How to Use the Ebby MCP in LlamaIndex

Q: How do I register Ebby tools in LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Ebby server URL. Wrap it in a McpToolSpec and call totoollistasync to get the list for your LlamaIndex FunctionAgent.

Q: Can LlamaIndex search Ebby transcripts by filename?

Yes, the LlamaIndex agent can call the Ebby tool searchtranscriptionsbyname to find specific files before reading their content. This lets you restrict your LlamaIndex vector queries to a single audio file.

Q: How does LlamaIndex handle Ebby speaker metadata?

LlamaIndex calls the Ebby tool listtranscriptionspeakers to fetch the speaker list. You can inject these Ebby speaker names as metadata tags into your LlamaIndex document nodes for better filtering.

Q: Can I filter active jobs using llama-index-tools-mcp?

Yes, your LlamaIndex agent can run the Ebby tool listlatesttranscriptions to find recently updated files. It can ignore files still flagged in listinprogresstranscriptions until they finish. This keeps your LlamaIndex indexing loop clean.

Index Ebby audio transcripts directly into LlamaIndex vector stores using this MCP Server.

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Connect Ebby MCP to LlamaIndex

Create your Vinkius account to connect Ebby 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 Ebby with LlamaIndex

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Build LlamaIndex RAG pipelines from Ebby files

Stop querying raw audio files when you can index Ebby transcripts directly into LlamaIndex. Your LlamaIndex agent calls `list_successfully_processed_audio` to find completed files, retrieves the raw content with `get_transcription_text`, and feeds it straight into your LlamaIndex vector index. Instead of guessing what was said in the audio, you query your LlamaIndex knowledge base. LlamaIndex matches your query against actual indexed Ebby transcripts, ensuring your agent answers with grounded facts rather than hallucinated summaries.

Semantic search in LlamaIndex via MCP Server metadata

Let your LlamaIndex agent organize your Ebby audio archive automatically. By calling `search_transcriptions_by_name` and `list_transcription_speakers`, the LlamaIndex agent extracts both the file structure and the speaker identities to tag your LlamaIndex document nodes. When you query LlamaIndex about who discussed a specific topic, the engine uses these Ebby tags to filter the vector space. It pulls the exact text blocks from `get_transcription_text` where that Ebby speaker was active.

Audit Ebby audio volume before LlamaIndex indexing

Your LlamaIndex pipeline uses `quick_transcription_volume_audit` to assess the scale of your Ebby audio library before running a bulk ingestion job over MCP. This keeps your LlamaIndex vector database clean and prevents bloated index sizes. If the Ebby audit shows pending files, the LlamaIndex agent checks `list_in_progress_transcriptions` to delay indexing until those files are ready. You only consume LlamaIndex embedding tokens for completed, high-quality Ebby transcription data.

Setup guide

Set up Ebby 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 Ebby 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 Ebby tools.",
)
response = await agent.run("List recent Ebby 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 Ebby. 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 Ebby MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Ebby server URL. Wrap it in a `McpToolSpec` and call `to_tool_list_async` to get the list for your LlamaIndex `FunctionAgent`.

Yes, the LlamaIndex agent can call the Ebby tool `search_transcriptions_by_name` to find specific files before reading their content. This lets you restrict your LlamaIndex vector queries to a single audio file.

LlamaIndex calls the Ebby tool `list_transcription_speakers` to fetch the speaker list. You can inject these Ebby speaker names as metadata tags into your LlamaIndex document nodes for better filtering.

Yes, your LlamaIndex agent can run the Ebby tool `list_latest_transcriptions` to find recently updated files. It can ignore files still flagged in `list_in_progress_transcriptions` until they finish. This keeps your LlamaIndex indexing loop clean.

Your Ebby speaker lists and transcribed text are processed locally within your LlamaIndex pipeline. The Vinkius MCP Server handles the Ebby API keys securely, never exposing your raw transcripts to external third parties.

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