Table of contentsComparison TableStrengths and LimitsSecurity and GovernanceEcosystem Signals (Portability/Adoption)Decision Rules (When to Use Which)References:

MCP (Model Context Protocol): Open, transport-agnostic protocol that standardizes discovery and invocation of tools/resources across hosts and servers. Best for portable, multi-tool, multi-runtime systems.

Function Calling: Vendor feature where the model selects a declared function (JSON Schema), returns arguments, and your runtime executes. Best for single-app, low-latency integrations.

OpenAPI Tools: Use OpenAPI Specification (OAS) 3.1 as the contract for HTTP services; agent/tooling layers auto-generate callable tools. Best for governed, service-mesh integrations.

Comparison Table

ConcernMCPFunction CallingOpenAPI ToolsInterface contractProtocol data model (tools/resources/prompts)Per-function JSON SchemaOAS 3.1 documentDiscoveryDynamic via tools/listStatic list provided to the modelFrom OAS; catalogableInvocationtools/call over JSON-RPC sessionModel selects function; app executesHTTP request per OAS opOrchestrationHost routes across many servers/toolsApp-local chainingAgent/toolkit routes intents → operationsTransportstdio / HTTP variantsIn-band via LLM APIHTTP(S) to servicesPortabilityCross-host/serverVendor-specific surfaceVendor-neutral contracts

Strengths and Limits

MCP

Strengths: Standardized discovery; reusable servers; multi-tool orchestration; growing host support (e.g., Semantic Kernel, Cursor; Windows integration plans).

Limits: Requires running servers and host policy (identity, consent, sandboxing). Host must implement session lifecycle and routing.

Function Calling

Strengths: Lowest integration overhead; fast control loop; straightforward validation via JSON Schema.

Limits: App-local catalogs; portability requires redefinition per vendor; limited built-in discovery/governance.

OpenAPI Tools

Strengths: Mature contracts; security schemes (OAuth2, keys) in-spec; rich tooling (agents from OAS).

Limits: OAS defines HTTP contracts, not agentic control loops—you still need an orchestrator/host.

Security and Governance

MCP: Enforce host policy (allowed servers, user consent), per-tool scopes, and ephemeral credentials. Platform adoption (e.g., Windows) emphasizes registry control and consent prompts.

Function Calling: Validate model-produced args against schemas; maintain allowlists; log calls for audit.

OpenAPI Tools: Use OAS security schemes, gateways, and schema-driven validation; constrain toolkits that allow arbitrary requests.

Ecosystem Signals (Portability/Adoption)

MCP hosts/servers: Supported in Microsoft Semantic Kernel (host + server roles) and Cursor (MCP directory, IDE integration); Microsoft signaled Windows-level support.

Function Calling: Broadly available across major LLM APIs (OpenAI docs shown here) with similar patterns (schema, selection, tool results).

OpenAPI Tools: Multiple agent stacks auto-generate tools from OAS (LangChain Python/JS).

Decision Rules (When to Use Which)

App-local automations with a handful of actions and tight latency targets → Function Calling. Keep definitions small, validate strictly, and unit-test the loop.

Cross-runtime portability and shared integrations (agents, IDEs, desktops, backends) → MCP. Standardized discovery and invocation across hosts; reuse servers across products.

Enterprise estates of HTTP services needing contracts, security schemes, and governance → OpenAPI Tools with an orchestrator. Use OAS as the source of truth; generate tools, enforce gateways.

Hybrid pattern (common): Keep OAS for your services; expose them via an MCP server for portability, or mount a subset as function calls for latency-critical product surfaces.

References:

MCP (Model Context Protocol)

https://modelcontextprotocol.io/

https://www.anthropic.com/news/model-context-protocol

https://modelcontextprotocol.io/docs/concepts/tools

https://modelcontextprotocol.io/legacy/concepts/tools

https://github.com/modelcontextprotocol

https://developers.openai.com/apps-sdk/concepts/mcp-server/

Semantic Kernel adds Model Context Protocol (MCP) support for Python

Integrating Model Context Protocol Tools with Semantic Kernel: A Step-by-Step Guide

https://cursor.com/docs/context/mcp

https://learn.microsoft.com/en-us/semantic-kernel/concepts/kernel

Function Calling (LLM tool-calling features)

https://platform.openai.com/docs/guides/function-calling

https://platform.openai.com/docs/assistants/tools/function-calling

https://help.openai.com/en/articles/8555517-function-calling-in-the-openai-api

https://docs.anthropic.com/en/docs/build-with-claude/tool-use

https://docs.claude.com/en/docs/agents-and-tools/tool-use/overview

https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-tool-use.html

OpenAPI (spec + LLM toolchains)

https://spec.openapis.org/oas/v3.1.0.html

https://swagger.io/specification/

https://www.openapis.org/blog/2021/02/18/openapi-specification-3-1-released

https://python.langchain.com/docs/integrations/tools/openapi/

https://python.langchain.com/api_reference/community/agent_toolkits/langchain_community.agent_toolkits.openapi.toolkit.OpenAPIToolkit.html

https://docs.langchain.com/oss/javascript/integrations/tools/openapi

https://js.langchain.com/docs/integrations/toolkits/openapi

The post Model Context Protocol (MCP) vs Function Calling vs OpenAPI Tools — When to Use Each? appeared first on MarkTechPost.