io.github.cogswellspacely8-star/simengine
PYPI · SIMENGINE-MCP · SCANNED SEP 21
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 → Why this is hard to 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 71 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability77
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 846 tokens (~94/item across 9 items; 9 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management93
- Stability observed for 28 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 9 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 9 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities20
- Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28. See how to fix → Fail
How do I install the io.github.cogswellspacely8-star/simengine MCP server?
io.github.cogswellspacely8-star/simengine runs locally as a PyPI package, launched with uvx simengine-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · simengine-mcp
claude mcp add cogswellspacely8-star-simengine -- uvx simengine-mcp
{
"mcpServers": {
"cogswellspacely8-star-simengine": {
"command": "uvx",
"args": [
"simengine-mcp"
]
}
}
} {
"servers": {
"cogswellspacely8-star-simengine": {
"command": "uvx",
"args": [
"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": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"simengine-mcp"
]
}
}
} assistant mcp add cogswellspacely8-star-simengine -t stdio -c uvx -a 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.
- 21 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 19 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Sept 26 −3
- Stability: pass → 0.80 functional
- 16 Sept 26 +1
- Stability: 0.97 → pass security
- 15 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 14 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 12 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 10 Sept 26 −3
- Stability: pass → 0.80 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 21 Sept 2026 · Analysed pypi/simengine-mcp@3.0.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Background: Why install scripts are a supply-chain risk →
Dependencies 0 packages
| Packages resolved | 0 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
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. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
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.
What is the io.github.cogswellspacely8-star/simengine MCP server?
io.github.cogswellspacely8-star/simengine is an MCP server listed in the public MCP registry as io.github.cogswellspacely8-star/simengine. Monte Carlo decision-intelligence over MCP: simulate, optimize, and decide under uncertainty. This page covers its PyPI package (simengine-mcp).
Is the io.github.cogswellspacely8-star/simengine MCP server safe to use?
io.github.cogswellspacely8-star/simengine scores 66 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 21 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.cogswellspacely8-star/simengine MCP server expose?
io.github.cogswellspacely8-star/simengine exposes 9 tools: simulate, list_domains, validate_config, optimize, update_bayesian, and 4 more. Their descriptions and schemas cost roughly 846 tokens of context every time the server is loaded.
Is the io.github.cogswellspacely8-star/simengine MCP server still maintained?
io.github.cogswellspacely8-star/simengine is still listed as active in the MCP registry. We last reached this channel on 21 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.cogswellspacely8-star/simengine MCP server under?
io.github.cogswellspacely8-star/simengine declares the Apache-2.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.