MCP server

QueueSim Official

com.queuesim/public

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Run M/M/c queue simulations and four scenarios (call center, ER, coffee shop, single server).

Working at last checkVerified publisher: queuesim.comAbout 4,573 tokens per request once connected11 tools

Source

https://queuesim.com/mcp/v1

At a glance

Type: MCP server
Publisher: queuesim.com (verified by the MCP Registry)
Status: Working at last check
Tools: 11
Token cost: About 4,573 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http public https://queuesim.com/mcp/v1

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.public]
url = "https://queuesim.com/mcp/v1"

Gemini CLI

{"mcpServers":{"public":{"httpUrl":"https://queuesim.com/mcp/v1"}}}

Cursor

{"mcpServers":{"public":{"url":"https://queuesim.com/mcp/v1"}}}

VS Code

{"servers":{"public":{"type":"http","url":"https://queuesim.com/mcp/v1"}}}

Antigravity

agy mcp add public https://queuesim.com/mcp/v1

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"public","server_url":"https://queuesim.com/mcp/v1","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"public","server_url":"https://queuesim.com/mcp/v1"}

Tools

compare_analytical_vs_simulated ~466 tokens
Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge o
compare_separate_vs_pooled ~505 tokens
Run the classic operations-research teaching demo: pooled queueing (one shared queue, c servers) vs separate queues (c independent queues, one server each, λ/c traffic to each). Both runs have identical total capacity (c × μ) and identical total arrivals (λ), so the offered load ρ is the same; the o
describe_scenario ~140 tokens
Return full details for one preset scenario: title, description, teaching note, peak parameters, and per-hour arrival + staffing arrays. Use this before simulate_scenario to understand the default shape and what overrides make sense.
explain_advanced_patterns ~163 tokens
Return a textbook-level description of six queueing complexity patterns beyond basic M/M/c: abandonment/reneging, priority tiers, overflow routing, skills-based routing, compound service, and server outages. Use this when the user describes real-world complexity (customers hanging up, VIP queues, sp
explain_queueing_theory ~108 tokens
Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.
interpret_result ~386 tokens
Given an M/M/c configuration (arrivalRate, serviceRate, servers) and optionally an observed average wait, returns a queueing-theory framed interpretation: where you sit on the utilization curve, what ρ means in plain language, what one more or fewer server would qualitatively do, and which complexit
list_scenarios ~112 tokens
List the four pre-built QueueSim scenarios. Returns key, title, and one-line description for each (Single Server, Coffee Shop, Grocery Checkout, Call Center). Call this when the user's problem matches one of the preset shapes — use describe_scenario for more detail and simulate_scenario to run one.
recommend_staffing ~661 tokens
INVERSE of simulate_mmc — given an arrival rate, service rate, and a target average wait time, returns the SMALLEST number of servers needed to meet the target. Use this when the user asks 'how many servers do I need?' / 'what staffing keeps wait under N minutes?'. The tool runs a binary search over
simulate_mmc ~723 tokens
Run a generic M/M/c queue simulation. Provide an arrival rate (λ, arrivals/hour), a service rate per server (μ, customers/hour each server can finish), and a server count (c). Optional: distribution shapes, service coefficient of variation, run length. Returns per-hour metrics and an overall summary
simulate_scenario ~691 tokens
Run one of the four preset scenarios (single, coffee, grocery, callcenter) with optional overrides. Overrides apply UNIFORMLY across open hours — e.g. setting servers=5 on 'coffee' replaces the 4/6/4 staffing pattern with a flat 5 during open hours (closed hours stay at zero). Use this for (a) faith
simulate_schedule ~618 tokens
Run a queueing simulation against an arbitrary 24-hour staffing schedule. Take this when the user describes a custom day shape that doesn't match a preset (e.g., 'my coffee shop is open 6am–10pm with 4 baristas off-peak, 7 at the 8am rush, 5 at the 4pm rush'). Inputs: `arrivalRates` (24-element arra

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