recent_observationsReturns time-series weather observations for a named place in Finland over the last 1–48 hours (default 6 h), with one row per parameter per timestamp including lat/lon, parameter name, and value.
| Name | Required | Description | Default |
|---|---|---|---|
| hours | No | 1-48 (default 6) | |
| place | Yes | ||
| parameters | No |
| Name | Required | Description | Default |
|---|---|---|---|
| observations | Yes | Array of observation points from recent hours |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, non-destructive, idempotent, and open-world. The description adds behavioral details beyond the annotations: the exact time range, default value, and the row-level structure of the response (including lat/lon, parameter, and value). It stops short of discussing rate limits or pagination, but the added context is valuable.
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, information-dense sentence that is front-loaded with the core action and resource. Every clause adds meaningful detail (time window, default, output format, geographic scope), with no redundancy or filler.
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?
Given the tool's simplicity, the output schema, and the annotations, the description covers the essential information needed for selection and invocation: what it returns, for what place, over what time range, and the response structure. It could be slightly more complete by explicitly describing the 'parameters' argument format or noting an alternative like 'latest_observations', but the existing description is sufficient for most use cases.
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 low (33%—only 'hours' is described). The description compensates by clarifying that 'place' refers to a named place in Finland and by mentioning parameter names in the output, which implies the 'parameters' argument filters which parameters to include. However, it does not explicitly state the format for 'parameters' (e.g., comma-separated), leaving that to be inferred from examples.
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 uses the specific verb 'Returns', identifies the resource as 'time-series weather observations', and scopes it to 'a named place in Finland' with a time window (1–48 hours, default 6h) and output format (one row per parameter per timestamp, including lat/lon, parameter name, and value). This clearly differentiates it from siblings like 'latest_observations' and 'forecast'.
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 clearly establishes the intended use case: retrieving recent weather observations over a configurable historical window for a Finnish place. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to select it over a single-observation tool or forecast tool.
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.