Active wildfire incidents from the National Interagency Fire Center
Scored across 3 tools
Each tool targets a distinct aspect of wildfire data: active fire incidents, fire perimeters, and aggregate statistics. There is no overlap or ambiguity between the tools.
All tools follow the pattern 'get_' followed by a descriptive noun in snake_case (get_active_fires, get_fire_perimeters, get_fire_stats). The naming is perfectly consistent and predictable.
With only three tools, the server is minimal but each tool serves a clear and necessary purpose for wildfire data retrieval. The count is well-scoped for the narrow domain.
The server covers listing active fires, retrieving perimeters, and getting statistics. A minor gap is the lack of a direct way to fetch detailed information for a specific fire by name, though perimeters can be looked up by name.
Get current active wildfire incidents from NIFC.
Returns data on active wildfires including name, location, size, and
containment status. Data is updated frequently during fire season.
Args:
state: Two-letter US state abbreviation to filter by (e.g. 'CA', 'OR').
min_acres: Minimum fire size in acres to include (e.g. 100 for fires over 100 acres).
limit: Maximum number of fires to return (default 50).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| state | No | ||
| min_acres | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 data is updated frequently and describes the return fields, clearly implying a read-only operation. It could add more about potential delays or errors, but for a straightforward GET tool, 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 concise and well-structured, starting with a one-sentence purpose, a brief return description, and a clear Args section. Every sentence serves a purpose; no fluff.
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 covers purpose, parameters, return fields, and data source. Since an output schema exists, it doesn't need to detail return structure fully. It lacks explicit differentiation from siblings, but the purpose is clear enough for correct selection. Overall, very complete for a 3-parameter read tool.
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 no descriptions (0% coverage), so the description fully compensates by explaining each parameter: state (two-letter abbreviation with examples), min_acres (minimum size), and limit (maximum number, default 50). This adds critical meaning the schema lacks.
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 a specific verb ('Get') and clearly identifies the resource ('active wildfire incidents') and source (NIFC). It also lists the returned fields (name, location, size, containment status), making it distinct from sibling tools like get_fire_perimeters and get_fire_stats.
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 (current, active incidents, frequently updated during fire season) implying when to use it. However, it does not explicitly mention when not to use it or name alternative tools, so it earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get fire perimeter data for active or recent wildfires.
Returns perimeter boundaries and associated metadata. Useful for
understanding the geographic extent of fires.
Args:
fire_name: Name of a specific fire to look up (case-insensitive partial match).
state: Two-letter US state abbreviation to filter by.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | ||
| fire_name | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It clearly states the tool returns perimeter boundaries and metadata, which is useful. It does not disclose data source, update frequency, or authentication needs, but for a read-only retrieval tool this is a reasonable level of disclosure.
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 short and front-loaded with the core purpose in the first sentence. The 'Useful for understanding geographic extent' sentence is slightly redundant but harmless. Overall, no wasted words.
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 covers purpose, output type, and both parameters adequately. Since an output schema exists, return structure does not need to be re-described. It could have mentioned how to filter to recent fires globally, but the current information is sufficient for basic use.
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 no property descriptions (0% coverage), but the description provides Args that explain fire_name as a 'case-insensitive partial match' and state as a 'Two-letter US state abbreviation to filter by'. This adds meaningful semantics beyond the raw schema, though it does not specify behavior when both are omitted.
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?
Description states 'Get fire perimeter data for active or recent wildfires' – a specific verb+resource. It further clarifies it returns perimeter boundaries and associated metadata, distinguishing it from sibling tools like get_active_fires and get_fire_stats by focusing on geographic extent.
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 phrase 'Useful for understanding the geographic extent of fires' implies a use case, but the description does not explicitly say when to prefer this tool over siblings or mention exclusions. It gives some context but no direct alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get wildfire statistics summary including total fires and acres burned.
Returns aggregate statistics from active incident data. When no year is
specified, returns current year statistics.
Args:
year: Year to get statistics for (e.g. 2025). Defaults to current year.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the disclosure burden. It clearly states that results are aggregate statistics from active incident data and explains the default-year behavior. This adequately covers the behavioral scope for a read-only stats tool, though it does not discuss edge cases like years with no data.
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 compact and front-loaded with the primary purpose, followed by return behavior and parameter guidance. The Args block slightly duplicates schema information, but it adds a concrete example and the default-behavior detail, so each sentence earns its place.
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?
This is a simple one-parameter tool with an output schema, so the description does not need to explain return values. It covers the core purpose, data source, and default year behavior. It does not explicitly address sibling-tool selection, but that is a usage-guidance gap, not an operational completeness 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?
The schema only shows 'year' as an integer with a null default and no description. The tool description compensates with an Args block explaining that year is the target year (e.g., 2025) and defaults to the current year, adding meaningful 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 a specific verb and resource: 'Get wildfire statistics summary' and explicitly mentions 'total fires and acres burned.' It clearly distinguishes this aggregate-summary tool from sibling tools like get_active_fires and get_fire_perimeters.
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 by describing aggregate statistics and the default to the current year, but it does not explicitly state when to prefer this tool over get_active_fires or get_fire_perimeters. No alternatives or exclusions are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_active_firesget_fire_perimetersget_fire_statsLive worldwide wildfire detections, fire archive, citable stats, aircraft. Free, CC BY 4.0.
Federal disaster declarations, emergencies, and assistance data
Wildfire events and aggregates across global, U.S., and Canada sources. Paid via x402 USDC.
NASA EONET MCP — Earth Observatory Natural Event Tracker
