yerevan-gis-mcp
Related Servers
Alternatives to yerevan-gis-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query sourced rural land, zoning, permit, public decision, and related government data for covered jurisdictions, with free coverage and pricing tools plus paid per-call data and watch tools.MIT
- AlicenseNot gradedqualityCmaintenanceSearch and query City of San Jose GIS open geospatial datasets (parcels, zoning, public works) via ArcGIS Feature Services, enabling AI agents to access live geospatial data through natural language.438 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to fetch public data from 120+ sources via natural language and save it locally as Excel, GeoJSON, or KML files.20 PyPIMozilla Public 2.0
- FlicenseNot gradedqualityCmaintenanceProvides tools to explore and analyze Yerevan land-use classifications across 2015, 2020, and 2024 using H3 hexagons, including class composition, transitions, and spatial queries. Enables users to retrieve hex details, compute change summaries, and export GeoJSON through natural language.-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to search and query Nantes Métropole open data datasets (mobility, urban services, environment, geography) using natural language and SQL-like filters.507 npmMIT
- AlicenseAqualityBmaintenanceEnables AI agents to query Earth observation data, satellite imagery, active fires, and weather via natural language, returning interactive maps and briefs.12MIT
TDQS
Scored across 24 tools
Most tools are clearly separated into curated domain lookups, generic layer queries, and portal discovery, but find_nearby_amenities and query_near_point overlap in spatial-nearest behavior, and count_features overlaps with aggregate for counting. These pairs require careful description reading but are still distinguishable.
The set overwhelmingly uses snake_case verb_noun with meaningful verbs like list, get, search, and query. Minor inconsistency: aggregate has no object noun, find_nearby_amenities uses find instead of search, and get_distinct_values is more of a query operation. Overall the pattern is predictable.
At 24 tools, the server is on the heavy side and falls within the 16-25 range that feels burdensome. The breadth of the GIS portal does justify many tools, but agents face a large surface to learn and select from, so the count is borderline rather than ideal.
For a read-only municipal GIS server, coverage is excellent: layer discovery, schema inspection, attribute and spatial queries, aggregation, map rendering, portal/app exploration, plus curated tools for air quality, cadastre, construction, heritage, amenities, and streets. Only minor gaps like full geocoding exist, and their workarounds are documented.