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c-cf

IMF Data MCP Server

by c-cf

fetch_bop_data

Retrieve time series data from the IMF Balance of Payments database by specifying frequency, country, indicator, and date range.

Instructions

Retrieves compact format time series data from the BOP database based on the input parameters.

Args:
    freq (str): Frequency (e.g., "A" for annual, "Q" for quarterly, "M" for monthly).
    country (str): Country code, multiple country codes can be connected with "+".
    indicator (str): Indicator code.
    start (str | int): Start year.
    end (str | int): End year.

Returns:
    str: Description of the queried data. Do not perform further analysis or retry if the query fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
freqYes
countryYes
indicatorYes
startYes
endYes

Implementation Reference

  • The main handler function for the 'fetch_bop_data' tool. It is registered via the @mcp.tool() decorator and implements the logic to fetch Balance of Payments (BOP) time series data from the IMF API, process it using process_imf_data, or return an error.
    @mcp.tool()
    def fetch_bop_data(freq: str, country: str, indicator: str, start: str | int, end: str | int) -> str:
        """
        Retrieves compact format time series data from the BOP database based on the input parameters.
    
        Args:
            freq (str): Frequency (e.g., "A" for annual, "Q" for quarterly, "M" for monthly).
            country (str): Country code, multiple country codes can be connected with "+".
            indicator (str): Indicator code.
            start (str | int): Start year.
            end (str | int): End year.
    
        Returns:
            str: Description of the queried data. Do not perform further analysis or retry if the query fails.
        """
        dimensions = f"{freq}.{country}.{indicator}"
        url = f"http://dataservices.imf.org/REST/SDMX_JSON.svc/CompactData/BOP/{dimensions}?startPeriod={start}&endPeriod={end}"
        try:
            response = requests.get(url)
            response.raise_for_status()
            data = response.json()
    
            return process_imf_data(data)
        except Exception as e:
            return f"Error fetching BOP data: {str(e)}"
  • Helper utility function imported and used by fetch_bop_data (and other tools) to parse the IMF JSON response into a human-readable string summary of the time series data.
    def process_imf_data(json_data: dict) -> str:
        """
        Process IMF data and return a string with the information.
        :param:
            json_data(dict): JSON data from the IMF API
        :return:
            (str) A string with the information from the JSON data
        """
        try:
           
            json_data = json_data["CompactData"]
            dataset = json_data["DataSet"]
    
            series_list = dataset["Series"]
            if isinstance(series_list, dict):
                series_list = [series_list]
            elif not isinstance(series_list, list):
                return f"Error: Expected series_list to be a list but got {type(series_list)}"
    
            output_texts = []
            
            for series in series_list:
                if series is None:
                    output_texts.append("Warning: No indicator value.")
                    continue
                country = series.get("@REF_AREA", None)
                obs = series.get("Obs", {})
                if isinstance(obs, dict):
                    obs = [obs]
                elif not isinstance(obs, list):
                    return f"Error: Expected obs to be a list but got {type(obs)}"
                for _obs in obs:
                    if _obs is None:
                        output_texts.append(
                            f"Warning: No indicator value for {country} in that Year, You should not try to access the data of this country."
                        )
                        continue
                    time_period = _obs.get("@TIME_PERIOD", "that Year")
                    obs_value = _obs.get("@OBS_VALUE")
                    
                    if obs_value is not None:
                        text = f"In {time_period}, {country} had an indicator value of {float(obs_value):.2f}."
                        output_texts.append(text)
                    else:
                        output_texts.append(f"Warning: No indicator value for {country} in {time_period}.")
            
            return "\n".join(output_texts)
        except KeyError as e:
            return f"Error processing IMF data: Missing key {str(e)}"
        except Exception as e:
            return f"Error processing IMF data: {str(e)}"
  • The @mcp.tool() decorator registers the fetch_bop_data function as an MCP tool, using the function name as the tool name.
    @mcp.tool()
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that data is retrieved in 'compact format' and includes a behavioral note in the Returns section: 'Do not perform further analysis or retry if the query fails.' This adds useful context about error handling. However, it doesn't mention rate limits, authentication needs, or what happens with invalid parameters, leaving gaps for a tool with 5 required parameters.

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 and appropriately sized. It starts with a clear purpose sentence, followed by an Args section with bullet-like parameter explanations, and ends with a Returns section. Every sentence adds value, with no wasted words or redundancy.

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 5 required parameters, no annotations, and no output schema, the description does a decent job but has gaps. It explains parameters well and includes a behavioral note on failure handling. However, it doesn't describe the return format beyond 'description of the queried data' (vague), and lacks context on data scope or limitations. For a data retrieval tool with multiple siblings, more differentiation would help.

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%, so the description must compensate fully. It provides detailed semantics for all 5 parameters: freq with examples ('A' for annual), country with format details (multiple codes with '+'), indicator as a code, and start/end as years. This adds significant meaning beyond the bare schema, effectively documenting parameter usage.

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 'retrieves compact format time series data from the BOP database based on input parameters,' which specifies the verb (retrieves), resource (time series data from BOP database), and format (compact). However, it doesn't explicitly differentiate from sibling tools like fetch_cdis_data or fetch_ifs_data, which likely retrieve similar data from different databases.

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 its siblings (e.g., fetch_cdis_data, fetch_ifs_data). It mentions 'based on input parameters' but doesn't clarify what makes BOP data distinct or when to choose this over alternatives. The only usage hint is in the Returns section about not retrying on failure, but this doesn't address tool selection.

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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