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get_coronal_mass_ejection

Retrieve coronal mass ejection data from NASA APIs to analyze space weather events and solar activity patterns within specified date ranges.

Instructions

Get coronal mass ejection (CME) data.

Args: start_date: Start date in YYYY-MM-DD format. Defaults to 30 days before current date. end_date: End date in YYYY-MM-DD format. Defaults to current date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateNo
end_dateNo

Implementation Reference

  • Main handler function for the 'get_coronal_mass_ejection' tool. Fetches CME data from NASA's DONKI/CME endpoint using make_nasa_request and formats the output using format_donki_results.
    async def get_coronal_mass_ejection(start_date: str = None, end_date: str = None) -> str:
        """Get coronal mass ejection (CME) data.
        
        Args:
            start_date: Start date in YYYY-MM-DD format. Defaults to 30 days before current date.
            end_date: End date in YYYY-MM-DD format. Defaults to current date.
        """
        params = {}
        if start_date: params["startDate"] = start_date
        if end_date: params["endDate"] = end_date
        
        url = f"{NASA_API_BASE}/DONKI/CME"
        data = await make_nasa_request(url, params)
        
        if not data: 
            return "Could not retrieve CME data due to a connection error."
        
        # Check for error response (must be a dictionary)
        if isinstance(data, dict) and "error" in data:
            return f"API Error: {data.get('error')} - Details: {data.get('details', 'N/A')}"
        if isinstance(data, dict) and data.get("binary_content"):
            return f"Received unexpected binary content from CME API. URL: {data.get('url')}"
    
        try:
            # Ensure data is a list for format_donki_results
            if not isinstance(data, list):
                logger.error(f"Unexpected non-list response from CME API: {data}")
                return "Received unexpected data format from CME API."
                
            return format_donki_results(data, "Coronal Mass Ejections", "activityID")
        except Exception as e:
            logger.error(f"Error processing CME data: {str(e)}")
            return f"Error processing coronal mass ejection data: {str(e)}"
  • Helper function used by get_coronal_mass_ejection (and other DONKI tools) to format the list of events consistently, including handling CME-specific fields like analyses.
    def format_donki_results(data: list, title_prefix: str, id_key: str) -> str:
        if not data: 
            return f"No {title_prefix.lower()} data for the specified period."
        
        result = [f"{title_prefix} found: {len(data)}"]
        display_limit = 10
        count = 0
    
        for item in data:
            if count >= display_limit:
                result.append(f"n... and {len(data) - display_limit} more entries.")
                break
            
            result.append(f"nID: {item.get(id_key, 'Unknown')}")
            # Add common fields if they exist
            if 'startTime' in item: result.append(f"Start Time: {item.get('startTime', 'Unknown')}")
            if 'eventTime' in item: result.append(f"Event Time: {item.get('eventTime', 'Unknown')}")
            if 'sourceLocation' in item: result.append(f"Source Location: {item.get('sourceLocation', 'Unknown')}")
            if 'note' in item: result.append(f"Note: {item.get('note', 'N/A')}")
            if 'link' in item: result.append(f"Link: {item.get('link', 'N/A')}")
            
            # Specific fields for different DONKI types can be added here if needed
            # Example for CME:
            if id_key == 'activityID' and 'cmeAnalyses' in item:
                analyses = item.get('cmeAnalyses', [])
                if analyses:
                    result.append("  Analyses:")
                    for analysis in analyses[:2]: # Limit analyses shown
                        result.append(f"    - Time: {analysis.get('time21_5', 'N/A')}, Speed: {analysis.get('speed', 'N/A')} km/s, Type: {analysis.get('type', 'N/A')}")
            
            # Example for GST:
            if id_key == 'gstID' and 'allKpIndex' in item:
                 kp_indices = item.get('allKpIndex', [])
                 if kp_indices:
                     result.append("  Kp Indices (first 2):")
                     for kp in kp_indices[:2]:
                         result.append(f"    - Time: {kp.get('observedTime', 'N/A')}, Index: {kp.get('kpIndex', 'N/A')}")
    
            # Linked Events
            linked_events = item.get('linkedEvents', [])
            if linked_events:
                result.append("  Related event IDs (first 5):")
                result.append("    " + ", ".join([le.get('activityID', 'N/A') for le in linked_events[:5]]))
    
            result.append("-" * 40)
            count += 1
            
        return "n".join(result)
  • General helper function used by all NASA API tools, including get_coronal_mass_ejection, to make HTTP requests to NASA APIs with API key handling, error management, and support for JSON/binary responses.
    async def make_nasa_request(url: str, params: dict = None) -> Union[dict[str, Any], List[Any], None]:
        """Make a request to the NASA API with proper error handling.
        Handles both JSON and binary (image) responses.
        """
        
        logger.info(f"Making request to: {url} with params: {params}")
        
        if params is None:
            params = {}
        
        # Ensure API key is included in parameters
        if "api_key" not in params:
            params["api_key"] = API_KEY
        
        async with httpx.AsyncClient() as client:
            try:
                response = await client.get(url, params=params, timeout=30.0, follow_redirects=True)
                response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)
    
                content_type = response.headers.get("Content-Type", "").lower()
                
                if "application/json" in content_type:
                    try:
                        return response.json()
                    except json.JSONDecodeError as json_err:
                        logger.error(f"JSON decode error for URL {response.url}: {json_err}")
                        logger.error(f"Response text: {response.text[:500]}") # Log beginning of text
                        return {"error": "Failed to decode JSON response", "details": str(json_err)}
                elif content_type.startswith("image/"):
                    logger.info(f"Received binary image content ({content_type}) from {response.url}")
                    # Return a dictionary indicating binary content was received
                    return {
                        "binary_content": True, 
                        "content_type": content_type,
                        "url": str(response.url) # Return the final URL after redirects
                    }
                else:
                    # Handle other unexpected content types
                    logger.warning(f"Unexpected content type '{content_type}' received from {response.url}")
                    return {"error": f"Unexpected content type: {content_type}", "content": response.text[:500]}
    
            except httpx.HTTPStatusError as http_err:
                logger.error(f"HTTP error occurred: {http_err} - {http_err.response.status_code} for URL {http_err.request.url}")
                try:
                    # Try to get more details from response body if possible
                    error_details = http_err.response.json()
                except Exception:
                    error_details = http_err.response.text[:500]
                return {"error": f"HTTP error: {http_err.response.status_code}", "details": error_details}
            except httpx.RequestError as req_err:
                logger.error(f"Request error occurred: {req_err} for URL {req_err.request.url}")
                return {"error": "Request failed", "details": str(req_err)}
            except Exception as e:
                logger.error(f"An unexpected error occurred: {str(e)}")
                return {"error": "An unexpected error occurred", "details": str(e)}
  • The @mcp.tool() decorator registers the get_coronal_mass_ejection function as an MCP tool.
    async def get_coronal_mass_ejection(start_date: str = None, end_date: str = None) -> str:
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the tool 'gets' data, implying a read-only operation, but doesn't mention rate limits, authentication needs, data format, pagination, or error handling. For a tool with no annotations, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded, with the purpose stated first followed by parameter details. It uses bullet points for clarity and avoids unnecessary information. However, the 'Args:' section could be slightly more integrated into the flow.

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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It covers parameters well but lacks context about the data returned, error cases, or how it fits with sibling tools. Without annotations or output schema, more behavioral details would improve completeness.

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 description adds substantial value beyond the input schema, which has 0% description coverage. It explains both parameters ('start_date' and 'end_date'), specifies their format ('YYYY-MM-DD'), and provides default values ('30 days before current date' and 'current date'). This fully compensates for the schema's lack of documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get coronal mass ejection (CME) data.' It specifies the verb ('Get') and resource ('coronal mass ejection data'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_solar_flare' or 'get_geomagnetic_storm', which are related solar/space weather tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, context for CME data retrieval, or any prerequisites. The only implicit usage hint is the date parameters, but this doesn't help the agent choose between similar 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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