Internet Archive Metadata 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 Internet Archive Metadata as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Internet Archive Metadata. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Internet Archive Metadata?"
)
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 Internet Archive Metadata MCP Server
Connect Internet Archive Metadata to any AI agent and retrieve comprehensive details about any archived item — including file listings, user reviews, collection memberships, access statistics, and modification history.
LlamaIndex agents combine Internet Archive Metadata 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
- Complete Metadata — Title, creator, date, description, subjects, license, language
- File Listings — All downloadable files with formats (PDF, EPUB, MP4, MP3) and sizes
- User Reviews — Community ratings and review text
- Collection Info — Which collections the item belongs to
- View Statistics — Download and view counts
- Modification History — Track changes made to items over time
- Parent Collections — Hierarchical categorization structure
- Derivative Files — Auto-generated thumbnails, streaming files, OCR text
- Lightweight Lookup — Metadata-only mode for fast queries
- Server Info — Storage location and hosting details
The Internet Archive Metadata 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 Internet Archive Metadata to LlamaIndex via MCP
Follow these steps to integrate the Internet Archive Metadata 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 Internet Archive Metadata
Why Use LlamaIndex with the Internet Archive Metadata MCP Server
LlamaIndex provides unique advantages when paired with Internet Archive Metadata through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Internet Archive Metadata tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Internet Archive Metadata tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Internet Archive Metadata, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Internet Archive Metadata tools were called, what data was returned, and how it influenced the final answer
Internet Archive Metadata + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Internet Archive Metadata MCP Server delivers measurable value.
Hybrid search: combine Internet Archive Metadata real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Internet Archive Metadata 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 Internet Archive Metadata for fresh data
Analytical workflows: chain Internet Archive Metadata queries with LlamaIndex's data connectors to build multi-source analytical reports
Internet Archive Metadata MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Internet Archive Metadata to LlamaIndex via MCP:
get_collections
Items can belong to multiple collections (e.g., "prelinger", "opensource_movies"). Use this to understand the categorization of an item. Get collections an item belongs to
get_derivatives
). These are derived from the original uploads. Use this to see what processed formats are available. Get auto-generated derivative files for an item
get_files
Files can be downloaded from: https://archive.org/download/{identifier}/{filename}. Use this to see what formats are available. Get all downloadable files for an Internet Archive item
get_history
Use this to track changes to an item over time. Get modification history of an Internet Archive item
get_metadata
Returns title, creator, date, description, subjects, collection, files, reviews, and stats. The identifier is found in item URLs (e.g., from archive.org/details/big_buck_bunny, identifier is "big_buck_bunny"). Use this for comprehensive item information. Get complete metadata for an Internet Archive item
get_metadata_only
Lighter response for quick lookups. Use this when you only need basic item information. Get only the metadata fields without files or reviews
get_parents
Use this to understand the broader categorization structure. Get parent collections of an Internet Archive item
get_reviews
Returns reviewer names, star ratings, and review text. Not all items have reviews. Use this to see community feedback. Get user reviews for an Internet Archive item
get_server_info
Useful for understanding where files are hosted. Use this for technical diagnostics. Get server and storage information for an item
get_stats
Shows how popular the item is. Use this to measure item popularity. Get access statistics for an Internet Archive item
Example Prompts for Internet Archive Metadata in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Internet Archive Metadata immediately.
"Get metadata for item big_buck_bunny."
"List all files for item gutenberg_etext1."
"Get reviews for item nasa_apollo11."
Troubleshooting Internet Archive Metadata MCP Server with LlamaIndex
Common issues when connecting Internet Archive Metadata to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpInternet Archive Metadata + LlamaIndex FAQ
Common questions about integrating Internet Archive Metadata 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 Internet Archive Metadata 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 Internet Archive Metadata to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.