Felt (Collaborative Maps) MCP Server for CrewAIGive CrewAI instant access to 11 tools to Add Elements, Create Layer, Create Map, and more

Name: Felt (Collaborative Maps)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Connect your CrewAI agents to Felt (Collaborative Maps) through Vinkius, pass the Edge URL in the `mcps` parameter and every Felt (Collaborative Maps) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The Felt (Collaborative Maps) MCP Server for CrewAI is a standout in the Collaboration category — giving your AI agent 11 tools to work with, ready to go from day one.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Felt (Collaborative Maps) Specialist",
    goal="Help users interact with Felt (Collaborative Maps) effectively",
    backstory=(
        "You are an expert at leveraging Felt (Collaborative Maps) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Felt (Collaborative Maps) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

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

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About Felt (Collaborative Maps) MCP Server

Connect Felt to your AI agent to take full control of your collaborative mapping workflows through natural conversation. This server allows you to manage maps, layers, and geographic elements without leaving your workspace.

When paired with CrewAI, Felt (Collaborative Maps) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Felt (Collaborative Maps) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Map Management — List all accessible maps, create new ones with specific viewports, and retrieve detailed metadata or delete maps.
Data Uploads & Layers — Create layers by uploading geographic data (GeoJSON, CSV, KML) via public URLs and monitor their processing status.
Dynamic Styling — Update layer names and apply complex visual styles using the Felt Style Object (FSO) programmatically.
Element Manipulation — Add, update, or delete specific geographic features like points, lines, and polygons within your map layers.
Spatial Analysis Context — Fetch map and layer details to provide your AI with the necessary context for spatial reasoning.

The Felt (Collaborative Maps) MCP Server exposes 11 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 11 Felt (Collaborative Maps) tools available for CrewAI

When CrewAI connects to Felt (Collaborative Maps) through Vinkius, your AI agent gets direct access to every tool listed below — spanning gis, mapping, spatial-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

add

Add elements on Felt (Collaborative Maps)

Add elements to a Felt layer

create

Create layer on Felt (Collaborative Maps)

Supports GeoJSON, CSV, KML, Shapefiles, etc. Create a layer (Upload Data) to a Felt map

create

Create map on Felt (Collaborative Maps)

Create a new Felt map

delete

Delete element on Felt (Collaborative Maps)

Delete a Felt element

delete

Delete layer on Felt (Collaborative Maps)

Delete a Felt layer

delete

Delete map on Felt (Collaborative Maps)

Delete a Felt map

get

Get layer on Felt (Collaborative Maps)

Get details for a specific Felt layer

get

Get map on Felt (Collaborative Maps)

Get details for a specific Felt map

list

List maps on Felt (Collaborative Maps)

List Felt maps

update

Update element on Felt (Collaborative Maps)

Update a Felt element

update

Update layer on Felt (Collaborative Maps)

Update a Felt layer

Connect Felt (Collaborative Maps) to CrewAI via MCP

Follow these steps to wire Felt (Collaborative Maps) into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 11 tools from Felt (Collaborative Maps)

Why Use CrewAI with the Felt (Collaborative Maps) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Felt (Collaborative Maps) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Felt (Collaborative Maps) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Felt (Collaborative Maps) MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Felt (Collaborative Maps) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Felt (Collaborative Maps), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Felt (Collaborative Maps) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Felt (Collaborative Maps) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Felt (Collaborative Maps) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Felt (Collaborative Maps) immediately.

"List all my current Felt maps."

"Create a new map titled 'Project Alpha' centered on San Francisco."

"Add a point element to layer `layer_abc` at [ -122.4, 37.8 ]."

Troubleshooting Felt (Collaborative Maps) MCP Server with CrewAI

Common issues when connecting Felt (Collaborative Maps) to CrewAI through Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Felt (Collaborative Maps) + CrewAI FAQ

Common questions about integrating Felt (Collaborative Maps) MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.