Exec-jet + military aircraft intel — TEB/VNY/DCA geofence, tail filter, B-52/F-35/E-3 detection via adsb.lol open. M&A / defense Polymarket edge.
| Name | Required | Description | Default |
|---|---|---|---|
| hours_back | No | Hours back, default 12 | |
| tail_number | No | Tail e.g. N123AB | |
| airport_code | No | Airport ICAO/IATA e.g. TEB, VNY, DCA |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description must disclose behavioral traits. It states the tool uses an open data source (adsb.lol), implying read-only access, but does not discuss permissions, rate limits, data freshness, or any potential side effects. The detection claim lacks detail on accuracy or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, but the first sentence packs many concepts (exec-jet, military, geofence, tail filter, detection) into a dense structure. The second sentence is clear. Some reformatting could improve readability, but it is not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain what the tool returns. It mentions 'intel' and 'detection' but does not describe the structure or content of the output, leaving the agent uncertain about the data format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 3 parameters have schema descriptions (100% coverage), so baseline is 3. The description adds value by explaining that airport codes define a geofence, tail numbers filter specific aircraft, and mentions detection of specific models (B-52/F-35/E-3). This enriches understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool provides intelligence on executive jets and military aircraft, referencing specific airports (TEB, VNY, DCA), tail filtering, and detection via adsb.lol. It also hints at use in M&A and defense betting. While not perfectly precise, it distinguishes well from siblings focused on other domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions 'M&A / defense Polymarket edge' as potential use cases, but provides no explicit guidance on when to use this tool versus alternatives. No when-not-to-use or alternative tool names are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.