How to Use the PreciseFP MCP in CrewAI

Q: How do I configure PreciseFP tools for CrewAI agents?

Pass the Vinkius endpoint URL directly into the mcps array when defining your agent. The framework automatically discovers and loads all thirteen tools.

Q: Can I restrict which CrewAI agent uses PreciseFP?

Yes. Use MCPServerHTTP from crewai.mcp and apply a toolfilter. You can give your document agent access to createformengagement while restricting the research agent to getaccount.

Q: Do the tools work in sequential process mode?

They do. Agent A can run createperson and pass the new ID to Agent B, who immediately runs listpersons to verify the addition before proceeding.

Deploy specialized AI agents to manage client onboarding and PreciseFP data extraction with CrewAI.

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Create your Vinkius account to connect PreciseFP to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup PreciseFP with CrewAI

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CrewAI agents audit PreciseFP records

You don't need humans checking every field. Assign an auditor agent in CrewAI to run `list_accounts` and pull the entire client roster. The agent scans the output for missing KYC requirements or outdated profiles. When it finds a gap, it passes the account ID to a remediation agent. This second agent uses `get_person` to identify exactly who needs updating, then drafts an internal ticket. The MCP Server allows these specialized workers to collaborate directly on your firm's database.

Delegate client engagements via MCP Server

Managing high-net-worth onboarding requires coordination. Set up a manager agent that evaluates a new prospect's complexity. It delegates tasks to a document specialist agent, which queries `list_form_templates` and `list_pdf_templates`. The specialist selects the required tax and trust documents, then executes `create_form_engagement` to send them out. The manager monitors the entire sequence, ensuring the right forms hit the right inbox without an advisor lifting a finger.

Track account activity autonomously

Client behavior changes fast. A dedicated monitor agent can run `get_account_activity` on a schedule to watch for newly completed forms or profile updates. It acts as a permanent watchdog on your financial planning system. If a client finishes their paperwork, the monitor alerts an onboarding agent to pull the finalized data using `get_form_engagement`. Shared memory ensures the entire team knows the client is ready for the next phase of planning.

Setup guide

Set up PreciseFP 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 PreciseFP tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent PreciseFP 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 PreciseFP. 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 PreciseFP MCP in CrewAI

Pass the Vinkius endpoint URL directly into the `mcps` array when defining your agent. The framework automatically discovers and loads all thirteen tools.

Yes. Use `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. You can give your document agent access to `create_form_engagement` while restricting the research agent to `get_account`.

They do. Agent A can run `create_person` and pass the new ID to Agent B, who immediately runs `list_persons` to verify the addition before proceeding.

The agent receives the error string from the MCP Server. LLM routing allows the agent to read the error, adjust its parameters, and attempt the call again.

Agent memory is localized to the session. When an agent pulls portfolio balances or beneficiary names via `get_account`, the data routes through a strictly ephemeral V8 sandbox. Once the task finishes, the context window clears and nothing persists.

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