How to Use the COR MCP in LlamaIndex

Q: Does the COR MCP server support filtered data for LlamaIndex?

You can use the allowedtools filter to restrict which functions your agent can trigger. This ensures your knowledge base only contains the data you want to index.

Index agency project data into LlamaIndex using the COR MCP Server to ground your RAG applications in live numbers.

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Connect COR MCP to LlamaIndex

Create your Vinkius account to connect COR 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 COR with LlamaIndex

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Build a knowledge base with LlamaIndex

Convert `list_cor_tasks` and `list_cor_time_entries` outputs into vector embeddings. LlamaIndex stores this data so your agents query actual project history instead of guessing. When you ask about profitability, your agent retrieves indexed results from previous months. It provides answers based on recorded time entries, not hallucinations.

Query project details with LlamaIndex

Use `get_cor_task` to pull specific task requirements into your local knowledge index. You can correlate task types with historical margin analysis to improve future estimates. Your RAG pipeline combines these API results with internal documentation. This gives your agent a complete picture of why a project might be trending over budget.

Automate resource reporting in LlamaIndex

Index `list_cor_team_members` to keep your internal contact directory current. Your agent references this data when it needs to route questions about specific team members. This keeps your LlamaIndex knowledge base synced with the platform. You always know who is assigned to which active project.

Setup guide

Set up COR 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 COR 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 COR tools.",
)
response = await agent.run("List recent COR data")

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Common questions about COR MCP in LlamaIndex

Use the MCP tool spec to convert server outputs into a format your indexer accepts. You can then pipe the tool list directly into your function agent.

Absolutely, as long as you have indexed the output from the list tools. You can query past sessions and get grounded answers about project performance.

You can use the allowed_tools filter to restrict which functions your agent can trigger. This ensures your knowledge base only contains the data you want to index.

Your agency data resides in a Vinkius-managed sandbox. The MCP server acts as a secure pipe between the platform and your local index.

We isolate your time entries and project records in a private environment. Only your authenticated client accesses these specific data points.

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