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mlflow-mcp-server

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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
MLFLOW_TOOLSNoComma-separated allowlist of tool categories
MCP_HTTP_HOSTNoHTTP bind host127.0.0.1
MCP_HTTP_PORTNoHTTP listen port3000
MCP_TRANSPORTNoTransport mode: 'stdio' or 'http'stdio
MCP_HTTP_TOKENNoBearer token for HTTP transport. Required when MCP_TRANSPORT=http.
MLFLOW_DISABLENoComma-separated denylist of tool categories
MCP_HTTP_SKIP_AUTHNoSkip Bearer auth for HTTP transportfalse
MLFLOW_ALLOW_WRITENoSet 'true' to enable write operationsfalse
MLFLOW_TRACKING_URIYesMLflow tracking URL (e.g., http://localhost:5000, Databricks workspace URL)
MLFLOW_EXPERIMENT_IDNoDefault experiment ID for tools that accept it implicitly
MLFLOW_TRACKING_TOKENNoBearer token for authentication (e.g., Databricks PAT)
MLFLOW_TRACKING_PASSWORDNoBasic-auth password (required if username is provided)
MLFLOW_TRACKING_USERNAMENoBasic-auth username (alternative to token)

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": true
}
resources
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
create-experimentC

Create a new MLflow experiment

search-experimentsC

Search experiments with filter and pagination

get-experimentA

Get experiment details by ID

get-experiment-by-nameC

Get experiment details by name

update-experimentC

Rename an experiment

delete-experimentC

Soft-delete an experiment by ID

restore-experimentB

Restore a deleted experiment

set-experiment-tagB

Set a tag on an experiment

delete-experiment-tagB

Delete a tag from an experiment

create-runC

Create a new run in an experiment

get-runB

Get run details by ID

search-runsC

Search runs with filter expression and pagination

update-runC

Update run status, end time, or name

delete-runA

Soft-delete a run by ID

restore-runB

Restore a deleted run

log-metricB

Log a single metric value to a run

log-paramB

Log a single parameter to a run

log-batchC

Log a batch of metrics, params, and tags to a run

log-inputsC

Log dataset inputs to a run

get-metric-historyC

Get full history of a metric for a run

set-run-tagC

Set a tag on a run

delete-run-tagC

Delete a tag from a run

list-artifactsC

List artifacts under a run's artifact directory

get-best-runB

Find the run with the best (max/min) value of a metric in an experiment

compare-runsA

Side-by-side metric/param comparison across multiple runs. Renders an Apps SDK card on ChatGPT clients (Claude clients receive the same JSON text).

search-runs-by-tagsB

Find runs whose tags match all of the given key/value pairs

create-registered-modelB

Create a new registered model in the model registry

get-registered-modelB

Get a registered model by name

search-registered-modelsD

Search registered models

rename-registered-modelA

Rename a registered model

update-registered-modelC

Update a registered model's description

delete-registered-modelA

Delete a registered model and all its versions

get-latest-model-versionsC

Get the latest model versions per stage

set-registered-model-tagC

Set a tag on a registered model

delete-registered-model-tagC

Delete a tag from a registered model

set-registered-model-aliasC

Set an alias on a registered model version

delete-registered-model-aliasB

Delete an alias from a registered model

get-model-version-by-aliasC

Get the model version pointed to by an alias

create-model-versionC

Create a new model version

get-model-versionC

Get a model version by name and version

search-model-versionsC

Search model versions with filter and pagination

update-model-versionC

Update a model version's description

delete-model-versionC

Delete a specific model version

transition-model-version-stageC

Transition a model version to a new stage

get-model-version-download-uriC

Get the artifact download URI for a model version

set-model-version-tagC

Set a tag on a model version

delete-model-version-tagB

Delete a tag from a model version

create-logged-modelB

Create a new MLflow 3 LoggedModel entity in an experiment

search-logged-modelsC

Search LoggedModels by experiment with filter and pagination

get-logged-modelB

Get a LoggedModel by ID

finalize-logged-modelB

Set a terminal status (READY/FAILED/...) on a LoggedModel

delete-logged-modelC

Soft-delete a LoggedModel by ID

set-logged-model-tagsB

Set or upsert tags on a LoggedModel

delete-logged-model-tagB

Delete a tag from a LoggedModel

log-logged-model-paramsC

Log parameters on a LoggedModel

search-tracesC

Search and filter traces in experiments

get-traceB

Retrieve detailed trace information by trace ID

get-trace-infoA

Retrieve trace metadata only (no spans)

delete-tracesB

Delete traces by ID or older than a timestamp

set-trace-tagB

Add a custom key-value tag to a trace

delete-trace-tagC

Remove a tag from a trace

list-trace-attachmentsA

List attachments on a trace (MLflow 3.9+)

get-trace-attachmentB

Get a specific attachment on a trace by ID (MLflow 3.9+)

log-feedbackC

Log evaluation feedback (score or judgment) on a trace

log-expectationB

Log a ground-truth expectation on a trace

get-assessmentB

Get an assessment by trace ID and assessment ID

update-assessmentD

Update an existing assessment

delete-assessmentB

Delete an assessment from a trace

create-webhookB

Register a webhook for model registry events

list-webhooksA

List webhooks (optionally filtered by model name)

get-webhookB

Get webhook details by ID

update-webhookC

Update an existing webhook (events, URL, status, secret)

delete-webhookC

Delete a webhook by ID

test-webhookB

Send a test event to a webhook to verify configuration

create-prompt-optimization-jobB

Create a prompt optimization job to automatically improve a registered prompt

get-prompt-optimization-jobB

Get a prompt optimization job by ID

search-prompt-optimization-jobsC

Search prompt optimization jobs in an experiment

cancel-prompt-optimization-jobA

Cancel a running prompt optimization job

delete-prompt-optimization-jobC

Delete a prompt optimization job

summarize-runA

Aggregated run view: run info + (optional) metric history + (optional) artifacts list in a single call. Replaces 3-4 round-trips of get-run + get-metric-history (per metric) + list-artifacts.

summarize-experimentA

Aggregated experiment view: experiment overview + topN runs (sorted by metric or start_time) + metric stats (min/max/mean across topN) in a single call. Replaces 3-5 round-trips of get-experiment + search-runs + get-best-run.

search-toolsA

Discover available tools by natural language query. Returns matching tool names + descriptions across all categories. Use this first to navigate the 77+ tool surface efficiently.

Prompts

Interactive templates invoked by user choice

NameDescription
debug-failed-tracesFind failed traces in an experiment, summarize the failure modes, and suggest next steps.
promote-best-runFind the best run in an experiment by metric, register the model, and set a 'champion' alias.
compare-top-runsPick the top N runs by metric in an experiment and produce a side-by-side comparison.
analyze-failed-tracesHigher-level analysis than debug-failed-traces: failure rate trend vs prior window, top patterns by impact, where to focus. Use this when you want 'is it getting worse?' not 'what broke this one?'
annotate-trace-qualityWalk through recent traces and log structured feedback (helpfulness/correctness) on each.

Resources

Contextual data attached and managed by the client

NameDescription
compare-runs-cardApps SDK UI template rendered with compare-runs tool output

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