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Opensensemap

by io.github.pipeworx-io

Opensensemap Area Average

opensensemap_area_average
Read-onlyIdempotent

Average one phenomenon across all citizen science sensors (openSenseMap / senseBox network) in an area — hyperlocal neighborhood-level temperature, PM2.5/PM10 air quality, humidity, pressure or noise from many independent community stations. Give either a bounding box or a center point + radius_km. Aggregates the latest reading per sensor over the recent window client-side (the network has no fast server-side aggregation). 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) — the name must match the sensor title exactly (e.g. "Temperatur", not "temperature"). Example: opensensemap_area_average({ phenomenon: "PM2.5", latitude: 52.52, longitude: 13.405, radius_km: 5 })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bboxNoBounding box as "west,south,east,north" in degrees, e.g. "13.3,52.45,13.5,52.55" (overrides center+radius)
latitudeNoCenter latitude (use with longitude + radius_km, instead of bbox)
longitudeNoCenter longitude
radius_kmNoRadius in km around the center, 0.1-25 (default 5)
phenomenonYesExact phenomenon / sensor title, e.g. "PM2.5", "PM10", "Temperatur", "rel. Luftfeuchte", "Luftdruck", "Lautstärke"
window_hoursNoHow far back to look for each sensor's latest reading, 1-24 hours (default 2)

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,
+    "phenomenon": "PM2.5",
+    "radius_km": 5
+  },
+  {
+    "bbox": "13.3,52.45,13.5,52.55",
+    "phenomenon": "Temperatur",
+    "window_hours": 2
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, but the description adds valuable context beyond these: 'Aggregates the latest reading per sensor over the recent window client-side (the network has no fast server-side aggregation)' and the strict requirement that 'the name must match the sensor title exactly'. It also lists exact German phenomenon names with units, giving the agent critical operational knowledge.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and includes essential details (usage modes, aggregation behavior, phenomenon list, example). It is longer than strictly necessary but every sentence contributes useful information, with no filler. The structure is logical, moving from what to how to example.

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

Completeness3/5

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

Input semantics are thoroughly covered, but there is no output schema and the description does not state what the tool returns (e.g., whether it's a single average number, an object with sensor count, or units). Edge cases like empty areas or invalid phenomenon names are also not addressed. For a tool with this complexity and no output schema, the description is incomplete on the output side.

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

Parameters4/5

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

The schema covers all 6 parameters at 100% coverage, so the baseline is 3. The description adds significant meaning beyond the schema by providing an exhaustive list of exact phenomenon names with units (e.g., 'Temperatur' (°C), 'PM2.5' (µg/m³)), explaining the exact-match requirement, and giving an example call that demonstrates how parameters combine. This pushes it above baseline.

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 clearly states the tool's function: 'Average one phenomenon across all citizen science sensors ... in an area'. It specifies the verb (average), resource (phenomenon across sensors), and scope (area), and distinguishes it from sibling tools like opensensemap_box and opensensemap_nearby by emphasizing area-based aggregation with bounding box or center+radius input.

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?

The description gives clear usage context: 'Give either a bounding box or a center point + radius_km' and explains the client-side aggregation window. However, it does not explicitly mention alternatives or when not to use this tool compared to related opensensemap tools, so it falls short of a full 5.

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