How to Use the Clearscope MCP in LlamaIndex

Q: Does the Clearscope LlamaIndex setup support data access filtering?

You control exactly what goes into the index. Set includeresources=True when configuring the tool spec to allow direct data access. This lets the agent ingest full briefs via getbrief automatically.

Index live SERP data and NLP content grades directly into your LlamaIndex knowledge base with Clearscope.

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Connect Clearscope MCP to LlamaIndex

Create your Vinkius account to connect Clearscope 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 Clearscope with LlamaIndex

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Query the Clearscope MCP Server as a semantic index

RAG applications usually rely on static PDFs. Adding this Clearscope MCP Server changes the equation by letting LlamaIndex pull live SEO metrics. Your FunctionAgent calls `list_terms` and immediately embeds those NLP bounding variables into your vector store. Now your internal search answers questions based on real-time ranking requirements. The integration turns raw API responses into searchable context. When a user asks about content gaps, the system fetches `get_keyword_research`. It indexes those Cloud calculations tracking Google traffic limits alongside your company's style guides, creating a unified knowledge base.

Ground answers in active competitor data

Hallucinations kill content strategy. You stop them by forcing your agent to read actual search results. Using `list_competitors`, LlamaIndex grabs the precise active arrays spanning native SERP competitor links and loads them into memory. Your query engine then synthesizes that data instead of making up fake URLs. You can filter exactly what gets indexed. By applying the allowed_tools parameter, you restrict the agent to only running `get_report_details`. This keeps your vector store focused purely on structural extraction properties without pulling in unnecessary workspace noise.

Audit content hierarchies on the fly

Building a localized index of a specific topic requires knowing the structure. Your setup triggers `get_outline` to pull the string arrays resolving content hierarchies. LlamaIndex chunks this data perfectly because the structural extraction is already formatted for semantic parsing. Then you grade the output. The agent runs `grade_content` and stores the resulting structural matching scores. You can query your own application weeks later to see exactly how a specific draft performed against mapped NLP bounds before it was published.

Setup guide

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

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Common questions about Clearscope MCP in LlamaIndex

Grab the llama-index-tools-mcp package via pip. You set up a BasicMCPClient with your Vinkius URL and wrap it in an McpToolSpec. That spec converts everything into async tools your agent can read.

It handles cross-workspace queries easily. The agent executes `list_workspaces` to retrieve root identity mappings. It then loops through those containers to index reports from different organizational units.

You control exactly what goes into the index. Set include_resources=True when configuring the tool spec to allow direct data access. This lets the agent ingest full briefs via `get_brief` automatically.

You just re-run the tool calls. The MCP integration fetches fresh bounds and frequencies. Your agent updates the vector store with the new metrics so your RAG application stays current.

Vinkius processes everything inside an ephemeral zero-trust sandbox. The server reads your NLP scores and SERP outlines, passes them to your client, and immediately wipes the instance. Nothing persists on our infrastructure.

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