Bring Railway Management
to LlamaIndex
Learn how to connect Cedar AI to LlamaIndex and start using 12 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the Cedar AI MCP Server?
Connect your Cedar AI railway management account to any AI agent and simplify how you coordinate rail operations, track car movements, and manage logistics documentation through natural conversation.
What you can do
- Inventory Management — List all railcars currently in your facility and retrieve detailed metadata and status for individual units.
- Car Movement Tracking — Record placements (setouts) and removals (pickups) of railcars at specific locations or tracks.
- Logistics Documentation — List and query waybills to understand shipping instructions, routes, and commodity data.
- Work Order Control — Manage the lifecycle of movement instructions by listing and updating work orders and associated tasks.
- Consist Coordination — Record train arrivals and departures to keep your inventory and operations synchronized.
- Status Maintenance — Update railcar tags and conditions (e.g., Bad Order, Empty/Loaded) directly via AI commands.
How it works
1. Subscribe to this server
2. Enter your Cedar AI API Key (found in your developer settings)
3. Start managing your railway ecosystem from Claude, Cursor, or any MCP client
Who is this for?
- Railroad Operators & Terminal Managers — quickly check yard inventory and record car movements via simple AI queries.
- Logistics Coordinators — monitor waybills and manage work orders across different tracks directly from the workspace.
- Fleet Managers — track railcar statuses and conditions to optimize equipment availability via the AI assistant.
Built-in capabilities (12)
Record train arrival
Record train departure
Get details for a specific railcar
Get details for a specific waybill
Get details for a specific work order
List railcars currently in inventory
List waybills
List work orders
Record removal of cars
Record placement of cars
g., Bad Order, Clean, Loaded/Empty). Update status of a railcar
Update a work order
Why LlamaIndex?
LlamaIndex agents combine Cedar AI tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.
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Data-first architecture: LlamaIndex agents combine Cedar AI tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Cedar AI tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Cedar AI, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Cedar AI tools were called, what data was returned, and how it influenced the final answer
Cedar AI in LlamaIndex
Cedar AI and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect Cedar AI to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Cedar AI in LlamaIndex
The Cedar AI 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. All 12 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Cedar AI for LlamaIndex
Every tool call from LlamaIndex to the Cedar AI MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I record a railcar movement via AI?
Yes! Use the setout_cars tool to record placement or pickup_cars for removal. Provide the location name and a list of railcar IDs to log the movement instantly.
How do I see the latest waybills for my shipments?
Run the list_waybills query. The agent will retrieve a history of active and completed shipping instructions, including route details and commodity info.
Is it possible to check the status of a specific work order via AI?
Absolutely. Use the get_work_order_details tool with the Work Order ID to retrieve the current status, assigned tasks, and completion progress.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Cedar AI tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp