How to Use the DictionaryAPI.dev MCP in CrewAI

Q: Where do the word queries sent by CrewAI agents get processed?

Every lookup string sent to getworddefinition is processed within our secure, zero-trust V8 sandbox. The data is processed in memory, and we do not store the English words or definitions queried by your agents.

Equip your specialized CrewAI agent teams with real-time English definitions and phonetics.

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Connect DictionaryAPI.dev MCP to CrewAI

Create your Vinkius account to connect DictionaryAPI.dev to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup DictionaryAPI.dev with CrewAI

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Equip specialized CrewAI teams with dictionary tools

In a multi-agent setup, specialized roles make all the difference. You can assign the `get_word_definition` tool exclusively to your researcher agent, while your editor agent focuses on style. This keeps your agents focused on their specific tasks without getting bogged down by unnecessary tools. CrewAI handles the delegation. When the editor agent encounters an unfamiliar term, it passes the word to the researcher, who queries this MCP Server to get the exact definition, phonetics, and origin.

Shared memory for vocabulary lookups

Avoid redundant API calls across your agent crew. CrewAI's shared memory features allow your agents to remember words they have already looked up using `get_word_definition` via the MCP Server. If Agent A checks the definition of a word, Agent B can access that definition directly from short-term memory. This speeds up your entire execution pipeline and keeps your API usage down.

Hierarchical execution for content validation

Set up a manager agent to oversee your content creation crew. The manager can review draft outputs and command a subordinate agent to run `get_word_definition` whenever a word seems out of place or overly complex. This hierarchical structure ensures that your final output is accurate and polished. The MCP Server acts as the source of truth, giving your agents objective linguistic data to settle vocabulary disputes.

Setup guide

Set up DictionaryAPI.dev MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke DictionaryAPI.dev tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="DictionaryAPI.dev Analyst",
    goal="Access and analyze DictionaryAPI.dev data via MCP.",
    backstory="Expert analyst with direct DictionaryAPI.dev access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent DictionaryAPI.dev transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="DictionaryAPI.dev Analyst",
        goal="Access and analyze DictionaryAPI.dev data via MCP.",
        backstory="Expert analyst with direct DictionaryAPI.dev access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent DictionaryAPI.dev transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DictionaryAPI.dev. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about DictionaryAPI.dev MCP in CrewAI

You assign the tool directly to that specific agent's definition. Instead of passing the MCP Server to the entire crew, only add the `get_word_definition` tool to the researcher agent's tool list during initialization.

Yes, CrewAI has built-in caching for tools. When an agent calls `get_word_definition` for a specific word, the result is cached locally, preventing subsequent agents from hitting the API for the same word.

The agent executing the task will receive the error message from the `get_word_definition` tool. CrewAI agents are designed to self-correct, meaning they will try to rephrase the query or proceed using existing context.

No extra Python packages are needed for the server itself since Vinkius hosts it. You only need the standard crewai library to connect your agents to our hosted endpoint.

Every lookup string sent to `get_word_definition` is processed within our secure, zero-trust V8 sandbox. The data is processed in memory, and we do not store the English words or definitions queried by your agents.

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