io.github.cogswellspacely8-star/simengine
PYPI · SIMENGINE-MCP
Monte Carlo decision-intelligence over MCP: simulate, optimize, and decide under uncertainty.
Available components
Last scanned (20 hours ago)
Scores higher than 84% of PyPI servers
Category Data and Analytics
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 Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised build step with no custom 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 116 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 Management100
- No destabilizing schema changes in the last 30 days.Pass
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
Checks we ran (16)
- Malware scan
- Known CVEs
- Install scripts
- Dependency health
- Source repository
- Provenance
- License
- Maintenance
- Security disclosure
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Does not apply to this package (2)
- Endpoint SecurityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
- Transport & ReachabilityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
The score does not measure everything. What the score does not measure →
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.
- 3 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 2 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 1 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 31 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 29 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 28 Oct 26 +1
- Stability: 0.97 → pass security
- 27 Oct 26 −1
- Stability: pass → 0.97 functional
- 24 Oct 26 −15
- Malware scan: pass → unverified ▼ security
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 5 Nov 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 |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | Known build tool | 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.
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
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 82 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 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 5 November 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.
What did VerifyMCP test on the io.github.cogswellspacely8-star/simengine MCP server?
VerifyMCP ran 16 checks on io.github.cogswellspacely8-star/simengine, covering Malware scan, Known CVEs, Install scripts, Dependency health and 12 more. Endpoint Security and Transport & Reachability do not apply to a PyPI package, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.