Geoserver MCP Server
geoserver-mcp
PyPI
v0.4.0
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
A GeoServer MCP server implementation that enhances LLM capabilities with geospatial data and mapping services via GeoServer REST API
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 5 = 95. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −3 | capability blast radius (moderate) — client exposure if the model is manipulated |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 0
Tools 56
Each tool and what it can reach — statically extracted from the published source.
get_versioningests untrusted inputadd_layer_to_layergroupno sensitive capabilitycreate_catagorized_featurestyleno sensitive capabilitycreate_classified_featurestyleno sensitive capabilitycreate_coveragestoreno sensitive capabilitycreate_coveragestyleno sensitive capabilitycreate_datastoreno sensitive capabilitycreate_featurestoreno sensitive capabilitycreate_gpkg_datastoreno sensitive capabilitycreate_layerno sensitive capability
Show 46 more tools ↓
create_layergroupno sensitive capabilitycreate_outline_featurestyleno sensitive capabilitycreate_shp_datastoreno sensitive capabilitycreate_styleno sensitive capabilitycreate_userno sensitive capabilitycreate_usergroupno sensitive capabilitycreate_workspaceno sensitive capabilitydelete_coveragestoreno sensitive capabilitydelete_layergroupno sensitive capabilitydelete_resourceno sensitive capabilitydelete_userno sensitive capabilitydelete_usergroupno sensitive capabilityedit_featuretypeno sensitive capabilitygenerate_mapno sensitive capabilityget_all_usergroupsno sensitive capabilityget_all_usersno sensitive capabilityget_coveragestoreno sensitive capabilityget_coveragestoresno sensitive capabilityget_datastoreno sensitive capabilityget_datastoresno sensitive capabilityget_feature_attributeno sensitive capabilityget_featuretypesno sensitive capabilityget_layer_infono sensitive capabilityget_layergroupno sensitive capabilityget_layergroupsno sensitive capabilityget_manifestno sensitive capabilityget_statusno sensitive capabilityget_system_statusno sensitive capabilitylist_layersno sensitive capabilitylist_workspacesno sensitive capabilitymodify_userno sensitive capabilitypublish_featurestoreno sensitive capabilitypublish_featurestore_sqlviewno sensitive capabilitypublish_styleno sensitive capabilitypublish_time_dimension_to_coveragestoreno sensitive capabilityquery_featuresno sensitive capabilityreload_geoserverno sensitive capabilityremove_layer_from_layergroupno sensitive capabilityreset_geoserverno sensitive capabilitystyle_catagorize_xmlno sensitive capabilitystyle_classified_xmlno sensitive capabilitystyle_coverage_style_colormapentryno sensitive capabilitystyle_coverage_style_xmlno sensitive capabilitystyle_outline_only_xmlno sensitive capabilityupdate_layergroupno sensitive capabilityupdate_serviceno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (56 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v0.4.0 latest |
A 95/100 | 0 | 1.13.0 | 2026-08-25 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
npx mcptrustchecker scan geoserver-mcp --online --registry pypiMore in Data Science & ML
MCP server for 2s.io — 575+ pay-per-call tools for AI agents — ground-truth data, AI gateway, and agent infra (storage, locks, queues, watchers). x402 USDC on Base/Solana, no API keys, upto usage billing, free trials.
ArXiv preprints + Google Scholar papers, with citation counts in one query.
Add MCP servers to your favorite coding agents with a single command.
Help agents automatically write and test stories for your UI components
Model Context Protocol server for AI-Archive platform - enables AI agents to discover, submit, and review research papers
MCP server for Semantic Scholar research workflows with stdio and Streamable HTTP transports.