How to Use the AssessTEAM MCP in LlamaIndex

Q: How does LlamaIndex index data from the AssessTEAM MCP Server?

You use the McpToolSpec to wrap the client. LlamaIndex calls the tools, like listprojects, and embeds the JSON output into a vector store, making your AssessTEAM API data searchable.

Q: How do I query employee evaluations from AssessTEAM with a LlamaIndex agent?

First, index the output of listperformanceevaluations. Then, you can use a LlamaIndex query engine to ask questions in natural language. The engine finds the relevant documents in your index and generates a summary.

Q: Does the AssessTEAM integration support tool filtering for LlamaIndex?

Yes. When you configure the tool spec, you can use the allowedtools filter to expose only specific functions from the AssessTEAM MCP server, like getprofitabilityreport, to your agent.

Turn your AssessTEAM performance and project data into a searchable knowledge base with LlamaIndex.

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Connect AssessTEAM MCP to LlamaIndex

Create your Vinkius account to connect AssessTEAM to LlamaIndex 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 AssessTEAM with LlamaIndex

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Index Your Team's Performance History

The `list_performance_evaluations` and `list_employees` tools feed data directly into a LlamaIndex vector store. This builds a private, queryable history of your team's reviews and goals. It’s your data, structured for search, and a powerful feature of this MCP server. Now you can ask plain-English questions like "Summarize the key feedback for the design team in Q2." LlamaIndex finds the relevant evaluations from the AssessTEAM data and synthesizes an answer grounded in facts.

Create a Live Project Knowledge Base

This MCP server lets you fetch project information and index it automatically. Set up a schedule to call `list_projects` and `get_profitability_report`, keeping your LlamaIndex knowledge base current with the latest data from AssessTEAM. Your RAG application can then answer complex business questions. Ask "Which projects are over budget but have high team engagement?" and get an answer that connects financial data with performance metrics.

Query Timesheets using LlamaIndex

The `list_timesheets` tool lets you pull raw time tracking data. By indexing this output with LlamaIndex, you create a semantic search layer over your team's work logs. No more clunky report builders. This setup lets you ask natural language questions like "How much time did the backend team spend on bug fixes last month?" The agent queries your indexed timesheet data to find the answer instantly.

Setup guide

Set up AssessTEAM MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all AssessTEAM MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to AssessTEAM tools.",
)
response = await agent.run("List recent AssessTEAM data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AssessTEAM. 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 AssessTEAM MCP in LlamaIndex

You use the `McpToolSpec` to wrap the client. LlamaIndex calls the tools, like `list_projects`, and embeds the JSON output into a vector store, making your AssessTEAM API data searchable.

Yes, that's exactly what this is for. Your RAG app queries the index built from AssessTEAM tools, ensuring its answers about project profitability or performance are based on up-to-date data.

First, index the output of `list_performance_evaluations`. Then, you can use a LlamaIndex query engine to ask questions in natural language. The engine finds the relevant documents in your index and generates a summary.

Yes. When you configure the tool spec, you can use the `allowed_tools` filter to expose only specific functions from the AssessTEAM MCP server, like `get_profitability_report`, to your agent.

Vinkius manages the connection and authentication. Your project profitability and timesheet data is fetched via a zero-trust transport managed by the MCP platform, and access is tied to your unique Vinkius token.

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