Real Monte Carlo simulation of a compound event/conditional probability. Paid via x402.
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Average 4.7/5 across 1 of 1 tools scored.
There is only one tool in this server, so there is no chance of confusion between tools. The tool's purpose is completely distinct by default.
The single tool name 'simulate_monte_carlo' follows a clear verb_noun pattern, matching the server's purpose. With only one tool, naming consistency is trivially maintained.
The server has just one tool, which is below the typical 3-15 range and feels thin. However, the tool is comprehensive and may justify its existence, making the count borderline appropriate.
The tool covers the full Monte Carlo simulation workflow, including multiple distributions, event expressions, conditional probability via rejection sampling, confidence intervals, and reproducibility. For the server's stated purpose, there are no obvious gaps.
simulate_monte_carloSimulate a compound event or conditional probability (Monte Carlo)AInspectActually draws random samples from real distributions and counts outcomes, instead of a model guess about a probability. Declare named random variables (uniform, normal, bernoulli, binomial, poisson, exponential, discrete), an "event" boolean expression over those variable names (e.g. "a > 0.5 && b == 1"), and an optional "condition" expression to estimate a conditional probability P(event | condition) by rejection sampling. Event/condition expressions are parsed and evaluated by a small built-in interpreter (arithmetic, comparisons, &&/||/!, min/max/abs) — no arbitrary code execution. Returns the estimated probability, a 95% confidence interval, and the seed used (pass the same seed back to reproduce the exact result). Costs $0.03 USDC (Base) per call.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | Optional PRNG seed for a reproducible run. If omitted, a random seed is generated and returned in the output. | |
| event | Yes | Boolean expression over the variable names, evaluated each trial (e.g. "a + b > 10", "x == 1 && y < 0.2"). | |
| trials | No | Number of trials to run. Default 10000, between 100 and 100000. | |
| condition | No | Optional boolean expression; if given, the result is P(event | condition), estimated only over trials where this is true. | |
| variables | Yes | Random variables to sample each trial. Max 10. |
| Name | Required | Description |
|---|---|---|
| error | Yes | Error or warning message. null if none. |
| valid | Yes | false if the input (variables, expressions, limits) was invalid. |
| seed_used | Yes | The PRNG seed used — pass it back as `seed` to reproduce this exact result. |
| trials_run | Yes | Number of trials actually simulated (0 if valid is false). |
| probability | Yes | Estimated P(event) or P(event | condition). null if invalid, or if condition matched zero trials. |
| standard_error | Yes | Estimated standard error of the probability estimate. |
| event_successes | Yes | Number of trials (or condition-matching trials) where event was true. |
| condition_successes | Yes | Number of trials where condition was true. null if no condition was given. |
| confidence_interval_95 | Yes | Approximate 95% confidence interval [low, high] via the normal approximation. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses rejection sampling for conditional probabilities, the interpreter's supported operations, and explicitly states 'no arbitrary code execution.' It also mentions the 95% confidence interval, seed reproducibility, and per-call cost, offering comprehensive behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is about five sentences, front-loaded with the core purpose and followed by usage, interpreter constraints, return values, and cost. Every clause contributes essential information without redundancy, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a rich input schema and an output schema, the description covers the tool's purpose, the modeling workflow, expression syntax, sampling approach, and cost. It mentions key outputs (probability, CI, seed) without detailing return fields, since the output schema handles that. The description is complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions, establishing a baseline of 3. The description adds value by explaining the expression interpreter's capabilities (arithmetic, comparisons, logical operators, min/max/abs) and giving example syntax 'a > 0.5 && b == 1', which goes beyond the schema's examples and clarifies the condition parameter's rejection sampling.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Actually draws random samples from real distributions and counts outcomes' which clearly identifies the tool as a Monte Carlo simulator distinct from analytic probability estimation. It specifies the distributions supported and the event/condition parameters, fully conveying what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the workflow (declare variables, write an event expression, optionally a condition) and contrasts with 'a model guess about a probability.' Although no siblings exist to compare against, it provides clear usage context but stops short of explicit 'when to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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