clearly defined parameters. This ensures the model produces predictable, machine-readable outputs that can drive real-world actions.
MCP comes into play in the second phase: executing those instructions in a standardized way. It provides a framework for discovering tools, invoking them, and managing responses, all without hardcoding tool-specific logic. This separation of concerns improves system modularity, making it easier to integrate new tools or switch models without breaking existing workflows.
This two-phase model (generation through function-calling, execution through MCP) offers the best of both worlds. It combines the LLM’s ability to interpret and generalize user intent with MCP’s ability to deliver structured, scalable, and reusable execution logic. For example, a customer support system could use function-calling to extract structured information from user input, then pass that information to MCP for ticket routing, escalation, or follow-up actions.
Managing MCP with Obot
While the Model Context Protocol (MCP) provides an open standard for connecting models to tools and services, operating MCP in real systems introduces practical challenges around deployment, security, and observability. By layering management and orchestration on top of the MCP standard, Obot enables teams to move from experimentation to production without sacrificing portability or openness — preserving the benefits of MCP while reducing operational overhead.
See how Obot can help get your team set up for success:
Explore the Obot open-source platform on GitHub — and start building with a secure, extensible MCP foundation
Schedule a demo to see how Obot can centralize and scale MCP integrations across your team or enterprise
Read the docs for step-by-step guides, tutorials, and reference materials to accelerate your implementation
