latest_observationsReturns the single most-recent observed value per weather parameter (temperature, humidity, wind speed, etc.) for a named place in Finland from the FMI WFS real-time observation feed.
| Name | Required | Description | Default |
|---|---|---|---|
| place | Yes | ||
| parameters | No |
| Name | Required | Description | Default |
|---|---|---|---|
| place | Yes | Place name queried | |
| latest | Yes | Map of parameter names to latest {time, value} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds context that the data comes from a real-time feed, which implies values are dynamic and may change between calls. It consistently describes a read-only operation, with no contradictions.
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 sentence that front-loads the core action and value ('Returns the single most-recent observed value'), then adds essential context (location, source). No redundant or filler words; every clause earns its place.
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 an output schema present, return values need not be explained. The description covers purpose, place scope, parameter examples, and data source. It omits details like unit conventions or exact parameter string formatting, but the schema examples partially address this. Overall complete for a simple read tool.
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?
Schema description coverage is 0%, so the description must compensate. It provides meaning for 'place' (a named place in Finland) and gives examples of parameter values ('temperature, humidity, wind speed'). However, it does not explain the exact format of the 'parameters' string (e.g., comma-separated) or behavior when omitted, leaving some ambiguity.
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 states a specific verb ('Returns'), a clear resource ('single most-recent observed value per weather parameter'), and a specific scope ('for a named place in Finland'). It also names the data source (FMI WFS real-time observation feed), which distinguishes it from siblings like 'forecast' or 'recent_observations'.
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 implies the use case: retrieve the latest real-time observation for a place. However, it does not explicitly contrast with alternatives (e.g., 'recent_observations' for historical recent data, or 'forecast' for predicted values). No explicit when-not-to-use guidance is provided.
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 are well-differentiated, with detailed descriptions clarifying their distinct purposes. However, there is some overlap between the multiple 'ask' tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, suggest_questions) and between weather tools (forecast, latest_observations, recent_observations, warnings), which could cause confusion. Overall, ambiguity is low.
All 34 tool names follow a consistent lowercase_with_underscores (snake_case) pattern. Names are descriptive and predictable, such as 'ask_pipeworx', 'entity_profile', 'polymarket_arbitrage', etc. No mixing of conventions like camelCase or inconsistent verb styles.
The server has 34 tools, which is on the higher side given its broad scope covering weather, company research, prediction markets, and general data queries. While not excessive, it could be split into more focused servers for clarity. The count feels a bit heavy but still manageable.
The tool set covers a wide range of data domains (weather, company financials, prediction markets, news, memory, subscriptions) with reasonable completeness. Minor gaps exist, such as limited weather coverage (Finland only) and no direct support for non-company entities or unofficial data sources, but core workflows are well-supported.