Braintrust MCP

The Braintrust MCP server is a hosted Model Context Protocol (MCP) server that lets AI tools read and write your Braintrust data directly. Query production logs, author prompts and scorers, configure monitoring, and run evals from Claude Code, Cowork, Cursor, Codex, VS Code, and any other MCP-compatible client.

MCP or CLI?

Which one you want depends on what your tool can access and where the work needs to run.

MCP: Best when your AI tool can connect to Braintrust but has no authenticated shell, which is common in chat applications. It also fits when you want an assistant to reason over your Braintrust data and take several connected actions in one conversation, without installing and maintaining a CLI in its execution environment.
bt CLI: Best for repeatable work in scripts, CI, local files, and shell pipelines, where you want deterministic commands instead of an assistant’s judgment. Coding agents with shell access can call those commands too.

If your tool supports both, either one works. Pick whichever is more reliable for the task at hand.

This page covers MCP clients, such as Claude Code and Cursor, connecting to the Braintrust MCP server. It does not cover Braintrust connecting to your MCP server or a third-party MCP server. To give prompts access to an external MCP server, see Add external MCP servers. To let Loop use one, see Connect external MCP servers.

Connect your client

Braintrust hosts the MCP server, so there is nothing to install or deploy.

Choose authentication

OAuth (recommended)
API key

Use OAuth when your client supports it. Your client opens a Braintrust authorization page where you sign in and approve access. The server acts with the permissions of the account you sign in with.

No Braintrust API key required
No OAuth client ID or client secret to configure

The server implements OAuth 2.0 with dynamic client registration and publishes its metadata at /.well-known/oauth-authorization-server on the same host as your MCP endpoint (see next step), so a client can register itself.

Use an API key for clients without OAuth support or for programmatic connections. The server acts with the permissions of the account the key belongs to.

Create a key in Settings > API keys.
Configure your client to send this header on every request, replacing YOUR_BRAINTRUST_API_KEY with your key:
Authorization: Bearer YOUR_BRAINTRUST_API_KEY

Choose your endpoint

Use the endpoint for your organization’s data plane region:

Region	MCP endpoint
US	`https://api.braintrust.dev/mcp`
EU	`https://api-eu.braintrust.dev/mcp`

Other connection details:

Self-hosted: Copy the MCP URL from Settings > Data plane. This is your Braintrust API URL with /mcp appended.
Transport: If your client asks you to choose a transport, choose HTTP (also called streamable HTTP). SSE-only clients can’t connect.

Configure your client

Follow your client’s documentation to add a remote MCP server using the endpoint and authentication method above. The client documentation covers configuration, authentication controls, and any plan or administrator requirements.

Each client names the HTTP transport setting differently. For example, in Claude Code, add --transport http to claude mcp add. In Gemini CLI’s settings.json, set the server address with httpUrl, not url.

Client	Setup
Amp	Amp guide
Claude Code	Claude Code guide · Braintrust guide If you installed the retired Braintrust MCP plugin, remove it before you add the server.
Claude (chat and Cowork)	Claude guide · Braintrust guide
Codex	Codex guide · Braintrust guide If you installed the retired Braintrust MCP plugin, remove it before you add the server.
Cursor	Cursor guide · Braintrust guide
Devin Desktop	Devin guide · Cascade guide (legacy agent) · Braintrust guide
Gemini CLI	Gemini CLI guide
Google Antigravity	Antigravity guide · Braintrust guide
Grok	Grok guide · Braintrust guide
OpenCode	OpenCode guide · Braintrust guide
pi	pi guide · Braintrust guide
VS Code	VS Code guide · VS Code configuration reference · Braintrust guide
Warp	Warp guide
Zed	Zed guide

For other clients, check that the client supports remote MCP servers over streamable HTTP with OAuth or bearer-token authentication.

Verify the connection

Ask your assistant:

Use the Braintrust MCP server to list my recent projects.

The assistant calls a Braintrust tool and returns projects you can access. If the call fails, see Troubleshooting.

What the MCP can do

Your assistant works with the data and objects in your Braintrust organization, and it chains several tools in one turn. Suppose a support chatbot starts making claims about your product that aren’t true. In a single conversation, your assistant can:

Query recent logs to find examples of the behavior.
Write an evaluator that detects the unsupported claims, and test it against those traces before saving it.
Add the failing cases to a regression dataset, with the corrected responses as the expected output.
Run an eval comparing the current prompt against a proposed fix.

Each step produces a real Braintrust object, and your assistant returns a permalink so you can inspect or share it. Each capability below lists the tools behind it, and Tools describes every tool in one place.

Write tools act on your Braintrust organization using the permissions of your authenticated account. Configure your MCP client to require confirmation before it runs a write tool.

Install the Braintrust SDK

Before you can query anything, your application has to send traces to Braintrust. Your assistant handles that setup: it detects your programming language and frameworks, installs the appropriate SDK, and configures auto-instrumentation. Once complete, it runs your app, verifies traces are being logged, and provides a permalink to view them in Braintrust. Example prompts:

Set up tracing from scratch

Install the Braintrust SDK and add tracing to my app.

Instrument an existing integration

My app calls the OpenAI SDK. Add Braintrust tracing so those calls show up as spans.

Confirm traces are landing

Run my app and confirm traces are reaching Braintrust. Send me a permalink to one.

See Trace LLM calls for what auto-instrumentation covers. Resource: docs://sdk-install.

Explore your data

Your assistant can answer questions about what your application actually did. It queries logs, experiments, and datasets with SQL, discovers the fields and value distributions in a data source before writing a query, and pulls aggregated metrics for an experiment with or without a baseline to compare against. Because it reads the same data the UI shows, you can investigate a production issue without switching to a browser. When a query returns more than 1 MB, sql_query returns a signed URL to the full result instead of inline rows, so your assistant can work with production-scale results without filling its context window. See Tools for how to control that behavior. Example prompts:

Find recent errors

Show me the last 10 logged requests with errors, and include the error message and the model used.

See what fields your data has

What fields are available in my production logs, and which ones are populated most often?

Break down cost

What did my chatbot experiments cost last week, broken down by model?

Compare an experiment to its baseline

Summarize my latest experiment and compare it to the baseline. Call out any score that regressed.

See SQL for query syntax, and View logs for the equivalent in the UI. Tools: sql_query, infer_schema, summarize_experiment. Most Braintrust tools take an object ID, so your assistant finds the right project, experiment, or dataset by name and translates between names and IDs on its own. It usually does this as a step inside a larger request rather than as something you ask for. When you want to hand a result to someone else, it produces a direct link to the object. It can also reverse the lookup, finding a person’s traces from their name or email. Example prompts:

List recent work in a project

What experiments have I run recently in the 'chatbot' project?

Identify a Braintrust URL

What does this Braintrust URL point to, and what does the object contain?

Attribute traces to a teammate

Who logged the traces in this project this week, and which API keys produced the rest?

Tools: list_recent_objects, resolve_object, generate_permalink, lookup_users, lookup_api_keys.

Analyze traces

A long agent trace is hard to follow span by span. Your assistant partitions a single trace into chronological work sections and annotates each one, so you can see what a run actually did and where it spent its time. Example prompts:

Break down a trace

Break down what this trace actually did, step by step, and annotate each step.

Find the slow steps

Which steps in this trace took the longest?

Tools: get_trace_work_items, update_trace_work_report.

Manage patterns

Patterns record the recurring behaviors your traces reveal, so an investigation doesn’t have to start over each time. Your assistant checks what has already been reported, catching duplicates that use different wording, adds a new pattern with supporting evidence, and attaches further evidence as it turns up. Example prompts:

Investigate a suspected problem

I think we might be overspending on web searches. Dig into that, and report a pattern if you find one.

Review active patterns

What patterns are active that I can solve right now?

Manage duplicate patterns

You can also manage duplicate patterns, which requires data plane v2.18.0 or later on self-hosted deployments:

Find duplicates: search_patterns accepts status: "duplicate" and returns duplicate_of, the canonical pattern’s ID.
Close a duplicate: Call update_pattern with status: "duplicate" and duplicate_of set to an existing canonical pattern in the same project. The target cannot be the pattern itself or another duplicate.
Undo duplicate closure: Call update_pattern with duplicate_of: null and status: "active" or status: "inactive". Both fields are required to undo the closure.

Closing a pattern as a duplicate removes it from the Open list and hides its Reopen button. Its history is preserved, and it links to the canonical pattern.

Tools: search_patterns, new_pattern, update_pattern.

Configure Topics

Topics preprocesses traces into text, extracts facets from that text, and clusters the results to show what your users actually do. Your assistant builds that pipeline for you: it writes a preprocessor that matches your trace shape, tests it against real traces before saving, defines the facets to extract, and enables the automation that keeps it running. It can also rewind an automation over historical traffic.

Rewinding a Topics automation processes historical traces and draws from your monthly model credits.

Example prompts:

Set up Topics from scratch

Set up Topics for my project. Validate the preprocessor and facets on real traces before you enable anything.

Write a preprocessor for an unusual trace shape

My traces don't store conversation text on LLM spans. Write a preprocessor that works with my trace shape, and test it on a few traces.

Process historical traces

Rewind my Topics automation so it covers the last 30 days.

Tools: create_preprocessor, test_preprocessor_on_trace, create_facet, test_facet_on_trace, enable_topics_automation, set_topics_automation, rewind_topics_automation.

Build dashboards

Dashboards collect the charts you check regularly. Your assistant previews a chart against real project logs so you can see it before anything is saved, then puts it into a new or existing dashboard. It can also read back what a dashboard already contains and edit charts in place, one at a time or in bulk. Example prompts:

Preview a chart before saving it

Chart daily cost for this project over the last two weeks. Show it to me before you save anything.

Create a cost dashboard

Create a dashboard for daily cost analysis, with charts for total cost, cost per trace, and cost by model.

Add a chart to an existing view

Add a p95 latency chart to my error monitoring dashboard.

See Dashboards for the equivalent in the UI. Tools: generate_monitor_chart, list_monitoring_views, get_monitoring_view, create_monitoring_view, update_monitoring_view.

Manage automations and alerts

Automations watch your data so you don’t have to. Your assistant inspects what a project already runs, including online scoring rules, exports, and retention policies, then creates what’s missing: an alert on individual matching logs, an alert on an aggregate threshold over a recent window, an alert on environment updates, or a Loop job that analyzes recent traffic on a schedule. It can also pause and resume any of them. Example prompts:

Audit what's configured

What automations are configured in this project, and which ones are paused?

Alert on an error rate

Alert me when the error rate goes above 2% over the last hour.

Schedule recurring analysis

Every Monday morning, analyze last week's traces and summarize the top failure modes.

Pause an automation

Pause the online scoring rule you just created.

See Alerts for delivery channels and tuning, and Loop automations for scheduled analysis. Tools: list_automations, set_automation_status, create_log_alert, create_environment_update_alert, create_threshold_alert, create_scheduled_loop_job, list_slack_channels.

Author prompts and evaluators

Prompts and evaluators are versioned objects that your application, experiments, and online scoring all share. Your assistant drafts one from what it found in your logs, runs it against real traces to see how it behaves before anything is saved, and saves it as a new version when you’re satisfied. It can then attach an evaluator to an online scoring rule so it scores production logs continuously. Example prompts:

Write a scorer for errors you found

Write a scorer that detects the errors in these logs, then test it on a few traces before saving it.

Create an LLM-as-a-judge

Create an LLM-as-a-judge scorer for helpfulness and test it on ten recent traces.

Apply a scorer to production logs

Set up online scoring with the scorer you just created. Leave it paused so I can review the configuration first.

Save a prompt as a new version

Save this prompt to my project as a new version of the summarizer prompt.

See Create prompts and Write scorers for details. Tools: create_prompt, create_evaluator, test_evaluator, update_online_scoring_rule.

Run evals and edit datasets

Evals, and the datasets that feed them, are how you measure whether a change helps. Your assistant curates dataset rows from the failures it finds in your logs, then runs an experiment against them using a saved or inline task and whichever scorers you want. A prior experiment can supply the input data, in which case its outputs become the expected values. Use add_to_dataset to copy project log rows into a dataset in the same project without loading their full contents into the assistant’s context. Find rows with sql_query against project_logs, then pass their id values in log_row_ids, along with project_id and either dataset_id or dataset_name. Use row IDs, not span_id or root_span_id values. Set create_if_missing to create a new named dataset.

Copied fields. Each row preserves input, metadata, and tags, with an origin link to the source log. Its expected comes from the log’s expected, falling back to output when expected is missing or null.
Missing rows. If any requested row ID is missing, the tool writes no rows.
Batch limits. Both add_to_dataset and edit_dataset_rows accept up to 1,000 rows per call on hosted Braintrust. Self-hosted limits depend on deployment configuration, with a fallback of 100. Check the tool’s description for its limit and split larger batches across calls.

Use save_dataset_custom_view to give reviewers a custom view for labeling a dataset’s rows. The tool saves the view’s React code on the dataset and returns a link that opens the dataset with the view selected. To reuse a view as a template, call list_dataset_custom_views to read its code, adjust it, and save it on another dataset under a new slug. Set if_exists to replace to save a new version of an existing view. Example prompts:

Build a regression dataset from failures

Add the traces that failed the helpfulness scorer to my regression dataset, and set the expected output from the corrected responses.

Compare two prompts

Run an eval comparing these two prompts on my regression dataset, and tell me which one wins.

Re-run last week's inputs

Run my task against the inputs from last week's experiment and compare the results.

See Run evaluations and Datasets for details. Tools: run_eval, edit_dataset_rows, add_to_dataset, list_dataset_custom_views, save_dataset_custom_view.

run_eval creates an experiment and can execute your code or call AI providers, so it incurs compute and model usage.

edit_dataset_rows can permanently delete dataset rows. Review the operations your assistant proposes before approving them.

Manage project settings

Project settings hold the defaults that other functions inherit. Your assistant reads a project’s typed settings, including which preprocessor facets and other project functions fall back to, and changes that default when you want a new one to apply everywhere. Example prompts:

Check the default preprocessor

What preprocessor is my project using by default?

Change the default

Make the preprocessor you just created the project default.

See Projects for the equivalent in the UI. Tools: get_project_settings, set_project_default_preprocessor.

Search docs and load skills

Your assistant grounds its answers in Braintrust documentation rather than guesswork, so it can explain a concept or find the right guide without leaving your editor. For multi-step work, it loads a skill first: a workflow guide that tells it the order to do things in and what to validate at each stage. Example prompts:

Ask a product question

How do I create a custom scorer in Braintrust?

Clarify a concept

What's the difference between experiments and project logs?

Follow a guided workflow

Walk me through building an evaluator and applying it to my logs.

See Skills for guidance on loading the right skill for a task. Tools: search_docs, load_braintrust_skill.

Reference

Tools

Every tool the server exposes, in the order of the capabilities above.

Tool	Description
`sql_query`	Query experiments, datasets, and logs using SQL. Supports `SELECT`, `FROM`, `WHERE`, `GROUP BY`, `ORDER BY`, and `LIMIT`.
`infer_schema`	Discover the available fields, data types, and most common values in experiments, datasets, or logs. The `limit` parameter defaults to 1,000 fields and cannot exceed 1,000. Use `fields` to narrow the result to specific top-level fields such as `metadata`.
`summarize_experiment`	Get aggregated performance metrics for an experiment, optionally compared to a baseline.
`list_recent_objects`	List recently created projects, experiments, datasets, prompts, or functions you have access to.
`resolve_object`	Convert names to IDs or vice versa, and parse Braintrust URLs. For pattern, prompt, and scorer URLs, returns the containing project as `object_id` and the resource as `row_id`. App URLs retain their organization scope. Supply `org_name` to disambiguate name lookups. Ambiguous names return an error.
`generate_permalink`	Generate a direct web link to a Braintrust object for sharing or bookmarking. For `project_logs`, `project_patterns`, `project_prompts`, and `project_functions`, supply `project_name` or the project UUID as `object_id`. Pass `root_span_id` as `row_id` for log entries, or the resource ID as `row_id` for patterns, prompts, and scorers. Supply `org_name` to scope a name lookup to one organization.
`lookup_users`	Resolve Braintrust organization member IDs to names and emails. Pass a batch of user IDs, search by partial name or email, or list every member.
`lookup_api_keys`	Resolve API key and service token IDs to their names. Pass a batch of IDs, match an exact name, or list every key visible to you. Results follow existing API key and service token visibility rules.
`get_trace_work_items`	Return meaningful LLM, tool, function, and task spans in chronological order for trace analysis. If no eligible child spans exist, return the root span when it contains analyzable input, output, or error.
`update_trace_work_report`	Save those work sections and per-span annotations back to the trace.
`search_patterns`	Search existing project patterns by text, semantic similarity, ID, status, or quality. Pass `similar_to` with one or more pattern IDs or unreported pattern drafts to catch duplicates that use different wording. For listings and text searches, continue while `has_more` is `true` by passing the returned `cursor` with the same project and filters. Semantic searches using `similar_to` do not support cursors.
`new_pattern`	Record a pattern the agent has identified, with the evidence behind it. The agent decides what counts as a pattern, so asking for one doesn’t guarantee a record is written.
`update_pattern`	Update an existing project pattern, attach evidence to matching traces, or manage duplicate patterns. Can also set `severity` and `category`, set or clear `quality` (`useful` or `noise`), and append a `feedback` comment attributed to the calling user or automation. Category values are ignored when a person has corrected the pattern’s categories.
`create_preprocessor`	Create a versioned preprocessor from inline JavaScript that converts raw trace data into text.
`test_preprocessor_on_trace`	Run a saved, global, or inline preprocessor on up to 50 span, trace, or group references without writing to the source trace.
`create_facet`	Create a versioned facet that extracts a short summary from spans or traces. Facet extraction always uses Braintrust’s built-in facet model.
`test_facet_on_trace`	Run an inline facet definition on up to ten span, trace, or group references without writing the result to the source trace.
`enable_topics_automation`	Enable Topics for a project. This seeds processing for new traffic and doesn’t rewind historical data.
`set_topics_automation`	Update an existing Topics automation’s facets, scope, filters, sampling, or timing.
`rewind_topics_automation`	Rewind an existing Topics automation to process historical data from a start time or a recent window.
`generate_monitor_chart`	Preview a monitor chart for project logs without modifying a saved view.
`list_monitoring_views`	List a project’s saved monitor views and chart IDs.
`get_monitoring_view`	Inspect a saved monitor view, including its options and ordered chart definitions.
`create_monitoring_view`	Create a project-scoped monitor view, optionally containing charts you already previewed.
`update_monitoring_view`	Insert, update, or remove charts in an existing monitor view, one edit at a time or several in bulk.
`list_automations`	List a project’s automations, including online scoring rules, alerts, exports, retention policies, and Topics automations. Filter by `automation_id`, `name`, or `kind`. Returns complete configurations, so you can inspect an automation before updating it.
`set_automation_status`	Pause or activate an alert, scheduled job, or online scoring rule.
`create_log_alert`	Create an alert for individual matching project logs. Use `config.interval_seconds` to throttle repeated notifications.
`create_environment_update_alert`	Create an alert for environment updates. Use `config.environment_filter` to limit notifications to specific environment slugs.
`create_threshold_alert`	Create an alert for an aggregate over a recent window of project data, evaluated on a schedule. Use it for averages, counts, rates, percentages, percentiles, and distributions.
`create_scheduled_loop_job`	Create a Loop job that runs on an interval or cron schedule over a recent window of project data.
`list_slack_channels`	List connected Slack workspaces and public channels for a project to resolve workspace and channel IDs before configuring Slack delivery.
`create_prompt`	Create a versioned prompt from a completion-style prompt or chat messages. Set `if_exists` to `replace` to save a new version, or `ignore` to leave an existing prompt unchanged.
`create_evaluator`	Create a versioned LLM or inline code evaluator. Set `output_type` to `score` for numeric scores or `classification` for categorical labels.
`create_human_review_score`	Create a categorical, slider, or free-form human review score for a project. Fails if the name already exists.
`add_to_human_review_queue`	Add up to 100 project log traces, experiment traces, or dataset rows from one source to human review by row ID. Targets project logs by default. Pass `experiment_id` or `dataset_id` to select an experiment or dataset in the same project. Optionally assign reviewers via `assignee_user_ids`. If any row is missing or already marked complete, the tool writes no rows.
`test_evaluator`	Run a saved, global, or inline evaluator against span, trace, or group references without writing results to the source trace.
`update_online_scoring_rule`	Save or rewind an online scoring rule that runs saved evaluator functions. New rules default to paused.
`run_eval`	Run an experiment with a hosted dataset, inline rows, or a prior experiment as input data, any saved or inline task, and zero or more saved or inline scorers. When a prior experiment supplies the data, its outputs become expected values unless an expected value was already recorded.
`edit_dataset_rows`	Insert, update, or delete dataset rows. Target a dataset by ID or name, and set `create_if_missing` to create a new named dataset. Inserts accept optional `tags` and `origin`. See Batch limits.
`add_to_dataset`	Copy project log rows into a dataset by row ID, preserving their data and source links. See Copy behavior and limits.
`list_dataset_custom_views`	List up to 100 of a project’s saved dataset custom views, optionally for one dataset, with each view’s code and a link that opens it.
`save_dataset_custom_view`	Save a custom view for labeling a dataset’s rows and return a link that opens the dataset with the view selected. Set `if_exists` to `replace` to save a new version of an existing view, or `ignore` to leave an existing view unchanged.
`get_project_settings`	Return a project’s typed settings, including the effective default preprocessor. An unset default resolves to the built-in `thread` preprocessor.
`set_project_default_preprocessor`	Set or clear a project’s default preprocessor. Pass `null` to restore the built-in default. This changes the default used by facets and other project functions that don’t select a preprocessor explicitly.
`search_docs`	Search Braintrust documentation to find relevant guides, API references, and code examples.
`load_braintrust_skill`	Load a Braintrust workflow guide or developer skill. The tool’s description and `skill_name` parameter list the skills available on your MCP server.

When a result exceeds 1 MB, sql_query uploads it to object storage and returns an overflow envelope instead of inline rows. The envelope includes an overflow_url (a signed URL to the JSON result), a byte_length, a row_count (when available), and an instructions field describing how to retrieve the full result. Set return_url: true to request a URL even when the result is below the threshold, which is useful when you want to download or save results without putting them in model context. Field values in the result are truncated to preview_length characters (1024 by default). Set preview_length: -1 to include untruncated field values.

Skills

Skills are workflow guides your assistant loads with load_braintrust_skill and then follows. They cover Braintrust workflows such as Topics, evaluators, patterns, and automations, as well as working with the Braintrust SDK and your application code. Loading a skill is read-only and costs one tool call. The catalog combines Braintrust’s built-in workflow guides with skills from the public Braintrust skills repository. You can also install the public skills directly into your assistant by following the repository’s installation instructions. Ask your assistant to inspect load_braintrust_skill for the available names, then load the relevant skill. Each MCP server release includes a pinned revision of the public skills alongside its built-in guides, so its contents can differ from the latest version on GitHub. Follow each skill’s prerequisites, such as access to your application repository or an authenticated Braintrust CLI. Loop uses a selected subset of this catalog. A skill available to an external MCP client is not necessarily available to Loop. Use the names advertised to the assistant making the request.

Resources

MCP resources provide contextual documentation that AI assistants can read to perform tasks more effectively.

docs://sdk-install - Step-by-step guidance for installing the Braintrust SDK into a project, setting up tracing, configuring auto-instrumentation, and running your first eval.
docs://sql - Documentation for the sql_query tool, including syntax, available fields, and examples.
docs://url-formats - Reference for Braintrust URL patterns, used by the resolve_object tool.
docs://experiments - Background on Braintrust experiments and how to create them.

docs://sdk-install has companion resources for Python, TypeScript, Go, Java, Ruby, and C#. Your assistant reads the one matching your project automatically.

Troubleshooting

Authentication fails: For OAuth, sign in again using your client’s authentication controls. If you are switching from API-key authentication, remove the manually configured Authorization header or bearer token first. For API-key authentication, verify that the key is valid and that your client sends the header as Authorization: Bearer YOUR_BRAINTRUST_API_KEY. Invalid client errors: Verify that the URL matches your organization’s MCP endpoint, including /mcp and no trailing slash. Connection timeouts: Check your internet connection. Corporate networks might need to allowlist *.braintrust.dev and, for self-hosted deployments, your MCP endpoint. MCP server not appearing: Restart your client, then check the server configuration and connection status using your client’s MCP documentation. Request body too large (HTTP 413): The MCP server accepts request bodies up to 100 MiB. A tool call whose payload exceeds that limit returns an HTTP 413 error. Split the input into smaller requests, or fetch the data incrementally. Server URL errors on a self-hosted deployment: The MCP server derives its own address from the forwarding headers your ingress sets. If it reports that it could not determine the server URL, set the MCP_SERVER_URL environment variable on your data plane to your API URL.

AI providers

SDKs

Developer tools

Runtime environments

Connect your client

What the MCP can do

Install the Braintrust SDK

Explore your data

Analyze traces

Manage patterns

Configure Topics

Build dashboards

Manage automations and alerts

Author prompts and evaluators

Run evals and edit datasets

Manage project settings

Search docs and load skills

Reference

Tools

Skills

Resources

Troubleshooting

​Connect your client

​What the MCP can do

​ Install the Braintrust SDK

​ Explore your data

​ Find and share objects

​ Analyze traces

​ Manage patterns

​ Configure Topics

​ Build dashboards

​ Manage automations and alerts

​ Author prompts and evaluators

​ Run evals and edit datasets

​ Manage project settings

​ Search docs and load skills

​Reference

​Tools

​Skills

​Resources

​Troubleshooting

Connect your client

What the MCP can do

Install the Braintrust SDK

Explore your data

Find and share objects

Analyze traces

Manage patterns

Configure Topics

Build dashboards

Manage automations and alerts

Author prompts and evaluators

Run evals and edit datasets

Manage project settings

Search docs and load skills

Reference

Tools

Skills

Resources

Troubleshooting