MCP tools: What they are + 7 tools to try (2026)

AI agents are quickly moving from experimentation into production across enterprise environments. Integration developers are building AI assistants that automate workflows across ERP, CRM, finance, and operations systems. But an AI agent is only as powerful as the tools it can call.

This is where the Model Context Protocol (MCP) comes in. The MCP defines a standardized way for AI agents to discover and invoke tools across systems. It creates a consistent interface between AI models and the APIs, integrations, and workflows that power real business outcomes.

Despite growing adoption, most content on MCP tools stays at a surface level. It explains what they are, but not how they work at the protocol level or how to deploy them reliably in enterprise environments. This matters because MCP tools are not just abstractions. They are executable functions that directly connect AI agents to production systems.

This guide explains how MCP tools operate within MCP servers, how they differ from other primitives in the model context protocol, and how AI agents discover and invoke them. It also covers the enterprise concerns that matter in real-world agentic deployments, including governance, authentication, error handling, and observability.

We’ll evaluate seven MCP tools and servers that integration developers are using today, with a focus on enterprise integration environments.

What are MCP tools?

MCP tools are executable functions exposed by MCP servers that AI clients can discover and invoke through the model context protocol.

Within the MCP protocol, tools are one of three core primitives. Understanding the distinction between them is critical when designing AI agents and integration architectures:

• Tools: Executable functions. These are model-controlled, meaning the AI agent decides when and how to call them. Examples include querying a database, triggering an integration flow, or calling external APIs.
• Resources: File-like data objects that the agent reads for context. These are application-controlled, not model-controlled. Examples include API responses, documents, or database records.
• Prompts: Predefined templates used to guide model behavior. These are user-controlled.

This separation is not just conceptual. It determines how AI agents interact with systems. Tools represent action, resources represent context, and prompts represent instruction.

At the protocol level, MCP servers expose tools through two core endpoints:

• Tools/list: Returns a JSON array of available MCP tools, including each tool’s name, description, and input schema
• Tools/call: Invokes a specific tool with structured inputs and returns the result

This directly answers a common question: how do you get a list of tools from an MCP server?

The answer is the tools/list endpoint. AI agents and other MCP clients use it to discover available executable functions before deciding which ones to call.

A simple enterprise example illustrates how this works. An AI agent connected to an ERP MCP server might call a get_open_orders tool. It passes a customer ID as input. The MCP server executes the underlying integration, queries the system via APIs, and returns structured order data. The developer does not need to write custom integration code for that interaction.

This is the core value of MCP tools. They standardize how AI agents connect to systems, turning APIs and integration flows into callable functions that AI models can use directly.

7 MCP tools and servers worth using in enterprise environments

MCP tools span a wide range of categories, from developer tooling and data access to SaaS connectors and enterprise integration platforms. The following seven MCP servers and tools reflect strong developer momentum and practical relevance for enterprise integration use cases.

MCP Inspector

MCP Inspector is the official debugging and testing tool for MCP servers. It allows developers to inspect tool definitions, send tools/call requests, and validate server behavior before connecting an AI agent or application.

For integration developers, this is essential tooling. It provides visibility into how MCP tools are defined and how they behave under real requests. Without a tool like this, debugging MCP servers becomes significantly more difficult, especially when working with complex schemas or multiple APIs.

MCP Inspector is also the answer to another common question: what is the tool to test MCP servers? In practice, it is the default choice for developers working with the MCP protocol.

GitHub MCP Server

The GitHub MCP server exposes GitHub actions and workflows as callable MCP tools. AI agents can create issues, review pull requests, trigger CI/CD workflows, and interact with repositories directly.

This is one of the most widely adopted MCP servers among developers. It connects AI agents to core coding workflows and enables automation across repositories without custom scripts.

In enterprise environments, governance is critical. The GitHub MCP server relies on scoped authentication and admin controls to manage access. Teams need to carefully define permissions to avoid overexposing repositories or allowing unintended actions by AI assistants.

Postgres / Database MCP Server

Database MCP servers allow AI agents to query relational databases such as Postgres using MCP tools. These tools translate structured queries into executable functions that the agent can invoke.

This is particularly useful in finance, analytics, and operations contexts. Instead of building custom APIs, developers can expose database queries directly as MCP tools.

However, this introduces security considerations. Most enterprise deployments enforce read-only access, use connection pooling, and tightly manage credentials. Without these controls, exposing database access to AI agents can create significant risk.

Slack MCP Server

The Slack MCP server enables AI agents to post messages, retrieve threads, and search channels as part of automated workflows.

This is a common pattern in enterprise automation. AI agents can trigger notifications, escalate issues, or request approvals through Slack as part of larger integration flows.

Because Slack is often used for human-in-the-loop workflows, this MCP server plays a key role in bridging automated systems and human decision-making.

Google Drive / Filesystem MCP Server

Filesystem and Google Drive MCP servers expose document retrieval and search as callable tools.

These are critical for retrieval-augmented workflows. AI agents can pull context from internal documents, knowledge bases, or shared drives before taking action or generating outputs.

For enterprise teams, this connects LLMs to unstructured data sources. It allows agents to operate with richer context without embedding all data directly into the model.

Celigo MCP Server

Celigo’s AI Studio includes a native MCP server builder that allows integration developers to expose APIs and integration flows as MCP tools at scale.

Builders in the Celigo platform can create AI agents, create internal MCP servers or connect to external ones, and select from more than 1,000 prebuilt connectors across ERP, CRM, ecommerce, and data systems for their agents to interface with using their MCP server toolset(s). Builders can also use callable tools without requiring custom integration code or additional SDKs.

This changes how MCP tooling is implemented in enterprise environments. Instead of building MCP servers from scratch, teams can use an existing integration platform to generate and manage tools across systems.

Each of an MCP server’s AI tools inherits Celigo’s governance layer. This includes role-based access control, audit trails, and compliance with SOC 2 and HIPAA requirements. It also includes AI-powered error resolution, which automatically resolves the majority of common failures, such as authentication errors or rate limits.

This directly addresses a real-world concern: what happens when an MCP tool call fails? In many MCP servers, error handling must be implemented manually. In this case, the integration platform manages error classification and resolution across tools.

Celigo also introduces a Tool Step primitive in integration flows. This allows developers to reuse MCP tools within broader automation workflows, with schema validation and runtime connection overrides. When used with AI agents, these tools can execute in real time with low latency.

This positions the integration layer as the operational backbone for AI agents. In practical terms, the AI model acts as the decision layer, while the integration platform connects systems and executes actions.

Custom / internal MCP tools

Many enterprise teams build custom MCP tools to expose internal systems and business logic.

These tools often handle ERP queries, approval workflows, compliance checks, or proprietary processes that are not available in external MCP servers.

Building these tools requires defining schemas, managing authentication tokens, and implementing structured error responses. Developers also need monitoring and observability to track tool usage and failures.

As of the November 2025 MCP protocol updates, parallel tool calls are supported. This allows AI agents to invoke multiple tools concurrently, improving performance in multi-step workflows.

In some cases, developers also use browser automation frameworks like Puppeteer to expose web interactions as MCP tools, particularly when working with legacy systems or external applications that do not offer reliable APIs.

Custom MCP tools are often necessary, but they also introduce complexity. Without proper governance, they can contribute to tool sprawl and inconsistent behavior across systems.

How AI agents discover and call MCP tools

Understanding how AI agents interact with MCP tools requires looking at the protocol-level flow.

First, the AI client connects to an MCP server. This could be an application, an IDE, or an orchestration layer managing multiple agents.

Next, the client sends a tools/list request. The MCP server responds with a JSON array of tool definitions, including names, descriptions, and input schemas.

The AI model then evaluates these tools in context. Based on the user’s request and available data, it determines which tools to call and how to structure the inputs.

When a tool needs to be executed, the client sends a tools/call request. The MCP server runs the underlying function, often calling APIs or triggering integration flows, and returns structured results.

This loop can repeat multiple times within a single interaction. With support for parallel tool calls, AI agents can now invoke multiple MCP tools concurrently. This is particularly important in enterprise integration scenarios where workflows span multiple systems.

Human-in-the-loop design is also critical. Because tools are model-controlled, enterprise teams often implement approval steps for high-risk actions. For example, an agent may propose triggering a financial workflow, but require human approval before execution.

This balance between automation and control is central to deploying AI agents safely at scale.

Enterprise challenges when deploying MCP tools at scale

• Authentication and authorization: Most MCP servers today rely on API token authentication. While OAuth standards are emerging, teams must manage token lifecycle, rotation, and per-tool permissions carefully to avoid security gaps.
• Security exposure: Research has shown that many MCP servers are publicly accessible without proper authentication. Over-permissioning and network exposure are common risks. Enterprises must treat tool definitions as untrusted unless verified and enforce strict access controls.
• Tool sprawl and governance: As MCP adoption grows, tools proliferate across teams and systems. Without centralized management, organizations lose visibility into what tools exist, who can access them, and how they are used.
• Error handling and observability: When an MCP tool fails, the impact can cascade across workflows. Developers need structured error responses and centralized observability to understand failures across APIs and integrations.
• Data residency and compliance: MCP tools often expose sensitive data from ERP, HR, or financial systems. Enterprises must ensure compliance with SOC 2, HIPAA, and data privacy regulations when connecting AI agents to these systems.
• Change management: APIs and integration flows evolve over time. MCP tools must be versioned and updated without breaking dependent AI agents. This requires disciplined lifecycle management.

These challenges are not unique to MCP, but the model-driven nature of AI agents amplifies them. Centralizing integration and governance becomes critical as adoption scales.

How Celigo simplifies MCP tool deployment for enterprise teams

Enterprise AI agents are only effective if the MCP tools they rely on are reliable, governed, and connected to real systems. One-off API scripts or loosely managed MCP servers do not meet enterprise requirements for scale or compliance.

Celigo addresses this by positioning the integration layer as the foundation for MCP tooling. Its AI Studio includes a native MCP server builder that allows developers to expose APIs and integration flows as MCP tools in a governed environment.

Integration developers can connect systems across ERP, CRM, ecommerce, and data platforms, then expose those connections as callable tools for AI agents. This approach eliminates the need to build custom MCP servers for each use case.

Several capabilities differentiate this model:

• Access to more than 1,000 prebuilt connectors and customizable APIs, available as MCP tools without additional coding
• Centralized governance, including role-based access control and audit trails
• AI-powered error resolution that handles the majority of common failures automatically
• Compliance with enterprise standards such as SOC 2, HIPAA, and data privacy regulations
• Support for both real-time and batch integration flows

This aligns with a broader architectural pattern. The LLM serves as the decision-making layer, while the integration platform connects and executes across environments.

Celigo describes this as the AI model being the brain, and the integration platform acting as the nervous system. It connects systems, orchestrates workflows, and ensures that AI agents can take meaningful action.

For integration developers, this provides a practical path forward. Instead of coding and managing fragmented MCP servers and tools, they can build on a governed integration platform that standardizes how tools are created, managed, and executed.

To explore this approach, developers can review Celigo’s MCP server documentation and AI Studio capabilities, and evaluate how a centralized integration layer can support enterprise AI agent deployments at scale.