Windsock

Count registered aircraft (facets by state & year)

count_aircraft

Read-only

Aggregate registry census for a filter set: the total number of matching aircraft plus breakdowns by US state and by build year — in one call, without paging individual tails. Directly answers "how many Cessna R182s are registered in Oregon" (filter make_model_id + state, then read total_count / state_counts). Uses the same filter grammar as search_aircraft. Counts are computed over up to fleet_cap aircraft.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`filters`	No	AND-combined filters; each is {field_name, condition, value}. Fields: make_model_id (numeric id), state (2-letter US code), city, year, tail_number, registrant_name (current FAA registrant / registered owner; substring via contains), location_point, registration_authority, registration_country, airworthiness_class. Conditions: is, is_not, contains, does_not_contain, in, not_in, lt, lte, gt, gte. For location_point use condition "within_<miles>" (e.g. "within_50") with value "lat,lon" (e.g. "45.52,-122.68"). To search SEVERAL make/models at once, use one clause with condition "in" and an array of ids (e.g. {field_name: "make_model_id", condition: "in", value: [1699, 1704]}) — that is a union. Do NOT repeat make_model_id in separate clauses: filters are AND-combined, so two of them match nothing. Spec filters (gt/gte/lt/lte only): engine_power_hp, useful_load_lbs (pounds); engine_make is text (is/contains, e.g. "LYCOMING"). ⚠️ These come from per-model performance specs and are NOT populated for every aircraft: engine power 75% of the fleet, weights 76%, engine make 78%. Filtering on one EXCLUDES every aircraft with no value, so tell the user that when you use them. airworthiness_class (is/is_not/in/not_in) is the FAA classification from the aircraft's LATEST registration — this is how you search for EXPERIMENTAL / amateur-built aircraft. Tokens: standard, limited, restricted, experimental, provisional, multiple, primary, special_flight_permit, light_sport. ⚠️ 36% of the fleet has no certification recorded, so filtering on it excludes all of those; say so when you use it. 🔴 STATE OF REGISTRY: this registry is NOT US-only. ~115k aircraft (21.6% of it) are on one of 17 international registers, and they are concentrated by model — 41% of the R44 fleet, 21% of C152s, 10% of SR22s. An unfiltered count is a WORLD count, not a US one. Use registration_authority with an authority code (FAA, TCCA, CASA, ANAC, DGAC, AESA, DGACL, ILT, ACG, IAA, CAANO, NSAT, CAAI, CAABG, CAALV, DAC, CAAS, ECAA) or the cohort tokens "domestic" / "international"; or registration_country with an ISO-2 code or country name ("CA", "Canada"). Say which fleet a number covers whenever you report one. ⚠️ There is NO `country` filter. The v3 validator rejects one (`country` is not a CAVN column), and the underlying value is the FAA registrant's mailing address, NULL for every international aircraft — so it could not select them even if it were accepted. Use registration_country. ⚠️ International aircraft have registry identity but carry no predicted_price and cannot be valued by tail.
`fleet_cap`	No	Max aircraft scanned for the facet counts (default 2500, max 10000).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes
`meta`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 20, 2026

changedInput schema / properties / filters / description

Previous value: -"AND-combined filters; each is {field_name, condition, value}. Fields: make_model_id (numeric id), state (2-letter US code), city, year, tail_number, registrant_name (current FAA registrant / registered owner; substring via contains), seen_on_market (\"true\"/\"false\"), latest_list_date, location_point, registration_authority, registration_country, airworthiness_class. Conditions: is, is_not, contains, does_not_contain, in, not_in, lt, lte, gt, gte. For location_point use condition \"within_<miles>\" (e.g. \"within_50\") with value \"lat,lon\" (e.g. \"45.52,-122.68\"). To search SEVERAL make/models at once, use one clause with condition \"in\" and an array of ids (e.g. {field_name: \"make_model_id\", condition: \"in\", value: [1699, 1704]}) — that is a union. Do NOT repeat make_model_id in separate clauses: filters are AND-combined, so two of them match nothing. Spec filters (gt/gte/lt/lte only): engine_power_hp, useful_load_lbs (pounds), smoh_hours (engine hours since major overhaul); engine_make is text (is/contains, e.g. \"LYCOMING\"). ⚠️ These come from per-model performance specs and listing data and are NOT populated for every aircraft: engine power 75% of the fleet, weights 76%, engine make 78%, smoh_hours only 12% (84% of aircraft that have ever been listed — pair it with seen_on_market). Filtering on one EXCLUDES every aircraft with no value, so tell the user that when you use them. airworthiness_class (is/is_not/in/not_in) is the FAA classification from the aircraft's LATEST registration — this is how you search for EXPERIMENTAL / amateur-built aircraft. Tokens: standard, limited, restricted, experimental, provisional, multiple, primary, special_flight_permit, light_sport. ⚠️ 36% of the fleet has no certification recorded, so filtering on it excludes all of those; say so when you use it. 🔴 STATE OF REGISTRY: this registry is NOT US-only. ~115k aircraft (21.6% of it) are on one of 17 international registers, and they are concentrated by model — 41% of the R44 fleet, 21% of C152s, 10% of SR22s. An unfiltered count is a WORLD count, not a US one. Use registration_authority with an authority code (FAA, TCCA, CASA, ANAC, DGAC, AESA, DGACL, ILT, ACG, IAA, CAANO, NSAT, CAAI, CAABG, CAALV, DAC, CAAS, ECAA) or the cohort tokens \"domestic\" / \"international\"; or registration_country with an ISO-2 code or country name (\"CA\", \"Canada\"). Say which fleet a number covers whenever you report one. ⚠️ There is NO `country` filter. The v3 validator rejects one (`country` is not a CAVN column), and the underlying value is the FAA registrant's mailing address, NULL for every international aircraft — so it could not select them even if it were accepted. Use registration_country. ⚠️ International aircraft have registry identity but no US market history, so they carry no predicted_price and cannot be valued by tail."New value: +"AND-combined filters; each is {field_name, condition, value}. Fields: make_model_id (numeric id), state (2-letter US code), city, year, tail_number, registrant_name (current FAA registrant / registered owner; substring via contains), location_point, registration_authority, registration_country, airworthiness_class. Conditions: is, is_not, contains, does_not_contain, in, not_in, lt, lte, gt, gte. For location_point use condition \"within_<miles>\" (e.g. \"within_50\") with value \"lat,lon\" (e.g. \"45.52,-122.68\"). To search SEVERAL make/models at once, use one clause with condition \"in\" and an array of ids (e.g. {field_name: \"make_model_id\", condition: \"in\", value: [1699, 1704]}) — that is a union. Do NOT repeat make_model_id in separate clauses: filters are AND-combined, so two of them match nothing. Spec filters (gt/gte/lt/lte only): engine_power_hp, useful_load_lbs (pounds); engine_make is text (is/contains, e.g. \"LYCOMING\"). ⚠️ These come from per-model performance specs and are NOT populated for every aircraft: engine power 75% of the fleet, weights 76%, engine make 78%. Filtering on one EXCLUDES every aircraft with no value, so tell the user that when you use them. airworthiness_class (is/is_not/in/not_in) is the FAA classification from the aircraft's LATEST registration — this is how you search for EXPERIMENTAL / amateur-built aircraft. Tokens: standard, limited, restricted, experimental, provisional, multiple, primary, special_flight_permit, light_sport. ⚠️ 36% of the fleet has no certification recorded, so filtering on it excludes all of those; say so when you use it. 🔴 STATE OF REGISTRY: this registry is NOT US-only. ~115k aircraft (21.6% of it) are on one of 17 international registers, and they are concentrated by model — 41% of the R44 fleet, 21% of C152s, 10% of SR22s. An unfiltered count is a WORLD count, not a US one. Use registration_authority with an authority code (FAA, TCCA, CASA, ANAC, DGAC, AESA, DGACL, ILT, ACG, IAA, CAANO, NSAT, CAAI, CAABG, CAALV, DAC, CAAS, ECAA) or the cohort tokens \"domestic\" / \"international\"; or registration_country with an ISO-2 code or country name (\"CA\", \"Canada\"). Say which fleet a number covers whenever you report one. ⚠️ There is NO `country` filter. The v3 validator rejects one (`country` is not a CAVN column), and the underlying value is the FAA registrant's mailing address, NULL for every international aircraft — so it could not select them even if it were accepted. Use registration_country. ⚠️ International aircraft have registry identity but carry no predicted_price and cannot be valued by tail."

changedInput schema / properties / filters / items / properties / field_name / enum

Previous value: -[
-  "make_model_id",
-  "state",
-  "city",
-  "year",
-  "tail_number",
-  "registrant_name",
-  "seen_on_market",
-  "latest_list_date",
-  "location_point",
-  "engine_power_hp",
-  "useful_load_lbs",
-  "engine_make",
-  "smoh_hours",
-  "airworthiness_class",
-  "registration_authority",
-  "registration_country"
-]New value: +[
+  "make_model_id",
+  "state",
+  "city",
+  "year",
+  "tail_number",
+  "registrant_name",
+  "location_point",
+  "engine_power_hp",
+  "useful_load_lbs",
+  "engine_make",
+  "airworthiness_class",
+  "registration_authority",
+  "registration_country"
+]

First observedSep 11, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), but the description adds substantial behavioral context beyond them: counts are capped by fleet_cap, spec filters exclude aircraft with missing values (with per-field coverage percentages), airworthiness_class is missing for 36% of the fleet, the registry includes ~21.6% international aircraft, and there is no `country` filter because the v3 validator rejects it. These are exactly the operational caveats an agent needs before calling the tool.

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

Conciseness3/5

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

The opening sentence front-loads the core purpose well, but the remainder is very long and re-documents much of what the input schema already covers (filter conditions, location_point format, field enumerations). The data-quality warnings earn their place, yet the prose is dense with repeated schema detail and emoji emphasis, so it is more verbose than necessary.

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?

An output schema exists, so return values need not be fully re-explained, and the description still mentions total_count/state_counts for convenience. Given the tool's complexity, the definition covers the critical traps (fleet_cap limits, sparse spec data, international registrations, missing country filter), leaving nothing essential for correct invocation unstated.

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?

Schema description coverage is 100%, so the schema already documents both parameters and the filter grammar; baseline is 3. The description nevertheless adds meaning beyond the schema: it warns that spec filters (engine_power_hp, useful_load_lbs, engine_make) exclude aircraft with missing values at stated rates, explains union vs AND semantics for make_model_id clauses, lists registration_authority codes and cohort tokens, and clarifies that registration_country replaces the nonexistent `country` filter.

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 first sentence states a specific verb and resource: 'Aggregate registry census for a filter set: the total number of matching aircraft plus breakdowns by US state and by build year — in one call, without paging individual tails.' It also explicitly distinguishes the tool from search_aircraft by noting it avoids paging individual tails, so an agent can tell them apart without opening either schema.

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 a concrete use case ('Directly answers "how many Cessna R182s are registered in Oregon" (filter make_model_id + state, then read total_count / state_counts)') and says it 'Uses the same filter grammar as search_aircraft,' which implicitly routes agents needing individual records to that sibling. However, it never explicitly states when not to use count_aircraft or names search_aircraft as the alternative for tail-level data, so it falls short of full 5-level when/when-not guidance.

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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