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Opensensemap

by io.github.pipeworx-io

Opensensemap Nearby

opensensemap_nearby
Read-onlyIdempotent

Find citizen science sensor stations (senseBox, openSenseMap network) near a lat/lon and return their latest readings — hyperlocal temperature, humidity, air pressure, PM2.5/PM10 air quality, illuminance, UV, noise. Answers "sensor readings near me", "what does the local air quality sensor say". Community-operated uncalibrated hardware: quality varies, so cross-check outliers. Filter to one measurement type with phenomenon (Phenomenon names on openSenseMap are mostly German (exact sensor titles): "Temperatur" (temperature °C), "rel. Luftfeuchte" (relative humidity %), "Luftdruck" (air pressure hPa), "PM2.5" and "PM10" (particulate matter µg/m³), "Beleuchtungsstärke" (illuminance lx), "UV-Intensität" (UV µW/cm²), "Lautstärke" (noise)). Stations silent for more than 7 days are skipped unless include_stale=true. Example: opensensemap_nearby({ latitude: 52.52, longitude: 13.405, phenomenon: "PM2.5" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax stations to return with full readings, 1-10 (default 5)
latitudeYesLatitude of the search center, e.g. 52.52
longitudeYesLongitude of the search center, e.g. 13.405
radius_kmNoSearch radius in km, 0.1-50 (default 10)
phenomenonNoOptional filter — only stations measuring this phenomenon. Common titles: "Temperatur", "rel. Luftfeuchte", "Luftdruck", "PM2.5", "PM10", "Beleuchtungsstärke", "UV-Intensität", "Lautstärke" (matched case-insensitively as a substring)
include_staleNoInclude stations whose last measurement is older than 7 days (default false)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "latitude": 52.52,
+    "longitude": 13.405
+  },
+  {
+    "latitude": 52.52,
+    "limit": 3,
+    "longitude": 13.405,
+    "phenomenon": "PM2.5",
+    "radius_km": 5
+  }
+]
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, non-destructive, idempotent behavior. The description adds meaningful beyond-annotation context: community-operated uncalibrated hardware with varying quality, a 7-day stale-skipping rule, and the ability to include stale stations. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose, then provides natural-language usage, data-quality caveat, parameter guidance, behavior, and an example. Every sentence serves a purpose; length is justified by the complexity of the phenomenon names and filtering behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only nearby-search tool with no output schema, the description covers the key aspects: what it returns (latest readings across various measurement types), filtering via phenomenon, stale-station behavior, data-quality caveats, and a concrete example. It is complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant value by explaining the phenomenon parameter with exact German names and units, clarifying the substring case-insensitive matching, and illustrating with an example. This goes well beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: 'Find citizen science sensor stations... near a lat/lon and return their latest readings.' It clearly distinguishes from sibling tools by emphasizing the 'near a lat/lon' point-based search, and provides concrete natural language queries it answers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context: 'Answers "sensor readings near me"...' and filters for phenomena and stale stations. It does not explicitly mention alternatives like opensensemap_area_average or opensensemap_box, but the 'nearby' framing implies when to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior today, while discover_tools and suggest_questions overlap as discovery/onboarding entry points. The Polymarket tools also blur together across arbitrage, edges, edge tracking, and fill risk, making tool selection prone to mistakes despite long descriptions.

Naming Consistency3/5

Names are consistently lowercase snake_case and readable, but they mix conventions: some are clear verb_noun actions like validate_claim and compare_entities, while others are noun-led like recent_alerts and entity_profile, or domain-prefixed like polymarket_edges and opensensemap_nearby. There is no single predictable naming pattern across the set.

Tool Count2/5

At 34 tools, this exceeds the 25+ threshold for a heavy tool set, and the tools span several unrelated domains: OpenSenseMap sensors, Pipeworx data access, Polymarket research, memory, subscriptions, and AI visibility. A server named Opensensemap hosting this much unrelated functionality feels poorly scoped.

Completeness3/5

The OpenSenseMap portion covers nearby discovery, box lookup, and area averages, but lacks historical/time-series access and station lifecycle operations, which are notable gaps for a sensor data server. The broader Pipeworx surface has strong lookup, research, validation, and subscription coverage, so the main incompleteness is in the named domain.

Resources