Tadata

Introduction

Since the introduction of the Model Context Protocol (MCP), teams have been exploring how to transform their existing APIs into MCP tools. When implemented well, MCP servers unlock powerful AI-native workflows and allow users to interact with software directly from their favorite AI tools.

Successful MCP design should:

Guide the language model (LLM) to choose the correct tool.
Ensure the LLM uses the tool result effectively, including chaining follow-up calls.
Deliver reliable, accurate, and efficient performance.

When implemented poorly, however, MCP servers can confuse the LLM, triggering excess tool calls and degrading performance. As we help our clients build high-quality MCP servers, we’ve compiled a set of common pitfalls, key considerations, and practical strategies essential for success.

The 1:1 Mapping Trap

The most frequent mistake in MCP implementations is attempting to wrap every existing API endpoint as a separate MCP tool. Traditional REST APIs tend to include dozens of highly specific endpoints. Exposing each one individually in an MCP toolset creates excessive noise and tool bloat.

Agents typically perform best with a limited number of tools (ideally between 10 and 15). When presented with too many options, models struggle to pick the right one and often make multiple suboptimal calls to achieve a single task.

We recommend the following strategies to combat this 1:1 trap:

Eliminate unused tools. If a tool is never called in practice (as observable via Tadata’s analytics dashboard), consider removing it entirely.
Group related operations into higher-order tools. Instead of exposing createCustomer, updateCustomer, and getCustomer separately, build a single manageCustomerProfile tool that handles these scenarios through parameters.
Use flexible parameters instead of duplicating tools. For example, rather than creating both getCustomersJSON and getCustomersXML, expose a single getCustomers tool with an outputFormat parameter.
Focus on the "happy path." Start by supporting the most common, high-value use cases before addressing edge cases.
Name tools by capability, not implementation. Tools like getInsightsForCampaign or manageUserAccess are clearer and more useful than generic names like runReport or updateData.

Reducing Excessive Data

REST APIs often return much more data than agents need. For example, a customer query might return every transaction, address, preference, and note associated with the customer, even when only the name and status are needed.

This introduces several problems:

Increased token usage and cost.
Slower responses.
Higher risk of agent confusion or misinterpretation.

To mitigate this, consider the following:

Use query-based APIs like GraphQL to precisely control which fields are returned.
Implement intelligent filtering in your MCP server, so only relevant subsets of data are sent back.
Allow flexible input parameters (e.g., booleans like includePreferences, includeAddresses) to keep responses concise.
Preprocess large or deeply nested objects to remove extraneous or redundant fields.

MCP servers should not simply provide backend data. They should curate responses. The goal is to give the model enough context to reason effectively, but not too much context as to overwhelm the model.

Using OpenAPI as a Semantic Source of Truth

OpenAPI specs are a natural starting point for MCP tool generation, but they must be written with care. It's not enough to describe structure; you must also convey intent.

To make your OpenAPI specification MCP-friendly:

Use operationId to define clear, unique tool names.
Populate the summary and description fields with information that helps the model understand when and why to use the tool.
Avoid deeply nested input schemas that might confuse the agent.
Ensure the security and responses sections are complete and accurate.
Add high-level usage context, preconditions, or expected outcomes, especially when exposing tools to general-purpose agents.

By enriching your OpenAPI spec with meaningful context, you enable safer, smarter tool use and reduce the chances of confusion or misuse.

Different APIs Require Different MCP Strategies

Not all APIs convert cleanly to MCP tools. Understanding the API’s original intent is essential to choosing the right approach.

Backend-for-Frontend (BFF) APIs tend to be the best fit. They are purpose-built for specific UI workflows, return focused data, and are already aligned with user-level actions.
Server-to-Server APIs are more complex. They favor performance and flexibility over clarity. These typically require substantial abstraction or reshaping before becoming effective MCP tools.

Tadata's Approach to Smarter MCP Servers

At Tadata, we’re building tools and infrastructure to make MCP development faster, safer, and more effective. Our platform supports instant setup and experimentation, and very detailed analytics to understand sources of error and possibilities for improvement.

Beyond that, we have also been implementing intelligent guidance features, including:

Detection of overly verbose tools or unused endpoints.
Identification of weak or missing OpenAPI components.
Validation of base URLs and authentication schemes.

Further, when we work with companies to create meaningful MCPs, we do not limit ourselves to API endpoints. Rather, we incorporate API documentation, best practices, extra context, and clever starting points that guide the LLM and maximize performance. For those curious, we highlight what these extra integrations look like in practice in our case study on Tavily.

We believe that building good MCP servers is as much about iteration and feedback as it is about design. Our platform exists to help you explore and refine what works best for your use case.

The 10-Step Checklist for MCP Server Tool Design

Putting all of this together, here are guidelines to consider for a best practice MCP server:

Start from use cases, not backend endpoints.
Limit the number of tools to fewer than 15 to avoid agent confusion.
Combine related operations into composite tools that align with workflows.
Use descriptive, action-oriented tool names that reflect real intent.
Filter tool responses to return only the most relevant data.
Use flexible parameters instead of duplicating similar tools.
Simplify input schemas and avoid unnecessary nesting.
Write OpenAPI summaries and descriptions with purpose and clarity.
Define clear stopping points and data boundaries in tool responses.
Monitor actual agent usage and iterate on what works.