forecastCompact 10-day forecast (temperature, precip, wind, cloud, humidity).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude (decimal degrees). | |
| lon | Yes | Longitude (decimal degrees). | |
| altitude | No | Meters above sea level (optional, improves accuracy). |
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Feature type (GeoJSON) | |
| geometry | No | ||
| properties | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose read-only, idempotent, and non-destructive behavior, so the description does not need to repeat that. However, it adds no new behavioral context (e.g., response size limits, pagination, or data freshness), providing only the output scope.
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?
The description is a single, tightly worded sentence that front-loads the key info (10-day forecast) and enumerates output variables. No wasted words.
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?
This is a simple tool with a complete input schema and an output schema available. The description covers the essential output aspects (time horizon and weather elements), leaving no critical gaps.
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?
The schema fully describes all three parameters (lat, lon, altitude) with 100% coverage. The description adds no extra meaning about the parameters themselves, so it meets the baseline for high schema coverage.
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 states what the tool does: it provides a 'Compact 10-day forecast' and names the specific weather elements (temperature, precip, wind, cloud, humidity). This distinguishes it from sibling tools like nowcast or oceanforecast.
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?
There is no explicit guidance on when to use this tool versus alternatives, nor any mention of exclusions or prerequisites. The '10-day' timeframe implies medium-range but does not say 'use nowcast for short-term' or otherwise contrast with siblings.
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.
Most tools have distinct purposes, but there is some overlap among closely related ones (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research; multiple Polymarket tools). Descriptions are detailed enough to differentiate, but an agent might still misselect on subtle differences.
Names consistently use lowercase with underscores, but no strong verb_noun pattern. Some are noun-based (airquality, nowcast), others verb-based (ask_pipeworx, compare_entities). This is readable but not perfectly predictable.
35 tools is high but justified by the server's broad scope (data queries, betting analysis, weather, memory, subscriptions). The number feels appropriate given the comprehensive functionality described.
The tool set covers an impressively wide range of capabilities: data querying with multiple modes, entity profiling, comparisons, search, betting analysis, weather, memory, subscriptions, and feedback. Missing features (e.g., updating memories) are minor; the surface is remarkably complete.