Tools

A tool is something an agent may decide to call while it answers: run a query, download a file, run one of your functions, call an external API, send an email. The model chooses when to call a tool and with which arguments; Wavefront runs it and gives the result back to the model.

Available tools

Tool	Does
`datasource_execute_query`	Runs a dynamic query
`datasource_insert_rows`	Inserts rows into a table
`datasource_insert_multi`	Inserts into several tables in one transaction
`datasource_download_file`	Loads a file; images and video reach the model as media
`send_email`	Sends an email from a connected mailbox
your functions	Every function is available as a tool, by its name
your API services	Every endpoint defined under API Services is available as a tool

The same functions also exist as function nodes for workflows, with the same parameters.

Giving an agent tools

Console
YAML

Open the agent, choose Edit, and use Add Tools to pick tools. Tools that need fixed values (such as which datasource and query) ask for them, and the console writes the tools into the agent’s YAML.

agent:
  name: order-assistant
  job: |
    Answer questions about orders. Look the order up with the order lookup
    tool, passing the order id as params.order_id. Never guess.
  model: { provider: rootflo, model_id: <llm-config-id> }
  settings:
    temperature: 0
  tools:
    - name: datasource_execute_query
      prefilled_params:                 # fixed: the model cannot change these
        datasource_id: <datasource-id>
        query_id: order_detail
      name_override: order_lookup       # optional: the name the model sees
      description_override: Look up one order and its lines by order id

tools is a list of objects with a name; a list of plain strings makes the agent fail to run.
prefilled_params fix arguments the model must not choose: which datasource, which query, which mailbox. The model supplies the rest.
name_override and description_override change what the model sees, which helps it pick the right tool.
Say in the agent’s instructions when to use each tool and how to fill its arguments.

You can also add tools for a single run with "tool_names": [...] in the inference request.

Tools or function nodes?

Both run the same code. The difference is who decides:

	Tool	Function node
Who decides to run it	The model, during its answer	The workflow, always, in order
Model calls	One per tool round trip; earlier messages, including files, are sent again on each	The agent answers in one call with everything already loaded
Predictability	Varies with the model	Same every run
Best for	Conversations and open questions (“which order are you asking about?”)	Processes: review this record, check these documents

For processes, prefer function nodes: load the data and files first, then give the agent a parser and one call. It is faster, cheaper, and easier to audit, and it avoids the known issue below.

Known issues

An agent cannot have both tools and a parser (#447). The model calls the parser as if it were a tool and the run fails. Use one or the other, or load data with function nodes as described above.

Datasource tools report failures to the model as text (“Insert failed (400): …”) rather than raising an error (#448). The model may carry on as if the call had worked; instruct it to report tool failures in its answer.

Getting started

Models and agents

Workflows

Functions

Data, files and configuration

Run and integrate

Guides

Patterns and troubleshooting

Operations

Available tools

Giving an agent tools

Tools or function nodes?

Known issues

​Available tools

​Giving an agent tools

​Tools or function nodes?

​Known issues

Available tools

Giving an agent tools

Tools or function nodes?

Known issues