Polish Academic MCP

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

No annotations are provided, so the description carries the full burden. It discloses important behavioral traits: the data format (JSON), the scope of data (all active stations or single station), the refresh frequency (roughly every hour), and the precedence rule for parameters. It doesn't mention error handling, rate limits, or authentication requirements, but covers key operational aspects well.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by details on parameters and behavior. Every sentence earns its place by providing essential information without redundancy. The structure flows logically from general to specific.

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 moderate complexity (2 optional parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, key behaviors, and parameter semantics. However, it doesn't describe the JSON structure of the return data (e.g., fields like temperature, wind speed), which would be helpful since there's no output schema. For a data retrieval tool, this is a minor gap.

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 100%, so the schema already documents both parameters thoroughly. The description adds some value by reiterating the precedence rule and providing examples (e.g., '12500 for Jelenia Góra', 'jeleniagora'), but doesn't add significant meaning beyond what the schema provides. Baseline 3 is appropriate when schema does the heavy lifting.

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 the verb ('retrieve'), resource ('current synoptic weather station readings'), and source ('IMGW-PIB'). It distinguishes this tool from its sibling tools (like imgw_hydro, imgw_meteo, imgw_warnings) by specifying it returns synoptic station data with specific weather parameters, not hydrological, meteorological, or warning data.

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 provides clear context on when to use this tool: to get weather readings for all active synoptic stations in Poland or for a single station when station_id or station_name is provided. It explains the precedence rule if both parameters are given. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.