io.github.cogswellspacely8-star/simengine
PYPI · SIMENGINE-MCP · SCANNED AUG 3
Monte Carlo decision-intelligence over MCP: simulate, optimize, and decide under uncertainty.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →
Supply Chain Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (Apache-2.0).Pass
- Actively maintained (last published 22 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability0
- Schema quality not yet verified: our sandbox run of this package did not complete, so we have no schema to assess.Unverified
Stability & Change Management0
- Stability not yet verified: our sandbox run of this package did not complete, so we have no schema to compare.Unverified
Tool Coverage0
- Tool coverage not yet verified: our sandbox run of this package did not complete, so we have no tool definitions to assess.Unverified
Capabilities0
- Protocol version not yet verified: our sandbox run of this package did not complete, so we never saw its MCP handshake.Unverified
Unverified: 4 categories
Categories scored 0 because our sandbox run of this package has not given us the schema these checks need to read. That is a gap on our side rather than a finding about the package, and we only credit what we can confirm, so the score stands at 0 until the capture succeeds. We are working through the fleet, so this normally clears without any action from you. How we score packages →
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
pypi · simengine-mcp
claude mcp add cogswellspacely8-star-simengine -- uvx simengine-mcp
codex mcp add cogswellspacely8-star-simengine -- uvx simengine-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"cogswellspacely8-star-simengine": {
"type": "local",
"command": [
"uvx",
"simengine-mcp"
],
"enabled": true
}
}
} openclaw mcp add cogswellspacely8-star-simengine --command uvx --arg simengine-mcp
mcp_servers:
cogswellspacely8-star-simengine:
command: "uvx"
args: ["simengine-mcp"] {
"mcpServers": {
"cogswellspacely8-star-simengine": {
"command": "uvx",
"args": [
"simengine-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 1 Aug 26 −5
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 30 Jul 26 −18
- Malware scan: pass → unverified ▼ security
- 28 Jul 26 0
- Tool coverage: Tool coverage not yet verified: our sandbox run of this package did not complete, so we have no tool definitions to assess. functional
- Schema quality: Schema quality not yet verified: our sandbox run of this package did not complete, so we have no schema to assess. functional
- 26 Jul 26 47
First indexed and scored.
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 3 Aug 2026 · Analysed pypi/[email protected]
Provenance none
Ecosystem: pypi · Outcome: none
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Dependencies 0 packages
0 packages in the resolved dependency tree.
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.
baseline_comparison ~63
Compare Monte Carlo simulation results to a deterministic (spreadsheet-style) single-point forecast. Shows the risk information hidden by deterministic forecasting.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | Domain config filename |
| n_simulations | integer | — | MC simulation count (default: 5000) |
No output schema declared.
No examples provided.
convergence_check ~73
Run convergence diagnostics to verify that the simulation sample size is sufficient. Tests whether statistics stabilize as N increases.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | Domain config filename |
| max_runs | integer | — | Maximum runs to test (default: 10000) |
| metric | string | — | Target metric to check (default: first metric) |
No output schema declared.
No examples provided.
decide_under_uncertainty ~245
Decision gate for agents. Runs a Monte Carlo simulation and returns whether to PROCEED or ESCALATE based on how much of the OUTCOME distribution falls inside an acceptable range you specify. Returns the full outcome distribution plus a proceed/escalate flag and a plain-English reason. Use this to stop a multi-step agent from acting on an overconfident point estimate. NOTE: this models OUTCOME RISK (the spread of results given uncertain inputs) — not the language model's own token confidence.
| Name | Type | Req | Description |
|---|---|---|---|
| acceptable | object | yes | Acceptable range for the metric; provide at least one of 'min' or 'max'. |
| config | object | — | Inline domain config JSON (alternative to domain filename) |
| domain | string | — | Domain config filename (alternative to config) |
| metric | string | yes | The output metric to evaluate the decision against |
| min_confidence | number | — | Probability mass that must fall within the acceptable range to proceed (default: 0.8) |
| n_simulations | integer | — | Number of simulations to run (default: from config) |
| seed | integer | — | Random seed for reproducibility (default: 42) |
No output schema declared.
No examples provided.
list_domains ~22
List all available domain configuration files in the SimEngine domains/ directory.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
optimize ~70
Run evolutionary optimization on a domain to find the best control settings. Uses a genetic algorithm with safety constraints.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | Domain config filename |
| generations | integer | — | Number of GA generations (default: 15) |
| population | integer | — | Population size per generation (default: 20) |
No output schema declared.
No examples provided.
sensitivity_analysis ~97
Run one-at-a-time sensitivity analysis to rank which input parameters drive the most output variance. Returns elasticity and relative importance scores.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | Domain config filename |
| metric | string | — | Target metric to analyze (default: first metric) |
| n_base | integer | — | Base simulation count (default: 1000) |
| n_perturb | integer | — | Perturbation trials per parameter (default: 10) |
No output schema declared.
No examples provided.
simulate ~146
Run a Monte Carlo simulation. Provide either a domain filename (from the domains/ directory) or an inline config JSON. Returns statistics, safety results, fitness score, and a narrative summary.
| Name | Type | Req | Description |
|---|---|---|---|
| config | object | — | Inline domain config JSON (alternative to domain filename) |
| domain | string | — | Domain config filename (e.g. 'saas_startup.json') |
| n_simulations | integer | — | Number of simulations to run (default: from config) |
| overrides | object | — | Dot-path overrides applied to the config, e.g. {'parameters.close_rate.params.mode': 0.35} |
| seed | integer | — | Random seed for reproducibility (default: 42) |
No output schema declared.
No examples provided.
update_bayesian ~93
Bayesian-update simulation parameters using real observed data. Uses conjugate prior updates (Beta-Binomial, Gamma-Poisson, Normal-Normal) to calibrate the model.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | Domain config filename |
| observations | object | yes | Map of variable_name to array of observed values, e.g. {'close_rate': [0.25, 0.30, 0.28]} |
No output schema declared.
No examples provided.
validate_config ~37
Validate a domain configuration JSON. Returns whether it is valid and any errors found.
| Name | Type | Req | Description |
|---|---|---|---|
| config | object | yes | The domain config JSON to validate |
No output schema declared.
No examples provided.