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Get States

get_states
Read-onlyIdempotent

Live ADS-B state vectors for aircraft currently transmitting — position, barometric and geometric altitude (metres), velocity (m/s), heading, vertical rate, squawk, and on-ground flag, plus registration and aircraft type. Filter to specific aircraft with comma-separated ICAO24 hex addresses, or to an area with lat/lon and a radius. Keyless. Use for "where is this aircraft now", "what is transmitting near this point", "is anything squawking an emergency code here".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoCentre latitude for an area query, degrees.
lonNoCentre longitude for an area query, degrees.
icao24NoFilter by one or more ICAO24 hex addresses, comma-separated (e.g. "3c675a,a835af").
radius_nmNoRadius around lat/lon in nautical miles (1-250, default 100).

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the description does not need to repeat safety traits. It adds useful context by stating the data is live and keyless (no authentication required), along with the specific data fields returned, which is valuable beyond the 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 compact at two sentences, yet packs in data fields, filtering options, and usage examples. It is front-loaded with the core purpose and wastes no words.

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

Completeness4/5

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

Despite lacking an output schema, the description enumerates the return fields, making the response shape clear. It also covers the main use cases and authentication requirement (keyless). Minor gaps exist such as API limits or pagination, but overall it is sufficiently complete for a live data query tool.

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

Parameters3/5

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

The schema already covers 100% of parameter meanings, including examples and defaults. The description reinforces that icao24 filters by aircraft and lat/lon/radius defines an area query, but it does not add any new semantic details beyond what the schema already provides.

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 identifies the tool as retrieving live ADS-B state vectors and enumerates the exact fields returned (position, altitude, velocity, etc.). It distinguishes itself from sibling tools like get_aircraft and get_flights by focusing on real-time state vectors with filtering options.

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 provides explicit use cases in quotation marks ('where is this aircraft now', 'what is transmitting near this point', 'is anything squawking an emergency code here'), giving clear guidance on when to use the tool. However, it does not explicitly mention when not to use it or recommend alternative tools for different scenarios.

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

A3.9/5.0
Disambiguation2/5

Several tools have heavily overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all take natural-language factual questions, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. There is also overlap among get_states, get_aircraft, and airspace_activity, plus a large cluster of prediction-market tools with similar discovery purposes.

Naming Consistency4/5

The set is mostly snake_case and readable, with familiar patterns like get_*, list_*, resolve_*, and compare_*. It is not chaotic, but there are notable deviations: noun-phrase names like entity_profile, recent_changes, ai_visibility_check, and airspace_activity break the verb-first pattern.

Tool Count2/5

35 tools is too many for a single coherent server, especially because they comprise several independent families: aviation, data/research, prediction markets, memory, and subscriptions. Each tool is individually justified, but the bundle should be split into smaller focused MCP servers.

Completeness3/5

The data-research side is fairly complete, with discover, routing, grounded verification, entity resolution, search-within, compare, and follow-up tools, and the subscription and memory lifecycles are covered. However, the OpenSky side is incomplete: get_flights explicitly cannot return its data, and referenced route/arrival/departure tools are missing from the set.