Tool Calling and Plugins for Heating Marketing Content

What the data for this category looks like

Heating category data comes from three types of data sources: heating operation management system, user heat metering terminals, and offline marketing outreach platform. Data update cadence is layered: pipe network operating parameters update every 10 seconds, user heat metering data updates every hour, and marketing outreach and conversion data updates daily.

Single data documents use standard JSON structure, including the following fields:

device_id: unique device identifier, string type
timestamp: data collection timestamp, ISO 8601 format
heat_load: real-time heat load, numeric type, unit megawatt
region_code: service area code, string type
touch_count: daily user outreach count, integer type
conversion_count: daily user conversion count, integer type

What constraints these characteristics impose on tool calling and plugins

Layered update cadence requires tools to support multi-dimensional polling interval configuration. Set short pull intervals for real-time pipe network data. Use long intervals or on-demand triggers for batch historical data. This avoids triggering API rate limits from frequent calls.

The multi-field layered structure requires plugins to support data filtering by device ID or region code. Without this support, precise outreach requirements of marketing scenarios cannot be met.

Scattered data sources require tools to support multiple authentication methods. This allows connection to different API protocols of the metering system and marketing platform respectively.

Large single-batch data volume requires plugins to support paginated pulling and batch processing. This prevents single request timeouts or exceeded return data limits.

How to set configurations

Configuration Item	Recommended Value	Rationale
`polling_interval`	10 seconds (pipe network data), 3600 seconds (user heat data)	Matches the actual update cadence of the corresponding data source, avoids invalid calls or data delays
`data_filter_dimension`	`device_id` or `region_code`	Data is stored layered by device and region, needs to match the precise filtering requirements of marketing scenarios
`batch_fetch_size`	50 entries	Balances API response speed and data processing efficiency, avoids single request timeouts
`unit_conversion_enabled`	`true`	The original data unit is megawatt, needs to be converted to the heat measurement unit commonly used in marketing scenarios
`timeout_threshold`	600 seconds	Adapts to the maximum response duration for full batch pulling of historical data
`auth_credential`	Fill in according to the key provided by the data source	Completes identity verification between the tool and the data source to ensure normal connection

The parameter values given on this page are all common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.

Three common mistakes

A 429 Too Many Requests error is returned after calling the tool. The cause is that the polling interval is not set according to the data source update cadence, and frequent pulling of heat pipe network data triggers API rate limits.
The conversion_count field returned by the tool is empty. The cause is that data_filter_dimension is not configured to the corresponding dimension, and the regional or device data of marketing outreach is not associated, resulting in failure to match the corresponding conversion data.
The tool displays "Temporary Status" after activation. The cause is that the correct data source authentication key is not configured, or the authentication information has expired, resulting in the tool being unable to establish a normal connection.

How to confirm the configuration is complete

Check the data source connection status in the tool configuration interface, confirm that it shows "Connected" and does not show "Temporary".
Initiate a single test call, check that the returned data fields include preset fields such as device_id and heat_load, and that the unit matches the configured conversion result.
Simulate a batch pull request, check that the response duration does not exceed the set timeout_threshold value.
Adjust polling_interval to different values, verify that the time interval for the tool to pull data matches the expected configuration.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.