Percy 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 Percy 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 Percy. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Percy?"
)
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 Percy MCP Server
Inject precision quality assurance workflows directly bounding LLM models via the Percy Visual Testing API (by BrowserStack). Programmatically verify pixel regressions executing queries evaluating visual boundaries natively across target projects. Inspect deep status arrays parsing CI build limits dynamically, extract metrics evaluating granular snapshot checkpoints asynchronously, and force immediate test baseline approvals seamlessly directly from explicit prompt commands naturally.
LlamaIndex agents combine Percy 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
- Project Navigation — Read bounded parameters tracking Percy deployments isolating configurations determining explicitly specific active QA targets natively
- Automated Build Oversight — Track specific arrays extracting dynamic checks returning pipeline checkpoints (approved/failed/unreviewed limits) explicitly seamlessly
- Visual Snapshot Operations — Log natively extracting bounds verifying comparison properties logging rendering differences mapping exact explicit width constraints
- Baseline Affirmations — Mutate bounding loops forcing active execution of JSON logic structurally bypassing native clicks allowing test approvals implicitly (
approve_buildorapprove_snapshot)
The Percy 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 Percy to LlamaIndex via MCP
Follow these steps to integrate the Percy 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 Percy
Why Use LlamaIndex with the Percy MCP Server
LlamaIndex provides unique advantages when paired with Percy through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Percy tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Percy tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Percy, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Percy tools were called, what data was returned, and how it influenced the final answer
Percy + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Percy MCP Server delivers measurable value.
Hybrid search: combine Percy real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Percy 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 Percy for fresh data
Analytical workflows: chain Percy queries with LlamaIndex's data connectors to build multi-source analytical reports
Percy MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Percy to LlamaIndex via MCP:
approve_build
/approve` moving the test suite to green. Approve all unreviewed snapshots in a Percy build. Marks the entire build as visually approved for deployment
approve_snapshot
Approve a single Percy snapshot. Marks it as visually correct, updating the baseline for future comparisons
get_build_details
Get full details of a Percy build including state, total/unreviewed snapshot counts, approved/rejected snapshots, branch, commit SHA, and finalized timestamp
get_project_details
Get full details of a Percy project including name, slug, default branch, auto-approve enabled, browser targets, and build count
get_snapshot_details
Get full details of a Percy snapshot including name, review state, widths, fingerprint, and comparison count
list_browsers
List all supported browser families on Percy. Returns browser names, versions, and OS combinations for cross-browser visual testing
list_builds
List builds for a Percy project. Each build contains snapshots from a test run. Returns build IDs, states (processing/finished/failed), branch names, commit SHAs, and snapshot counts
list_comparisons
List visual comparisons for a Percy snapshot. Each comparison shows the diff between baseline and head at a specific width/browser. Returns diff images, diff percentages, and browser info
list_projects
List all projects on Percy (BrowserStack). Percy is the leading visual regression testing platform that captures snapshots and detects pixel-level UI differences across builds. Uses JSON:API format. Returns project names, slugs, and browser configs
list_snapshots
List snapshots in a Percy build. Each snapshot is a captured page/component at specific widths and browsers. Returns snapshot names, review states (unreviewed/approved/rejected), and diff percentages
Example Prompts for Percy in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Percy immediately.
"Log explicitly the builds targeting structural limits seamlessly isolating project 'org-slug/my-app' dynamically checking bounding states natively."
"Reverse check explicit structures extracting limits comparing properties cleanly bounding snapshot ID 'snap_778' natively efficiently."
"Force explicit validation mutating boundaries executing structurally an approval across build ID '8910' automatically natively flawlessly securely."
Troubleshooting Percy MCP Server with LlamaIndex
Common issues when connecting Percy to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPercy + LlamaIndex FAQ
Common questions about integrating Percy 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 Percy 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 Percy to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.