Advanced usage

Enable and use every option in an Agent Evaluator's connectors menu: web search, connectors, knowledge bases, data injection, and summaries

An Agent Evaluator reasons over the text you hand it, but a connectors menu in its model bar lets it do more: reach the web, call your tools, read your knowledge bases, pull in extra context, and shape how it writes up its verdict. This guide walks every option in that menu, and how what you turn on gets saved.

Note

The connectors menu appears only on a single Agent Evaluator. It isn’t available on a composite eval, or on an LLM-as-Judge or a Code Eval, which score what they’re given.

Open the connectors menu

Open an Agent Evaluator, like the hateful_speech custom eval from Create a custom eval. In the model bar under the instructions, next to the eval type and model, click the + button to open the connectors menu.

The hateful_speech Agent eval detail page with the + button in the model bar, next to the Agent type and the Turing Large model

The + button sits in the model bar, under the instructions

The connectors menu lists five capabilities. Each one you turn on shows up as a chip in the model bar, so you can see at a glance what the evaluator has.

Use Internet

A toggle that lets the evaluator search the web while it judges, for verdicts that depend on public facts the response doesn’t carry. Switch Use Internet on, and an Internet chip appears in the model bar.

The connectors menu open showing Use Internet, Connectors, Knowledge Base, Data Injection, and Summary, with the Use Internet toggle on

The five capabilities in the connectors menu

Connectors

Connectors let the evaluator call your own tools mid-judgment, the same way it uses web search, so it can check a claim against your database, confirm an ID exists, or verify a rule in an internal service. A connector is a tool you expose over the Model Context Protocol (MCP) and register once on the MCP Connectors page; this section assumes you have one registered.

%%{init: {"flowchart": {"curve": "linear"}}}%%
flowchart LR
  R["Response to judge"] --> AE["Agent Evaluator"]
  AE -->|"needs an outside fact"| T["Tool<br/>web search or your connector"]
  T -->|"result"| AE
  AE --> V["Verdict + reason"]
  style T fill:#2f2f2f,stroke:#ffffff,stroke-width:2px

In the connectors menu, open Connectors and pick the ones this eval should call. Registered connectors belong to your whole organization and are shared across evals (and with Falcon AI). If yours isn’t listed yet, add it on the MCP Connectors page, or open Manage connectors to jump straight there.

The Connectors submenu showing a Tavily connector and a Manage connectors option

Pick a registered connector, or open Manage connectors to add one

Knowledge Base

Attach one or more knowledge bases and the evaluator can look up reference context while it judges, like your policies, SOPs, or style guides. In the connectors menu, choose Knowledge Base and select from the searchable list, or Create in Knowledge Base if you don’t have one yet.

The Knowledge Base submenu with a search field, a No knowledge bases yet message, and a Create in Knowledge Base button

Attach a knowledge base, or create one if you have none yet

Note

A knowledge base here is reference context for the evaluator, not your agent’s retrieval store. It gives the judge something to check against; it doesn’t serve documents to your application at runtime.

Data Injection

By default an eval sees only the {{variables}} you mapped to your data when you set the eval up. Data Injection widens that, so the evaluator also reads surrounding context. In the connectors menu, choose Data Injection and toggle the context to include:

OptionWhat the evaluator also receives
Template variablesOnly the mapped {{variables}} (the default)
Dataset row contextAll columns from the current dataset row
Call contextThe call transcript, recording, and scenario
Full span contextThe complete span, including its metadata
Trace contextThe full trace tree, every span in the request
Session contextThe full conversation history
The Data Injection submenu with Template variables on and toggles for Dataset row, Call, Full span, Trace, and Session context

Pick the context the evaluator receives beyond the mapped variables

For example, when you attach an eval as a task on your traces, its Data Injection level is the level it runs on: trace, span, or session. The screenshot below shows a different eval, the built-in toxicity one, with Trace context turned on so it scores across the whole trace instead of a single span, which is what you need when an eval’s variables live on different spans. See the errors table for the “not in row” case it solves.

The built-in toxicity eval in a task on traces, with Data Injection set to Trace context and the trace's variable mapping shown on the right

In a task, the context level you pick is the level the eval runs on

Summary

Summary controls how the evaluator writes up its result, from a raw verdict to a full explanation. In the connectors menu, choose Summary and pick a preset:

  • None: return the raw evaluation output with no summary
  • Short: a brief summary with the key points only
  • Long: a detailed summary with full context and explanations
  • Concise: a compact summary focusing on essential insights
The Summary submenu with None, Short, Long, and Concise presets, Concise selected, and a Create custom template option

Pick how detailed the write-up should be

For anything the presets don’t cover, choose Create custom template, name it, and write your own summary criteria to reuse later.

The new custom summary template form with a Template name field and a Summary criteria field

Write and save your own summary criteria as a reusable template

Save what you turned on

Everything you enable in the connectors menu is part of the eval’s configuration, shown as chips in the model bar. Whether those choices are saved depends on the template.

The model bar with an Internet chip enabled, and the Save Version button highlighted at the bottom right

Enabled capabilities show as chips; Save Version records them

On a custom eval

A custom eval you own can carry these choices in the template itself. Click Save Version to record them into a new version.

The hateful_speech eval now at V2, with the Versions tab showing V2 as Default above V1, and a Version V2 saved toast

Saving records your choices into a new version, here V2

The latest version becomes the default, and when you attach the eval as a task you can pick which version runs.

The hateful_speech V2 eval attached as a task, with a version dropdown offering Default version, V2, and V1

The latest version runs by default; switch to an earlier one from the version dropdown

On a built-in eval

A built-in eval is read-only, so you can’t save a version. Set these capabilities when you configure a run, like attaching it as a task on your traces, and they apply to that run only, never saved back into the template. That’s what the built-in toxicity eval did earlier: its Trace context was set for that one task, not stored on the template.

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