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get_location_time_series

Retrieve historical location data over time with configurable sampling, distance filtering, and reverse geocoding options for analysis.

Instructions

Retrieve a time series of locations that the user was at. Result timestamps will include time zones. Always translate timestamps to the user's local tz when this is known.

Args: start_time: The start of the time range (inclusive), as an ISO 8601 string or datetime object. end_time: The end of the range (exclusive), as an ISO 8601 string or datetime object. change_meters: Optional. When specified, subsequent samples that are fewer than this many meters away will not be included. sample_rate: Optional. The length (in seconds) of each sample. Default is 900. reverse_geocode: Optional. When true, Fulcra will attempt to reverse geocode the locations and include the details in the results. Default is False. Returns: A JSON string representing a list of location data points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_timeYes
end_timeYes
change_metersNo
sample_rateNo
reverse_geocodeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool(), implementing the core logic to fetch location time series data from FulcraAPI and return it as JSON string.
    @mcp.tool()
    async def get_location_time_series(
        start_time: datetime,
        end_time: datetime,
        change_meters: float | None = None,
        sample_rate: int | None = 900,
        reverse_geocode: bool | None = False,
    ) -> str:
        """Retrieve a time series of locations that the user was at.
        Result timestamps will include time zones. Always translate timestamps to the user's local tz when this is known.
    
        Args:
            start_time: The start of the time range (inclusive), as an ISO 8601 string or datetime object.
            end_time: The end of the range (exclusive), as an ISO 8601 string or datetime object.
            change_meters: Optional. When specified, subsequent samples that are fewer than this many meters away will not be included.
            sample_rate: Optional. The length (in seconds) of each sample. Default is 900.
            reverse_geocode: Optional. When true, Fulcra will attempt to reverse geocode the locations and include the details in the results. Default is False.
        Returns:
            A JSON string representing a list of location data points.
        """
        fulcra = get_fulcra_object()
        kwargs = {}
        if change_meters is not None:
            kwargs["change_meters"] = change_meters
        if sample_rate is not None:
            kwargs["sample_rate"] = sample_rate
        kwargs["look_back"] = 14400
        if reverse_geocode is not None:
            kwargs["reverse_geocode"] = reverse_geocode
    
        location_series = fulcra.location_time_series(
            start_time=start_time,
            end_time=end_time,
            **kwargs,
        )
        return f"Location time series from {start_time} to {end_time}: " + json.dumps(
            location_series
        )
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: result timestamps include time zones with translation guidance, and it explains optional filtering ('change_meters') and enhancement ('reverse_geocode'). However, it misses details like rate limits, error handling, or data freshness, which could be important for a location-tracking tool.

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 well-structured with clear sections (purpose, args, returns) and uses bullet-like formatting for parameters. It's appropriately sized, but the first sentence could be more front-loaded with key details, and some phrasing (e.g., 'Always translate...') might be slightly verbose without adding critical info.

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

Completeness4/5

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

For a tool with 5 parameters, no annotations, and an output schema, the description is mostly complete. It covers purpose, parameters, and return format adequately. However, it lacks context on permissions, data sources, or sibling tool differentiation, which could enhance usability given the server's other location/metric tools.

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?

Given 0% schema description coverage, the description compensates fully by providing detailed semantics for all 5 parameters. It explains each parameter's purpose, format (e.g., ISO 8601), optionality, defaults, and effects (e.g., 'reverse_geocode' adds details). This adds significant value beyond the bare schema, making parameters well-understood.

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: 'Retrieve a time series of locations that the user was at.' This specifies the verb ('retrieve') and resource ('time series of locations'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_location_at_time' or 'get_metric_time_series', which prevents a perfect score.

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

Usage Guidelines3/5

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

The description provides some implicit guidance by mentioning 'Always translate timestamps to the user's local tz when this is known,' suggesting usage when timezone-aware location data is needed. However, it lacks explicit when-to-use instructions, alternatives (e.g., vs. 'get_location_at_time'), or exclusions, leaving gaps in practical application.

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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