Prismic MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Prismic as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Prismic. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Prismic?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Prismic MCP Server
Connect your Prismic headless CMS to any AI agent and integrate content querying directly into your conversation workflow.
LlamaIndex agents combine Prismic tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Search Documents — Perform advanced searches using Prismic predicates, filter by tags, locales, and custom types
- Retrieve Content — Fetch full document data by their unique IDs to immediately get component architecture and copy
- Explore Schema — List all available custom types, tags, and languages defined in your repository
- Analyze Structure — Retrieve repository metadata including master refs and view specific query form schemas
The Prismic MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Prismic to LlamaIndex via MCP
Follow these steps to integrate the Prismic MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Prismic
Why Use LlamaIndex with the Prismic MCP Server
LlamaIndex provides unique advantages when paired with Prismic through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Prismic tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Prismic tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Prismic, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Prismic tools were called, what data was returned, and how it influenced the final answer
Prismic + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Prismic MCP Server delivers measurable value.
Hybrid search: combine Prismic real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Prismic to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Prismic for fresh data
Analytical workflows: chain Prismic queries with LlamaIndex's data connectors to build multi-source analytical reports
Prismic MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Prismic to LlamaIndex via MCP:
get_document_by_id
g., from a search result or relationship field) and need to retrieve its full content. Fetches a specific Prismic document by its unique ID
get_query_form_schema
Retrieves the schema for a specific query form (e.g., "everything")
get_repo_metadata
Retrieves metadata about the Prismic repository, including master refs, types, and languages
list_custom_types
Lists all Custom Types defined in the Prismic repository
list_documents_by_tag
Lists all Prismic documents that have a specific tag
list_documents_by_type
Lists all Prismic documents of a specific Custom Type
list_global_tags
Lists all tags used across the Prismic repository
list_i18n_languages
Lists the languages (locales) configured in the repository
query_prismic_documents
This is the most powerful tool for finding content. It supports pagination and locale filtering internally. Queries the Prismic API for documents using raw Predicates
search_filtered_locale
g., "en-us" or "fr-fr"). Performs a filtered search for documents within a specific locale
Example Prompts for Prismic in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Prismic immediately.
"List all custom types available in my Prismic repository."
"Can you fetch the document JSON for the ID 'ZbHwWxEAACUAx9'?"
"Search for all documents tagged with 'seo' and 'landing'."
Troubleshooting Prismic MCP Server with LlamaIndex
Common issues when connecting Prismic to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPrismic + LlamaIndex FAQ
Common questions about integrating Prismic MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Prismic with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Prismic to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.