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update_scan_schedule

Update a scan schedule's attributes: name, frequency, targets, tags, or throttling. Only provided fields are changed, leaving others unchanged.

Instructions

    Update an existing scan schedule. Only the provided fields are changed.

    Args:
        schedule_id: The ID of the scan schedule to update
        name: Rename the schedule
        first_scan_time: ISO 8601 timestamp, in the future and on the hour
        scan_frequency: One of 'daily', 'weekly', 'monthly', 'quarterly'
        target_ids: Replace the set of target IDs included in the schedule
        tag_names: Replace the set of target tag names included in the schedule
        throttled: Whether to throttle the scan
        web_ports_only: Only scan standard web ports
        upload_to_drata: Upload scan results to Drata
        upload_to_vanta: Upload scan results to Vanta
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schedule_idYes
nameNo
first_scan_timeNo
scan_frequencyNo
target_idsNo
tag_namesNo
throttledNo
web_ports_onlyNo
upload_to_drataNo
upload_to_vantaNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for update_scan_schedule. It's an async function decorated with @mcp.tool(). It accepts optional parameters to update a scan schedule, parses the ISO 8601 timestamp, calls api.update_scan_schedule(), and formats the result.
    @mcp.tool()
    async def update_scan_schedule(
        schedule_id: int,
        name: Optional[str] = None,
        first_scan_time: Optional[str] = None,
        scan_frequency: Optional[ScanFrequencyEnum] = None,
        target_ids: Optional[List[int]] = None,
        tag_names: Optional[List[str]] = None,
        throttled: Optional[bool] = None,
        web_ports_only: Optional[bool] = None,
        upload_to_drata: Optional[bool] = None,
        upload_to_vanta: Optional[bool] = None,
    ) -> str:
        """
        Update an existing scan schedule. Only the provided fields are changed.
    
        Args:
            schedule_id: The ID of the scan schedule to update
            name: Rename the schedule
            first_scan_time: ISO 8601 timestamp, in the future and on the hour
            scan_frequency: One of 'daily', 'weekly', 'monthly', 'quarterly'
            target_ids: Replace the set of target IDs included in the schedule
            tag_names: Replace the set of target tag names included in the schedule
            throttled: Whether to throttle the scan
            web_ports_only: Only scan standard web ports
            upload_to_drata: Upload scan results to Drata
            upload_to_vanta: Upload scan results to Vanta
        """
        parsed_first_scan_time = _parse_iso8601(first_scan_time) if first_scan_time is not None else None
        result = api.update_scan_schedule(
            schedule_id=schedule_id,
            name=name,
            first_scan_time=parsed_first_scan_time,
            scan_frequency=scan_frequency,
            tags=tag_names,
            targets=target_ids,
            throttled=throttled,
            web_ports_only=web_ports_only,
            upload_to_drata=upload_to_drata,
            upload_to_vanta=upload_to_vanta,
        )
        return _format_schedule_result("Updated", schedule_id, result)
  • The API client method that sends a PATCH request to /scans/schedules/{schedule_id}/ using PatchedAssessmentScheduleCreateUpdateRequest for serialization.
    def update_scan_schedule(self, schedule_id: int, name: Optional[str] = None,
                            first_scan_time: Optional[datetime] = None,
                            scan_frequency: Optional[ScanFrequencyEnum] = None,
                            tags: Optional[List[str]] = None, targets: Optional[List[int]] = None,
                            throttled: Optional[bool] = None, web_ports_only: Optional[bool] = None,
                            upload_to_drata: Optional[bool] = None, upload_to_vanta: Optional[bool] = None) -> dict:
        data = PatchedAssessmentScheduleCreateUpdateRequest(
            name=name, first_scan_time=first_scan_time, scan_frequency=scan_frequency,
            tags=tags, targets=targets, throttled=throttled, web_ports_only=web_ports_only,
            upload_to_drata=upload_to_drata, upload_to_vanta=upload_to_vanta,
        )
        return self.client.patch(f"{self.base_url}/scans/schedules/{schedule_id}/",
                                 json=data.model_dump(mode="json", exclude_none=True)).json()
  • PatchedAssessmentScheduleCreateUpdateRequest Pydantic model (all fields Optional) used for partial updates to a scan schedule.
    class PatchedAssessmentScheduleCreateUpdateRequest(BaseModel):
        name: Optional[str] = Field(None, min_length=1)
        first_scan_time: Optional[datetime] = None
        scan_frequency: Optional[ScanFrequencyEnum] = None
        tags: Optional[List[str]] = None
        targets: Optional[List[int]] = None
        throttled: Optional[bool] = None
        web_ports_only: Optional[bool] = None
        upload_to_drata: Optional[bool] = None
        upload_to_vanta: Optional[bool] = None
  • ScanFrequencyEnum enum with values: monthly, daily, weekly, quarterly.
    class ScanFrequencyEnum(str, Enum):
        MONTHLY = "monthly"
        DAILY = "daily"
        WEEKLY = "weekly"
        QUARTERLY = "quarterly"
  • The @mcp.tool() decorator on the update_scan_schedule function registers it as an MCP tool.
    @mcp.tool()
Behavior3/5

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

With no annotations, the description provides some behavioral context (partial update semantics) and parameter constraints (e.g., ISO 8601 format for timestamps). However, it does not disclose required permissions, error behavior, or atomicity of updates.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly long (11 lines) and structured like a docstring with parameter list. It front-loads the purpose but could be more concise without losing 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?

Although an output schema exists, the description does not mention return values or output format. It lacks information on error handling, side effects, and prerequisites. For a tool with 10 parameters and no schema descriptions, more contextual completeness is needed.

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

Parameters4/5

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

Schema description coverage is 0%, but the description adds significant value by detailing each parameter's meaning and constraints (e.g., 'ISO 8601 timestamp, in the future and on the hour'). For basic boolean parameters, it adds minimal value beyond the schema, but overall it compensates well.

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

Purpose5/5

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

The description clearly states 'Update an existing scan schedule' which specifies the verb and resource. It distinguishes from sibling tools like create_scan_schedule and delete_scan_schedule by implying modification rather than creation or deletion.

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 implies that this tool is for modifying existing schedules via partial update ('Only the provided fields are changed'). However, it does not explicitly state when to use this vs. create or delete, nor does it mention prerequisites or when not to use it.

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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