How to Use the Mapillary MCP in CrewAI

Q: Can CrewAI agents share Mapillary data?

Yes. Using the crew's shared memory, one agent can store getmapfeatures data for another agent to review. This facilitates team-based analysis.

Deploy autonomous monitoring crews in CrewAI to audit urban infrastructure with Mapillary.

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Connect Mapillary MCP to CrewAI

Create your Vinkius account to connect Mapillary 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 Mapillary with CrewAI

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Autonomous monitoring with CrewAI

Assign a dedicated agent to run `search_images` across your target city. The agent acts as a scout, finding the latest imagery for your crew to analyze. Once images are found, the agent uses `get_image` to grab the specific capture time. This helps your crew prioritize the most recent data for their reports.

Infrastructure analysis with CrewAI

Task your analysis agent with `get_map_features` to extract sign locations. The agent compares these locations against your internal GIS database. If the agent finds a discrepancy, it triggers `get_image_detections` to verify the visual evidence. This creates a closed-loop system for infrastructure maintenance.

Coordinate multi-agent crews with CrewAI

Use `search_sequences` to define the scope of work for your entire crew. Each agent can take a different segment of the sequence to process in parallel. Your moderator agent uses `get_sequence` to assemble the final report. This allows your team to cover thousands of miles without human input.

Setup guide

Set up Mapillary 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 Mapillary tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Mapillary 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="Mapillary Analyst",
        goal="Access and analyze Mapillary data via MCP.",
        backstory="Expert analyst with direct Mapillary access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Mapillary 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 →

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Common questions about Mapillary MCP in CrewAI

You simply pass the server URL to the agent's MCP list. Once connected, your agents can call any of the seven available tools.

Yes. Using the crew's shared memory, one agent can store `get_map_features` data for another agent to review. This facilitates team-based analysis.

You can set your agents to filter by confidence score. This ensures only high-quality detections are used in your final infrastructure reports.

It does. The server is compatible with SSE, making it ideal for the persistent connections required by CrewAI multi-agent crews.

Data is processed in an isolated sandbox environment. We only handle the public spatial coordinates and infrastructure labels returned by the API.

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