How to Use the Apperio MCP in CrewAI
Deploy autonomous agent crews to manage Apperio legal spend, monitor matters, and process invoices with CrewAI.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Apperio MCP to CrewAI
Create your Vinkius account to connect Apperio 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.
A Crew for Financial Oversight
Assemble a team of agents to manage your Apperio account. An 'Auditor' agent can use `list_invoices` to find new bills. A 'Clerk' agent can then use `get_invoice_details` to verify line items against matter records fetched with `get_matter_header`. If an invoice looks right, the Clerk agent can pass it to a 'Manager' agent with the authority to call `approve_invoice`. If there's a problem, it can call `reject_invoice` and notify a human. CrewAI lets you define these roles and their interactions.
Autonomous Matter Administration
Keep your legal matters perfectly organized without lifting a finger. A 'Librarian' agent's only job is to periodically run `list_matters` and `list_matter_tags`. When it finds a matter without the right tags, it passes the information to an 'Organizer' agent. The Organizer analyzes the matter details and applies the correct labels using `tag_matter`. This division of labor is what makes CrewAI so effective.
Connect Your CrewAI MCP Server
Getting your crew connected to Apperio is fast. Just pass the Vinkius MCP Server URL into your Agent's `mcps` parameter. Your agents instantly get access to all the tools they need to interact with Apperio. For more control, you can use `MCPServerHTTP` with a `tool_filter`. This lets you create specialized agents. For example, you can build a 'Read-Only' agent that can only call `list_invoices`, but not `approve_invoice`.
Set up Apperio MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. 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
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke Apperio tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="Apperio Analyst",
goal="Access and analyze Apperio data via MCP.",
backstory="Expert analyst with direct Apperio access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent Apperio transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
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="Apperio Analyst",
goal="Access and analyze Apperio data via MCP.",
backstory="Expert analyst with direct Apperio access.",
tools=mcp_tools,
)
task = Task(
description="List recent Apperio transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Apperio. 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 Apperio MCP in CrewAI
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