Environment Nature Mcp
Server Details
MCP server for Environment & Nature
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.4/5 across 4 of 4 tools scored. Lowest: 2.7/5.
Each tool has a clearly distinct purpose: air quality, weather, geocoding, and health check. No overlap between them.
All tool names follow the consistent verb_noun pattern with snake_case (get_air_quality, get_weather, health_check, search_location).
Four tools is well-scoped for an environment nature server, covering essential functionalities without being too sparse or bloated.
Core environmental data (air quality, weather) and location discovery are present, but some common features like UV index or pollen data are missing, which is a minor gap.
Available Tools
4 toolsget_air_qualityBInspect
Current air quality (PM2.5, PM10, ozone, AQI) for a city.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. It indicates 'current' but does not specify data freshness, permissions, rate limits, or any side effects. Lacks essential transparency for a data retrieval tool.
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, well-structured sentence that front-loads the main purpose. Every word is efficient and no redundant information.
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 (one parameter, no output schema), the description is minimally adequate but lacks usage guidelines and behavioral details. It does not fully equip an AI agent to use it confidently.
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%, but the description adds meaning to the sole parameter 'city' by stating it is a city for air quality data. This clarifies the input beyond the schema's generic string type.
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 returns current air quality data (PM2.5, PM10, ozone, AQI) for a city, using a specific verb and resource. It does not explicitly differentiate from sibling tools like get_weather, but the purpose is well-defined.
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?
No guidance is provided on when to use this tool versus alternatives (e.g., get_weather for weather data). There is no mention of appropriate contexts or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_weatherAInspect
Current weather + 3-day forecast for a city. Returns temps, wind, conditions.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes |
Tool Definition Quality
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 transparency. It discloses the tool returns current weather and a 3-day forecast with temps, wind, and conditions, indicating a read-only operation. However, it does not mention any potential side effects or limitations.
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 efficiently conveys the tool's purpose and output. Every part is meaningful, with no redundant information.
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 (one parameter, no output schema), the description covers the basic functionality. However, it lacks details on city format support or potential limitations, making it adequate but not fully 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?
The parameter 'city' has 0% schema description coverage, and the tool description does not elaborate on its format, validation, or examples. The description adds minimal value beyond the parameter name, failing to compensate for the lack of schema documentation.
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 retrieves current weather and a 3-day forecast for a city, specifying return fields (temps, wind, conditions). This distinguishes it from siblings like get_air_quality, which focuses on air quality parameters.
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 usage for weather data but provides no explicit guidance on when to use this tool versus alternatives or any usage restrictions. It is assumed the user wants weather information, but no exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
health_checkCInspect
Health check. FREE.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It does not disclose return format, side effects, or operational details (e.g., read-only nature). 'Health check' and 'FREE' add minimal behavioral insight.
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 extremely concise (two words plus 'FREE'), but this brevity comes at the cost of missing key details. It is front-loaded but under-specified.
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 simplicity (no params, no output schema), the description should clarify what the tool returns (e.g., status, success/failure). It fails to do so, leaving the agent without sufficient 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?
The input schema has zero parameters with 100% coverage, so the description is not required to explain parameters. It does not add extra meaning but also does not detract.
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 'Health check' indicates the tool's basic purpose of checking service health, but it lacks specificity. It is not a tautology because it adds 'FREE' but is still vague. It distinguishes from sibling tools (get_air_quality, get_weather, search_location) by being a health endpoint.
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?
No guidance is provided on when to use this tool versus alternatives. The description does not mention scenarios, prerequisites, or exclusions, leaving the agent without context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_locationAInspect
Find coordinates for a place by name (geocoding). FREE discovery. Returns {locations: [{name, country, latitude, longitude}]}
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| limit | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description discloses the return format and mentions it's free to use, but does not discuss rate limits, accuracy, or other behavioral traits. Barely adequate.
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: first states purpose, second shows return format. No wasted words, front-loaded with key information.
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 simple tool with two parameters and no output schema, the description covers the main points: purpose, return structure, and free usage. Could mention input format but not necessary.
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%. The description implies that 'name' is for the place name, but does not explain the 'limit' parameter or any format constraints. The description does not sufficiently compensate for the missing schema descriptions.
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 finds coordinates for a place by name (geocoding). It uses specific verbs and resource description, and differentiates from sibling tools which are weather and health related.
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 mentions 'FREE discovery' but provides no explicit guidance on when to use this tool versus alternatives. No exclusions or context for using it with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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