Why Do MCP Tools Behave Differently Across LLM Models?

If you’ve ever wired up the same MCP Tool to different LLMs and gotten wildly different results, welcome to the club.

We’ve seen it firsthand: a developer builds a tool, connects it to an MCP server, tests it with Claude… and everything works. Then they switch to GPT-4 and the agent completely ignores it. Or Cursor starts generating unpredictable chains of function calls, even when the logic seems obvious.

So what’s going on? Why do tools that follow the same schema and protocol behave so differently depending on the model?

Different Models, Different Assumptions

At its core, this friction comes down to how different models interpret the same information. While the Model Context Protocol (MCP) provides a standardized interface for tool invocation, each model brings its own internal logic, biases, and prompting preferences.

Some models lean heavily on explicit instruction scaffolding. Others try to infer meaning from descriptions and parameter names, or tool metadata. That divergence creates a kind of “model dialect,” unique to each model. If your tool doesn’t “speak” that dialect fluently, things break down.

Let’s walk through how this plays out with two of the most popular models today.

Claude (Anthropic)

Claude prefers structured prompting. It responds best when tool usage is spelled out in the system prompt or context window:

“Use search_customers when looking up customer info. Do not call it unless the user asks about a specific customer.”

Tools like Cursor may auto-generate this guidance behind the scenes based on the MCP spec. But if you’re building custom prompts, you’ll often need to add usage hints yourself, especially for multi-tool workflows.

GPT-4 (OpenAI)

GPT-4 is more inference-driven. It will often use a tool based solely on its name, description, and input schema, with no explicit prompting required.

But that flexibility can backfire. If your tool metadata is ambiguous or your descriptions too vague, the model simply fills in the gaps. As a result, you might find GPT-4 overusing a tool, skipping it entirely, or hallucinating inputs that don’t match your schema. 

Why Even the “Same” Tool Behaves Differently

Even when you don’t switch models, you might notice inconsistency.

Using a particular toolset with a specific LLM will generally yield similar results each time. However, even if you provide the same toolset to the same LLM, you’ll still see some variation in the responses. They may be similar, but won’t be identical every time, because that’s simply how LLMs work due to their probabilistic nature.

When you switch to a different LLM, you’ll almost certainly get different results. A system prompt that’s optimized for one LLM usually won’t transfer cleanly to another model, even if it’s just a different version of the same base model. This is because each LLM has its own training data, internal representations, and subtle biases, and it may interpret instructions or keywords differently.

In short, system prompts typically need to be tuned specifically for each LLM and for each version to get the best results in practice.

This variability is a feature (not a bug) of how these models work. But it reinforces why cross-model testing, adaptive prompting, and metadata tuning are critical in MCP development.

Improving MCP Tool Reliability Across LLMs

Until LLM behavior becomes more standardized (or model-native tool grounding becomes more robust), there’s no one definitive solution to address tool reliability across models. But several practical strategies have emerged:

1. Add Tool-Specific Prompt Scaffolding

Include clear guidance in your system prompt about when and how to use each tool. This is especially important when targeting Claude or other Anthropic-based models.

Use `check_balance` only when the user mentions account balance or current funds.

2. Include Usage Examples

Concrete examples improve reliability. MCP now supports adding examples for tool calls, which some models (and IDEs like Cursor) use to fine-tune agent behavior.

{  "example": {    "input": { "customer_id": "123" },    "expected_output": { "name": "Jane", "status": "Active" }  }}

3. Focus on Clear, Goal-Oriented Descriptions

Avoid generic descriptions like “Returns data from X system.” Instead, write to the user intent: 

“Retrieves a customer’s full profile, including status, plan, and last activity. Call this when the user asks for account or customer details.”

The model should understand why the tool exists—not just what it technically does.

4. Simplify Your Schema

Deeply nested input parameters tend to confuse models, especially during generation. Flatten your schema where possible and use clear, unambiguous parameter names.

✅ customer_id, query_type
🚫 meta.context.customer.object.id

If the model can’t reason about how to fill out your input, it probably won’t try.

How Gentoro Simplifies Cross-Model MCP Tool Development

At Gentoro, we’ve baked these learnings into our MCP tooling workflow. When you generate a tool in Gentoro:

• Tool descriptions are auto-optimized for clarity and intent
• Prompt scaffolding and examples can be added at generation time
• Input parameters are flattened and labeled to maximize LLM compatibility
• Tools are tested against multiple models during development

The result is more consistent agent behavior across Claude, GPT-4, and others, without having to rewrite your backend or hand-code every prompt. And because Gentoro generates MCP Tools from any OpenAPI spec, you can go from raw API to model-ready tool in minutes.

Final Thoughts: Know Your Model’s Mental Model

We’re still in the early days of multi-model agent development. Even with standardized protocols like MCP, LLMs interpret and apply tool metadata differently.  That means successful multi-model development still depends on human intuition: understanding how Claude “thinks,” or how GPT-4 infers context.

But the tools are improving. The ecosystem is learning. And with the right platform, you can build once and deploy everywhere. Speaking of the right platform, try the Gentoro Playground, or request a demo to see how we’re helping teams deliver agentic apps that work across models.