> ## Documentation Index
> Fetch the complete documentation index at: https://docs.thecontext.company/llms.txt
> Use this file to discover all available pages before exploring further.

# Insight search

> Ask anything about your AI agent's performance in natural language and get instant, data-backed answers.

<Note>
  Insight search requires a **Pro** or **Enterprise** plan. You can upgrade from [Settings](https://thecontext.company/prod/settings).
</Note>

Insight search lets you query your entire agent dataset using natural language. Ask about errors, costs, user behavior, performance, trends - anything. No SQL, no filters, no dashboards to navigate. Just ask and get answers backed by real data, with links to the specific runs behind every finding.

You can be as broad or as specific as you want.

**Available through:**

* The [dashboard](https://www.thecontext.company/prod/search) search bar
* The [Slack](/features/slack) bot (`@The Context Company`)
* [MCP](/access-data/mcp) and [API](/access-data/api) integrations

## Example queries

### General

**Frustration analysis**

> Find the 3 biggest reasons users were frustrated with the agent in the past week and tell me potential reasons for why they're happening.

**Cost trends**

> How have my agent costs changed over the past month? Which models and tools are driving the most spend?

**Weekly health check**

> Give me a summary of how my agent performed this week compared to last week. What got better, what got worse?

**Topic breakdown**

> What are users asking about most? Which topics have the highest failure rate and which have the most positive feedback?

**User satisfaction**

> Are users generally happy with the agent? Show me the ratio of positive to negative feedback and what the negative feedback is about.

### Specific

**Tool ordering issues**

> Find runs where the search tool was called after the summarize tool for users on the enterprise plan where the final response didn't answer the user's question.

**Multi-turn failures**

> Show me sessions with more than 5 back-and-forth messages where the user eventually gave up. What was the agent getting stuck on?

**Cost outliers by metadata**

> Find the most expensive runs from the past two weeks where the customer\_tier metadata is set to "free". What models were used and how many tool calls did they make?

**Tool error chains**

> Find runs where a tool call failed immediately after another tool succeeded. Which tool pairs fail together most often?

**Regression detection**

> Compare error rates for the generate\_report tool this week vs last week. Did a specific model start failing more?

**Slow paths**

> Find runs that took over 30 seconds where the user had to wait for more than 3 sequential tool calls. What tools were in the chain?

## Browsing results

Every answer includes a table of relevant runs or sessions. Click any row to open the full trace - the complete prompt, the agent's response, all tool calls and their results, token usage, costs, and any errors.

## Chat history

Your searches are saved locally so you can return to previous investigations. Start a new chat anytime or continue where you left off.