MeteoSwiss MCP Server

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

With no annotations, the description carries the full burden. It discloses that the id must be a full URL discovered via the search tool, that the tool converts content to markdown or plain text, and that it retrieves 'full content'. It does not mention side effects, but for a fetch operation the read-only nature is implicit and sufficient given the output schema.

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: the first states the purpose and output, the second provides the prerequisite workflow. No filler words, front-loaded with the actionable verb.

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?

The tool is a simple fetch with an output schema and fully documented parameters. The description covers the core operation and the critical workflow constraint, but does not elaborate on error handling or rate limits, which are less essential given the schema richness.

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 input schema already documents all three parameters with descriptions, reaching 100% coverage, so the description is not required to elaborate. It does reinforce the id requirement (full URL from search tool), but adds no new semantics 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 uses specific verbs ('Fetch', 'convert') and identifies the resource ('MeteoSwiss webpage') and output formats (markdown/plain text). It clearly distinguishes this tool from the data-specific sibling tools (climate, weather, forecast, pollen, stations) and the search tool.

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 explicitly instructs to use the search tool first to discover valid page URLs before calling fetch, establishing a clear workflow. It does not explicitly state exclusions for using data tools, but the workflow guidance is strong enough for an agent to select this tool appropriately.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the data source (NBCN), the resolutions available (daily, monthly, yearly), the number and type of stations, and the accepted input formats. It falls short of mentioning potential error behavior, date range limitations (beyond 'going back decades'), or any data adjustments (e.g., 'homogeneous' implies homogenization but isn't explained). Overall, it is transparent about what the tool returns and how to invoke it.

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 moderately long but well-organized: it opens with a direct statement of purpose, then details network and resolution, provides use cases, and ends with input formats. Every sentence earns its place, and the structure helps the agent quickly grasp the tool's scope. It is slightly verbose in the station count detail, but that is useful context for selecting the tool.

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 complexity (6 parameters, nested coordinates object, output schema), the description covers the essential aspects: data content, geographic scope, resolution options, station types, and input alternatives. It does not explain the output structure, but an output schema exists to handle that. It also omits specific date range limits or station list details, but the sibling 'meteoswissStations' likely covers that. Overall, it is sufficiently complete for an agent to understand when and how to invoke it.

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 input schema already describes all parameters with 100% coverage, giving a baseline of 3. The description adds value beyond the schema by providing concrete examples of valid station abbreviations ('SMA', 'BAS'), clarifying the meaning of 'homogeneous' (though not fully), summarizing resolution differences (daily temp min/max/mean, monthly full summary, yearly annual summary), and illustrating the precedence of coordinates over station. This extra semantic context justifies a score above baseline.

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 tool's function: 'Get homogeneous climate measurement series from Switzerland's National Basic Climatic Network (NBCN)' and enumerates the types of data returned (temperature, precipitation, sunshine, radiation, wind, pressure, climate indicators). It is specific about the verb (Get), resource (climate series), and scope (Switzerland, NBCN), and it effectively distinguishes itself from sibling tools by focusing on historical climate data rather than current weather or forecasts.

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 usage context through example use cases ('What are typical January temperatures in Zurich?') and input options (station names, abbreviations, coordinates). It does not explicitly state when not to use the tool or name alternative tools, but the context strongly implies this for historical climate queries versus current conditions. A clear exclusion statement would make it a 5.

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

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: station coverage (~300 stations, with ~140 being precipitation-only), data update frequency (every 10 minutes), the limitation that precipitation-only stations return only rainfall, and that 8 specific stations also include daily visual observations. It also mentions automatic nearest-station matching. This goes well beyond a minimal description, though it could still detail error handling or output format, but the output schema covers the latter.

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 concise and well-structured, with three short paragraphs: general data, special observations, and input methods. Every sentence adds value and no redundant information exists. It is front-loaded with the core purpose and quickly covers important details.

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 two optional parameters, no annotations, and the presence of an output schema, the description is remarkably complete. It explains what data is returned, the update interval, station types and limitations, special observational additions, and all accepted input formats. Nothing critical for invocation appears missing.

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 coverage is 100%, so baseline is 3. The description adds meaningful semantics beyond the schema: it explains that station accepts names, abbreviations, and addresses (with examples), and that coordinates are an alternative. It also adds the key behavior of automatically finding the nearest station, which is not in the schema. This enhances understanding of both parameters.

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 tool provides 'real-time weather measurements' from Swiss automatic weather stations, listing specific data types and station counts. This distinctively separates it from sibling tools like meteoswissLocalForecast (forecast), meteoswissClimateData (historical), and meteoswissStations (station metadata). The verb 'Get' and specific resource are explicit.

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 gives clear context for when to use this tool: for current, real-time observations updated every 10 minutes. It also details input methods (station names, abbreviations, addresses, coordinates) and automatic nearest-station selection. However, it does not explicitly mention alternatives or state 'when not to use', so it lacks explicit exclusions that would earn a 5.

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

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

With no annotations provided, the description fully carries the behavioral transparency burden. It explains timezone handling ('NOT UTC — do not convert it'), zero-hour inclusion, null-field semantics, empty-array vs null distinctions, and the expected mismatch between official station daily aggregates and hourly-derived values.

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 long, but it is well-structured with clear paragraphs and bullet-like lists. Every section addresses a real need for safe invocation and output interpretation, and the core purpose is front-loaded in the first sentence.

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?

The description is complete for this tool's complexity, covering input variants, output structure, edge cases, and data quirks. Given the output schema exists, the description goes beyond what is minimally required and would allow an agent to correctly invoke the tool and interpret results without additional context.

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?

Even though schema description coverage is 100%, the description adds substantial meaning beyond the schema by providing concrete location examples, forecast horizon details, field definitions for the returned summaries, and hourly object semantics. This helps an agent accurately construct inputs and interpret outputs.

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 opens with a specific verb and resource: 'Get a multi-day weather forecast for any Swiss location', and explicitly lists what is returned (daily summaries plus a hierarchical hourly breakdown). This clearly differentiates the tool from siblings like meteoswissCurrentWeather and meteoswissClimateData.

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 gives clear context for when to use the tool: multi-day forecasts for Swiss locations, including accepted input formats, coverage of ~6000 locations, forecast horizon, and update frequency. However, it does not explicitly name sibling alternatives or state when not to use this tool, so it falls just short of a 5.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable details: all 7 species are always included, out-of-season species show a 'no-current-data' status, and ambrosia is excluded because it is forecast-only. This goes beyond the schema and clearly explains edge cases and limitations, giving the agent a realistic expectation of the output.

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 three sentences long, with the primary purpose front-loaded in the first sentence. The following sentences add crucial context (species list, out-of-season behavior, exclusion) without any filler. Every sentence earns its place, making it both concise and informative.

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?

An output schema exists, so return value details are covered separately. The description covers the station network size, exact species measured, the always-included behavior with a no-data status, and an explicit exclusion. It gives a complete picture of what the tool does and its quirks, leaving no significant gaps for an agent to fill.

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 coverage is 100% for the only parameter ('station'), and the schema description already explains omission behavior. The tool description does not add any extra parameter-specific semantics; it remains entirely at the data-level. A baseline score of 3 is appropriate since the schema handles the parameter documentation adequately.

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 tool's function: 'Get current pollen concentration data from MeteoSwiss monitoring stations.' It specifies the resource (MeteoSwiss stations), the action (get), and the scope (current pollen concentration, ~15 stations). It also distinguishes from siblings by listing the 7 measured species, making it unmistakable for pollen 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 a clear use case: 'Useful for allergy sufferers.' While it doesn't explicitly contrast with sibling tools (e.g., meteoswissCurrentWeather) or state when not to use it, the domain is so specific that the usage context is clear. No explicit alternatives or exclusions are mentioned, but the context is sufficient for an agent to decide.

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

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

No annotations are provided, so the description carries the behavioral transparency burden. It discloses the scope of the tool ('~300 stations across Switzerland') and breaks down station types ('~160 full weather + ~140 precipitation-only'), which adds useful context beyond a generic listing tool. It doesn't mention rate limits or side effects, but for a read-only catalog, this is sufficient.

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 two sentences: the first states the primary action and resource, the second adds network composition detail. No wasted words, and the most important information is front-loaded. It earns every word it uses.

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?

For a list/search tool with an output schema, the description covers the essential aspects: purpose, filtering options, and network scope. The output schema handles return format, and sibling names clarify this is the station reference tool. No significant gaps remain.

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 input schema has 100% coverage with descriptions for all three parameters (limit, canton, search), including examples. The description reiterates the filtering concepts ('Filter by name, canton') but adds little beyond what the schema already captures. Baseline 3 is appropriate since the 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 tool's function: 'List and search MeteoSwiss measurement stations.' It names the specific resource (MeteoSwiss stations) and the actions (list, search), and distinguishes it from sibling tools like meteoswissClimateData or meteoswissCurrentWeather by focusing on station metadata.

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?

It provides clear usage context: filter by name/canton or browse the full network. While it doesn't explicitly mention alternatives or when-not-to-use, the sibling tools are clearly for different data (weather, climate, pollen), so the station-focused purpose implicitly guides selection. The context is clear, but explicit exclusions are absent.

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

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 the fixed 10-result page size and the upstream duplication limitation, which are critical behavioral traits. It does not cover every edge case, but for a search tool these are the key operational constraints.

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 three sentences, front-loaded with the purpose, followed by behavioral notes and a limitation. Every sentence earns its place, with no redundant content.

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 and rich parameter descriptions, the description covers the essential operational quirks (fixed page size, duplicates) and the workflow (pass URLs to fetch). It could mention sort behavior, but that is already in the schema, so the description is sufficiently complete.

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 five parameters are fully documented in the schema (100% coverage), so the description adds only marginal value beyond the schema. It mentions language support and the fixed page size, but these are already reflected in parameter descriptions, so baseline 3 is appropriate.

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 'Search MeteoSwiss website content' with specific verb and resource, and notes multi-language support. It distinguishes itself from sibling tools like meteoswissCurrentWeather and meteoswissClimateData, which focus on data rather than website content.

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 explicitly mentions that returned URLs can be passed to the fetch tool, establishing a clear usage workflow. It does not explicitly name alternatives or exclusions, but the tool's scope is evident from sibling names, so guidance is clear despite lacking explicit 'when not to use' statements.

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