LinearB MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LinearB 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 LinearB. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in LinearB?"
)
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 LinearB MCP Server
Connect your LinearB account to any AI agent to automate your engineering intelligence and DORA metrics reporting. This MCP server enables your agent to query cycle time, track deployments, and report incidents directly from natural language interfaces.
LlamaIndex agents combine LinearB tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Metric Ingestion — Query complex engineering metrics including cycle time, coding time, and pickup time across teams
- Deployment Management — Inform LinearB of new software releases by reporting Git refs (SHAs or tags) programmatically
- Incident Tracking — Report and list engineering incidents to maintain accurate Change Failure Rate and MTTR metrics
- Metadata Oversight — List teams and connected repositories to map technical IDs to organizational structures
- DORA Analytics — Retrieve aggregated performance data to identify bottlenecks in your delivery pipeline
The LinearB MCP Server exposes 7 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 LinearB to LlamaIndex via MCP
Follow these steps to integrate the LinearB 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 7 tools from LinearB
Why Use LlamaIndex with the LinearB MCP Server
LlamaIndex provides unique advantages when paired with LinearB through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LinearB tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LinearB tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LinearB, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LinearB tools were called, what data was returned, and how it influenced the final answer
LinearB + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LinearB MCP Server delivers measurable value.
Hybrid search: combine LinearB real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LinearB 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 LinearB for fresh data
Analytical workflows: chain LinearB queries with LlamaIndex's data connectors to build multi-source analytical reports
LinearB MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect LinearB to LlamaIndex via MCP:
list_connected_repos
List all connected repositories
list_engineering_teams
List all teams defined in LinearB
list_software_deployments
List recent deployments
list_software_incidents
List engineering incidents
query_software_metrics
Requires a JSON body with requested_metrics and time_ranges. Query software engineering metrics (v2)
record_new_deployment
Requires repo_id and ref. Report a new deployment to LinearB
record_new_incident
Requires provider_id and started_at. Report a new incident
Example Prompts for LinearB in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with LinearB immediately.
"Query the average cycle_time for the last 30 days for team 'Backend'."
"Record a new deployment for repo ID '123' with Git ref 'v1.2.0'."
"Report a new incident starting now for provider 'OpsGenie'."
Troubleshooting LinearB MCP Server with LlamaIndex
Common issues when connecting LinearB to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLinearB + LlamaIndex FAQ
Common questions about integrating LinearB 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 LinearB 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 LinearB to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.