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Polygon-io MCP Server

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get_futures_contract_details

Retrieve detailed information for a specific futures contract, including its attributes and pricing, at a specified point in time using Polygon-io MCP Server.

Instructions

Get details for a single futures contract at a specified point in time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNo
paramsNo
tickerYes

Implementation Reference

  • The handler function decorated with @poly_mcp.tool that implements the tool logic by calling the Massive API client and formatting the JSON response to CSV.
    async def get_futures_contract_details(
        ticker: str,
        as_of: Optional[Union[str, date]] = None,
        params: Optional[Dict[str, Any]] = None,
    ) -> str:
        """
        Get details for a single futures contract at a specified point in time.
        """
        try:
            results = massive_client.get_futures_contract_details(
                ticker=ticker,
                as_of=as_of,
                params=params,
                raw=True,
            )
    
            return json_to_csv(results.data.decode("utf-8"))
        except Exception as e:
            return f"Error: {e}"
  • The @poly_mcp.tool decorator registers this function as an MCP tool.
    async def get_futures_contract_details(
  • The function signature defines the input schema (parameters) and output type for the tool.
    ticker: str,
    as_of: Optional[Union[str, date]] = None,
    params: Optional[Dict[str, Any]] = None,
  • The json_to_csv helper function used to format the API response from JSON to CSV for all tools including this one.
    def json_to_csv(json_input: str | dict) -> str:
        """
        Convert JSON to flattened CSV format.
    
        Args:
            json_input: JSON string or dict. If the JSON has a 'results' key containing
                       a list, it will be extracted. Otherwise, the entire structure
                       will be wrapped in a list for processing.
    
        Returns:
            CSV string with headers and flattened rows
        """
        # Parse JSON if it's a string
        if isinstance(json_input, str):
            try:
                data = json.loads(json_input)
            except json.JSONDecodeError:
                # If JSON parsing fails, return empty CSV
                return ""
        else:
            data = json_input
    
        if isinstance(data, dict) and "results" in data:
            results_value = data["results"]
            # Handle both list and single object responses
            if isinstance(results_value, list):
                records = results_value
            elif isinstance(results_value, dict):
                # Single object response (e.g., get_last_trade returns results as object)
                records = [results_value]
            else:
                records = [results_value]
        elif isinstance(data, dict) and "last" in data:
            # Handle responses with "last" key (e.g., get_last_trade, get_last_quote)
            records = [data["last"]] if isinstance(data["last"], dict) else [data]
        elif isinstance(data, list):
            records = data
        else:
            records = [data]
    
        # Only flatten dict records, skip non-dict items
        flattened_records = []
        for record in records:
            if isinstance(record, dict):
                flattened_records.append(_flatten_dict(record))
            else:
                # If it's not a dict, wrap it in a dict with a 'value' key
                flattened_records.append({"value": str(record)})
    
        if not flattened_records:
            return ""
    
        # Get all unique keys across all records (for consistent column ordering)
        all_keys = []
        seen = set()
        for record in flattened_records:
            if isinstance(record, dict):
                for key in record.keys():
                    if key not in seen:
                        all_keys.append(key)
                        seen.add(key)
    
        output = io.StringIO()
        writer = csv.DictWriter(output, fieldnames=all_keys, lineterminator="\n")
        writer.writeheader()
        writer.writerows(flattened_records)
    
        return output.getvalue()
    
    
    def _flatten_dict(
        d: dict[str, Any], parent_key: str = "", sep: str = "_"
    ) -> dict[str, Any]:
        """
        Flatten a nested dictionary by joining keys with separator.
    
        Args:
            d: Dictionary to flatten
            parent_key: Key from parent level (for recursion)
            sep: Separator to use between nested keys
    
        Returns:
            Flattened dictionary with no nested structures
        """
        items = []
        for k, v in d.items():
            new_key = f"{parent_key}{sep}{k}" if parent_key else k
    
            if isinstance(v, dict):
                # Recursively flatten nested dicts
                items.extend(_flatten_dict(v, new_key, sep=sep).items())
            elif isinstance(v, list):
                # Convert lists to comma-separated strings
                items.append((new_key, str(v)))
            else:
                items.append((new_key, v))
    
        return dict(items)
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states what the tool does, without mentioning any behavioral traits like authentication requirements, rate limits, error conditions, response format, or whether it's a read-only operation. This leaves critical operational context unspecified.

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 extremely concise with just one sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it efficiently front-loaded with the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a financial data tool with 3 parameters, no annotations, no output schema, and 0% schema description coverage, the description is insufficiently complete. It doesn't explain what details are returned, how to interpret results, or provide necessary operational context for proper tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage and 3 parameters (ticker, as_of, params), the description provides minimal parameter context. It mentions 'at a specified point in time' which hints at the 'as_of' parameter, but doesn't explain the 'ticker' format, what 'params' might contain, or provide any examples. The description doesn't adequately compensate for the complete lack of schema 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 verb ('Get details') and resource ('for a single futures contract'), making the purpose specific and understandable. It distinguishes from siblings like 'get_futures_product_details' by focusing on contracts rather than products, but doesn't explicitly mention how it differs from other contract-related tools like 'list_futures_contracts'.

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?

No guidance is provided on when to use this tool versus alternatives. While the description implies it's for single contract details (versus listing multiple contracts), it doesn't explicitly state this distinction or mention other contract-related tools like 'get_futures_snapshot' or 'list_futures_contracts' as alternatives.

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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