BCRP-MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_time_series_groupsA | Search for time series groups using one or multiple keywords. This function searches the BCRP (Banco Central de Reserva del Perú) database for time series groups that match the provided keywords. It returns a list of unique time series group names that contain or relate to the search terms. Args: keywords (List[str]): A list containing one or more keywords to search for. Each keyword should be a single word without spaces. Returns: List[str]: A list of unique time series group names that match the search criteria. Returns an empty list if no matches are found. |
| search_time_series_by_groupA | Search for time series within a specific group from the BCRP database. This function retrieves time series metadata from the BCRP (Banco Central de Reserva del Perú) database and filters it to find all time series that belong to a specific group. It returns a list of dictionaries containing the code and name of each matching time series. Args: time_series_group (str): The name of the time series group to search within. This should match or be contained within the "Grupo de serie" field in the BCRP metadata. Returns: List[Dict[str, str]]: A list of dictionaries where each dictionary contains: - "code": The unique identifier code for the time series - "name": The descriptive name of the time series If an error occurs, returns a list with a single dictionary containing an "error" key with the error message. |
| get_time_series_dataA | Get the data for a specific time series within a date range. This function retrieves time series data from the BCRP (Banco Central de Reserva del Perú) database for a specific time series code within the specified date range. The data is returned as a list of lists with dates formatted as 'YYYY-MM-DD'. Args: time_series_code (str): The unique code identifier for the time series. start (str): The start date for the data retrieval. Format should be '2020-1' for monthly data or '2020-1-1' for daily data. end (str): The end date for the data retrieval. Format should be '2020-1' for monthly data or '2020-1-1' for daily data. Returns: List[List[str]]: A list of lists where each inner list contains: [formatted_date, time_series_value] The date is formatted as 'YYYY-MM-DD' and the value is the corresponding data point for that date. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| search_data | |
| ask |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The three tools have clearly distinct purposes with no overlap: get_time_series_data retrieves actual time series data, search_time_series_by_group finds series within a specific group, and search_time_series_groups searches for group names. Each tool serves a different function in the data discovery and retrieval workflow.
All tools follow a consistent snake_case naming pattern with clear verb_noun structure: get_time_series_data, search_time_series_by_group, and search_time_series_groups. The naming convention is predictable and readable throughout the set.
Three tools is reasonable for a specialized BCRP data server, though it feels slightly minimal. The tools cover core discovery and retrieval workflows, but additional utilities like metadata lookup or data transformation might enhance completeness. The count is appropriate but could benefit from slight expansion.
The toolset provides good coverage for the BCRP time series domain with clear data retrieval and discovery capabilities. Minor gaps exist, such as no direct metadata lookup by code or data transformation tools, but agents can work effectively with the provided search and get operations for most common use cases.