chuk-mcp-open-meteo

by IBM

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.2.1
Disambiguation5/5
Every tool has a clearly distinct purpose. Singular and batch variants are explicitly separated by name and description, covering different domains (geocoding, weather, air quality, marine, historical, weather codes). No overlaps or confusion.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern using snake_case. Singular tools use lowercase names (e.g., geocode_location), batch tools are prefixed with 'batch_'. The pattern is predictable and uniform across all 12 tools.
Tool Count5/5
With 12 tools covering geocoding, multiple weather data types, and code interpretation with both singular and batch variants, the count is well-scoped. Each tool serves a needed function without redundancy.
Completeness5/5
The tool surface covers all major weather-related tasks: geocoding, current/forecast/historical weather, air quality, marine conditions (including tides), and weather code interpretation. Both singular and batch operations are provided, leaving no obvious gaps.
Average 4.6/5 across 12 of 12 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 25 commits in the last 12 weeks
- Last stable release on March 2, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose behavioral traits such as rate limits, authentication requirements, error handling, data freshness, or behavior with invalid coordinates. Only states the API source without additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with Args section and a practical example. Reasonably concise but the example adds value. Could be slightly more compact.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Provides an example referencing output fields (us_aqi) and mentions the return type (AirQualityResponse). However, with no output schema and 5 parameters, it lacks full coverage of return structure and error scenarios, leaving gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameters are listed with basic descriptions (latitude/longitude ranges, timezone hints). However, 'hourly' lacks allowed variable examples, and 'domains' lists only three options but not a full enumeration. With 0% schema coverage, the description should provide more detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Get air quality data and forecasts from Open-Meteo Air Quality API.' specifying verb, resource, and source. Distinguishes from sibling tools like batch_get_air_quality (batch) and other data-type tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage through example but provides no explicit guidance on when to use versus alternatives, no conditions or prerequisites, and no when-not-to-use advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It mentions the Open-Meteo Archive API but does not disclose rate limits, data availability, mutation risks, or response details. The behavioral context is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with 'Args', 'Returns', and 'Example' sections. Every sentence adds value, and the example illustrates usage clearly. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers parameters and return type well, but lacks explanation for hourly/daily variable formatting and error handling. Given no output schema, it provides a good but not exhaustive overview.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by detailing each parameter's purpose, format (e.g., 'YYYY-MM-DD'), ranges (e.g., '-90 to 90'), and defaults. It also explains the return type as a Pydantic model.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get historical weather data from Open-Meteo Archive API,' distinguishing it from sibling tools like 'get_weather_forecast' (forecast) and 'batch_get_historical_weather' (batch). The verb 'Get' and resource 'historical weather data' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly guides usage by specifying it retrieves historical data, contrasting with forecast-related siblings. However, it lacks explicit when-to-use and when-not-to-use instructions or alternative mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Describes return model, mentions Open-Meteo API, and weather codes. No mention of side effects or destructive behavior, but it's a read-only forecast.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections (overview, usage, args, returns, tips, examples). Slightly long but each part adds value. Front-loaded with purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but return object detailed. 10 parameters all documented. Usage tips and examples provided. Complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description thoroughly explains all 10 parameters with defaults, options, and examples. Includes popular hourly/daily variables and tips like using geocode_location for coordinates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Get comprehensive weather forecast with current conditions, hourly, and daily forecasts.' Distinguishes from sibling tools like get_marine_forecast, get_air_quality, get_historical_weather.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
States 'Use this for answering questions about current weather, future forecasts, or detailed conditions.' Provides tips for LLMs on when to use current_weather, hourly, daily. No explicit exclusions but context clear given distinct siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Discloses that results are in same order as input, all locations share settings. Does not mention destructive actions (none expected) or rate limits; could add data freshness or failure handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-organized with sections: description, args, returns, tips, example. Every sentence adds value. No unnecessary text. Length appropriate for complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description explains return structure (BatchAirQualityResponse with results and total_locations). Includes example usage. Could be slightly more thorough on error cases or limits, but adequate for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% coverage, but description provides full parameter details: format (comma-separated), examples, constraints (same count for lat/lng), defaults, and options for hourly and domains. This compensates completely for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Get air quality data' with specific resource 'multiple locations'. Differentiates from sibling tools like get_air_quality (single location) and batch_geocode_locations (geocoding). Explicitly states use case: comparing pollution across cities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: for multiple locations in one call, comparing across cities. Provides tip to use batch_geocode_locations first. Implicitly contrasts with single-location tool. No misleading guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, yet description fully discloses behavior: requires ocean coordinates, shares variables among locations, returns ordered results, uses Open-Meteo batch support, and explains default variable selection. Covers what to expect and what fails.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with intro, args, returns, tips, and example. Length is justified by complexity; no fluff. Slightly verbose with code example but adds clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers all necessary aspects: purpose, parameters, return format, constraints, usage tips, and example. No output schema, but return type is described sufficiently. Complete for a batch tool with 6 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% coverage, but description provides exhaustive parameter details: formats (comma-separated), examples, defaults, constraints (forecast_days 1-16, timezone 'auto'). Goes well beyond schema to explain usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it fetches marine forecasts for multiple coastal locations via batch API. Distinguishes from single-location get_marine_forecast and other batch tools like batch_get_weather_forecasts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides specific use cases (comparing surf spots, coastal trip planning) and tips (use batch_geocode_locations first, results in order, coordinates must be over ocean). Lacks explicit 'when not to use' but context is clear from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully covers behavioral details: input range (0-99), return model fields (code, description, severity, icon), error handling for unknown codes, and even map icon usage. This exceeds typical descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly long but well-structured with clear sections (Args, Returns, Common Codes, Tips, Example). Every section adds value, though some details (like map icon tip) could be condensed if brevity were prioritized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description is remarkably complete: it explains the input, output, error cases, common codes, and practical usage tips, leaving no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines weather_code as an integer, with 0% description coverage. The description adds full semantics: it's the 'weathercode' field from weather data, range 0-99, and includes a detailed list of common codes with their meanings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it interprets WMO weather codes (0-99) into human-readable descriptions, using a specific verb and resource. It distinguishes from sibling tools which fetch data (e.g., get_weather_forecast, geocode_location) rather than interpret codes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides tips for when to use the tool, such as explaining weather conditions and recommending activities based on severity. However, does not explicitly state when not to use it or name alternative tools for different needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries full responsibility. It discloses that all requests are made concurrently to reduce latency, mentions partial failures are normal, and recommends a maximum of 50 locations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections, examples, and tips, but it is somewhat lengthy. However, every sentence adds value, so it remains highly readable and organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of batch geocoding and the lack of an output schema, the description fully explains the return structure, including fields like query, found, results, and error. It also provides practical tips for handling failures and ordering.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description thoroughly explains each parameter: names (with examples and formatting), count (with usage recommendations), and language (default). This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it geocodes multiple location names to coordinates in a single call, distinguishing it from the sibling tool geocode_location by emphasizing batching and concurrency.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly advises when to use this tool (instead of calling geocode_location repeatedly) with examples like 'weather across the UK' and provides explicit tips for LLMs, such as using this first for multiple places and checking the 'found' field.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden. It discloses that all locations share the same date range and units, that results maintain input order, and describes the return structure. It also includes tips and examples, ensuring transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with sections (Args, Returns, Tips, Example) and covers all necessary information. While it is thorough, it could be slightly more concise without losing clarity. Still, it earns a 4 for its structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, no output schema, no annotations), the description is remarkably complete. It explains return format, behavior, constraints, and includes a concrete example, leaving no gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 0% description coverage, yet the description provides comprehensive parameter details: examples, constraints (e.g., comma-separated, equal lengths), defaults, and allowable values. It fully compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool fetches historical weather data for multiple locations in a single API call. It distinguishes itself from the sibling tool 'get_historical_weather' by emphasizing the batch capability, and from other batch tools by specifying the weather domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool (multiple locations, same date range) and when not to (different dates per location → use separate calls). It also recommends using batch_geocode_locations first, providing clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully discloses behavior: uses Open-Meteo native batch support, returns results in input order, shares parameters across locations, and is faster than repeated calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections but is somewhat lengthy. However, every sentence adds value, and the format is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 10 parameters, no output schema, and no annotations, the description is comprehensive: explains return type, workflow, constraints, and provides a full example. It leaves no significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides detailed explanations for all parameters, including examples, defaults, and the constraint that latitudes and longitudes must have equal comma-separated values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it gets weather forecasts for multiple locations in a single API call, distinguishing it from get_weather_forecast for single locations and mentioning batch geocoding as a prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises using batch_geocode_locations first, explains when to use this tool vs get_weather_forecast (batch vs different params per location), and notes the API limit of ~1000 locations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description fully discloses behavior: input format (comma-separated ints 0-99), output structure (BatchWeatherCodeResponse with results list and total_codes), and icon details for map integration.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-organized with sections (Args, Returns, Example) but contains slightly verbose map icon tip. Still efficient overall with no wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter, no output schema, and no annotations, the description covers input format, output structure, use case, and example. Complete enough for effective tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single parameter weather_codes has 0% schema description coverage, but the description explains its format (comma-separated integers, 0-99), provides examples, and adds map icon usage tip – far beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description starts with 'Interpret multiple WMO weather codes in a single call' – a specific verb+resource. It clearly distinguishes from sibling 'interpret_weather_code' by emphasizing batching.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use: 'instead of calling interpret_weather_code multiple times... pass all codes at once' and gives efficiency rationale for processing weather data for multiple locations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: it searches a worldwide database, returns results sorted by relevance, and details the retry logic on failure. It also explains what the return values contain, compensating for the lack of 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but well-structured with sections (Args, Returns, Tips, Examples). It is slightly verbose in places (e.g., repeating 'ALWAYS geocode'), but every sentence adds value. It could be more concise, but the structure compensates.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no output schema), the description is remarkably complete. It covers all parameters, return value structure, error handling workflow, and example use cases. The context signals indicate high complexity, and the description meets it fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It does so excellently by explaining each parameter in detail: name formats, count usage suggestions, language options, and format instructions. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Convert location names to coordinates and get detailed geographic information.' It distinguishes itself from sibling tools like weather tools by being a prerequisite, and from batch_geocode_locations by being the single-location version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Use this tool FIRST before calling weather tools' and includes a retry workflow for handling no results. It gives specific advice on count selection and how to handle ambiguous names, leaving no ambiguity about usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses coordinate adjustment to nearest ocean grid point, explains return structure (MarineForecast model), gives interpretation of wave parameters and tidal cycles. No annotations provided, so the description fully covers behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with headers, bullet points, and code examples, but is verbose. Could be trimmed without losing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a tool with no output schema: covers purpose, parameters, return values, common use cases, and best practices. Examples and tips fill any gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description provides detailed parameter explanations: latitude/longitude bounds and usage hint, timezone examples, hourly variable list with descriptions, daily options, forecast_days default. Adds immense value beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches ocean and marine weather forecasts including waves, swell, currents, and tides, with an explicit emphasis on tide predictions. It distinguishes itself from sibling tool 'get_weather_forecast' by warning not to include regular weather variables.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides extensive guidance: primary use for tides, common use cases (surfing, sailing, etc.), tips for LLMs, when not to use (e.g., weather variables), and alternative tools (geocode_location for coordinates, get_weather_forecast for weather).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![chuk-mcp-open-meteo MCP server](https://glama.ai/mcp/servers/IBM/chuk-mcp-open-meteo/badges/card.svg)](https://glama.ai/mcp/servers/IBM/chuk-mcp-open-meteo)

Score Badge

Copy to your README.md:

[![chuk-mcp-open-meteo MCP server](https://glama.ai/mcp/servers/IBM/chuk-mcp-open-meteo/badges/score.svg)](https://glama.ai/mcp/servers/IBM/chuk-mcp-open-meteo)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/IBM/chuk-mcp-open-meteo'

If you have feedback or need assistance with the MCP directory API, please join our Discord server