# Agent Tools and MCP Integration

### **Tools & MCP (Model Context Protocol)**

In **Crowe**, tools extend the capabilities of agents beyond their built-in reasoning, allowing them to interact with external systems, perform computations, and retrieve or manipulate data.\
Tools are modular, pluggable components that can be dynamically registered, configured, and invoked within agent workflows.

#### **Tools in Crowe**

* **Purpose:** Connect agents to APIs, databases, file systems, computational functions, or any other external resource.
* **Dynamic Registration:** Agents can load and switch tools at runtime, enabling flexible task execution.
* **Structured Interfaces:** Tools define clear input and output schemas, ensuring predictable interactions.
* **Integration Patterns:** Tools can be chained, run in parallel, or combined with orchestration layers for more complex workflows.

**Example Tool Use Cases:**

* Calling an **LLM API** for text generation or analysis.
* Executing a **data transformation function** on structured datasets.
* Querying **vector databases** for semantic search.
* Interacting with third-party services (payment gateways, CRM systems, analytics platforms).

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#### **MCP (Model Context Protocol) Integration**

The **Model Context Protocol (MCP)** provides a standardized interface for exchanging information between agents, tools, and external services.\
It ensures that context — including task details, memory state, and tool usage parameters — is communicated in a consistent and structured format.

**Key Benefits of MCP in Crowe:**

* **Interoperability:** Enables agents to work with tools and services regardless of implementation details.
* **Consistency:** Ensures all participants in a workflow share the same context and state.
* **Scalability:** Makes it easier to integrate new tools or services without altering core agent logic.
* **Extensibility:** Developers can define custom MCP-compliant tools for domain-specific needs.

### **Tools & Integration in Crowe**

Crowe offers a robust toolkit for extending your AI agents with a wide range of tools — from simple callable functions to **Model Context Protocol (MCP)** services and standardized tool schemas. This guide walks through the available options and demonstrates how to integrate them into your workflows.

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#### **Installation**

```bash
pip install crowe
```

***

#### **Overview**

Crowe’s tooling system is designed to **amplify agent capabilities** by enabling seamless access to external services, computations, and data sources. The framework supports multiple integration patterns:

| **Tool Type**          | **Description**                                                                                                  |
| ---------------------- | ---------------------------------------------------------------------------------------------------------------- |
| **Callable Functions** | Direct integration of Python functions with type hints and docstrings for instant tool functionality.            |
| **MCP Services**       | Model Context Protocol servers that enable distributed tool execution across multiple services and environments. |
| **Tool Schemas**       | Structured, validated definitions for creating standardized tool interfaces.                                     |
| **Tool Collections**   | Pre-packaged tool bundles for common and enterprise use cases.                                                   |

***

#### **Using Callable Functions**

Callable functions are the **fastest and simplest** way to add custom tools to a Crowe agent. They are standard Python functions enhanced with clear typing and documentation.

**Step 1: Define the Tool Function**\
When creating your tool function:

* Include **type hints** for all parameters and return values.
* Write **comprehensive docstrings** covering `Args`, `Returns`, `Raises`, and `Examples`.
* Implement **error handling** for stability in production environments.

{% embed url="<https://github.com/modelcontextprotocol>" %}

### Implementation Guidelines

#### 1) Function & Interface Design

* **Strong Typing** – Always use type hints for every parameter and return value; use `Optional[...]` or default values for nullable parameters.
* **Comprehensive Docstrings** – Include **Args**, **Returns**, **Raises**, and **Examples** in your docstrings. Show both success and failure examples.
* **Early Input Validation** – Validate inputs at the very start of the function to avoid unnecessary computation.
* **Structured Outputs** – Prefer returning **JSON-serializable** objects (e.g., `dict`, `pydantic` models) over plain strings.
* **Granular Error Types** – Raise specific exception classes to make error handling predictable.

***

#### 2) MCP Server Development

* **Descriptive Tool Names** – Tool names should make it obvious what the tool does.
* **API Timeouts** – Always set reasonable timeouts for network calls to prevent hanging requests.
* **Graceful Failure** – Handle network errors, invalid data, and timeouts with clear messages.
* **Environment Configuration** – Keep API keys, ports, and secrets in environment variables (never hardcode).
* **Isolated Testing** – Test each tool independently before integrating it with the agent.

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#### 3) Agent Configuration

* **Controlled Looping** – Set `max_loops` based on the task’s complexity; avoid infinite loops unless explicitly required.
* **Token Limits** – Define sensible token caps to control cost and response size.
* **Clear System Prompts** – Explicitly describe tool capabilities and constraints in the prompt.
* **Meaningful Naming** – Name agents in a way that helps debugging and log analysis.
* **Tool Synergy** – Combine complementary tools to cover a broader range of tasks.

***

#### 4) Performance Optimization

* **Data Caching** – Cache frequent or expensive results when possible.
* **Connection Reuse** – Use connection pooling for APIs or databases to reduce latency.
* **Rate Limiting** – Respect API limits to avoid throttling or bans.
* **Execution Profiling** – Measure tool execution time and optimize bottlenecks.
* **Async Execution** – Use asynchronous calls when tools can run in parallel.

***

#### 5) Reliability & Monitoring

* **Audit Logging** – Log all tool calls, inputs, outputs, and errors for traceability.
* **Health Checks** – Provide readiness and liveness endpoints for production deployments.
* **Graceful Degradation** – Offer fallback responses if a tool or service is unavailable.
* **Error Recovery** – Implement retries with exponential backoff for transient failures.
