- MCP (Model Context Protocol): Transport-agnostic open protocol that standardizes the discovery and invocation of tools/resources across hosts and servers. most suitable Portable, multi-tool, multi-runtime system.
- Function call: Provider functions where the model selects a declared function (JSON schema), returns parameters, and then executes it at runtime. most suitable Single application, low latency integrated.
- OpenAPI tools: use OpenAPI Specification (OAS) 3.1 As a contract for HTTP services; the proxy/tool layer automatically generates callable tools. most suitable Governance, service mesh integrated.
comparison table
| concern | MCP | function call | Open API tools |
|---|---|---|---|
| Interface contract | Protocol Data Model (Tools/Resources/Tips) | JSON schema for each function | Organization of American States 3.1 Document |
| Discover | dynamic vias tools/list |
Static list provided to the model | From Organization of American States; Catalogable |
| pray | tools/call Via JSON-RPC session |
Model selection function; application execution | HTTP requests for each OAS operation |
| Arrange | Host routing across many servers/tools | Application local link | Agent/Toolkit Route Intent → Action |
| transportation | stdio/HTTP variant | In-band via LLM API | HTTP(S) to service |
| portability | Across hosts/servers | Vendor specific surface | supplier-neutral contract |
Advantages and limitations
MCP
- Advantages: Standardized discovery; reusable servers; multi-tool orchestration; growing host support (e.g., semantic kernel, cursor; Windows integration program).
- limit: Required to run server and host policies (Identity, Consent, Sandbox). The host must implement session lifecycle and routing.
function call
- Advantages: Minimal integration overhead; fast control loop; simple validation via JSON schema.
- limit: Application local directory; portability requires redefinition per vendor; limited built-in discovery/governance.
Open API tools
- Advantages: Mature contracts; specification-compliant security solutions (OAuth2, keys); rich tools (proxy from OAS).
- limit: OAS defines HTTP contracts, not proxy control loops – you still need a coordinator/host.
Security and Governance
- MCP: Enforce host policies (allowed servers, user consent), scope for each tool, and temporary credentials. Platform adoption (such as Windows) emphasizes registry controls and consent prompts.
- Function call: Validate model-generated parameters against the schema; maintain permission lists; log audit requests.
- OpenAPI tools: Use OAS security schemes, gateways, and schema-driven authentication; restrict toolkits that allow arbitrary requests.
Ecosystem signals (portability/adoption)
- MCP host/server: Supported by Microsoft Semantic Core (host + server role) and cursor (MCP directory, IDE integration); Microsoft said it will provide Windows-level support.
- Function call: Broadly applicable to the main LLM API (OpenAI documentation shown here), with a similar pattern (architecture, selection, tool results).
- OpenAPI tools: Multiple proxy stacks are automatically generated from OAS (LangChain Python/JS) tools.
Decision rules (when to use which)
- Application-native automation with few operations and strict latency goals → function call. Keep definitions small, verify rigorously, and unit test loops.
- Cross-runtime portability and shared integration (agent, IDE, desktop, backend) → MCP. Standardized discovery and invocation across hosts; reuse servers across products.
- HTTP serving enterprise assets requiring contracts, security solutions and governance → Open API tools with the coordinator. Use OAS as source of truth; generate tools, implement gateways.
- Blending modes (common): Keep organization of american states To provide services to you; by exposing them MCP server For portability, or install the subset as function call Suitable for delay-critical product surfaces.
refer to:
MCP (Model Context Protocol)
Function call (LLM tool calls function)
OpenAPI (specification + LLM tool chain)
Michal Sutter is a data science professional with a master’s degree in data science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex data sets into actionable insights.
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