Connectors · AI
Google Vertex AI MCP server for AI agents.
actionlake is an MCP gateway: one remote MCP server that connects Claude and other MCP clients to Google Vertex AI and the rest of your systems. It gives your agent 1,128 Google Vertex AI actions (565 reads, 242 writes and 321 that wait for a confirmation) and holds the Google Vertex AI credential itself, so the agent never does. It paces Google Vertex AI's limits and records every call. Connecting Google Vertex AI needs your own Google Vertex AI OAuth app and one admin sign-in.
100,000 free calls a month. No card, no email, no password: press Start a free sandbox on the next screen and keep the key it shows you.
claude mcp add --transport http actionlake https://connect.actionlake.com/mcpdo { action: "gcp_vertex_ai.express_batch_prediction_jobs.list", intent: "check which Google Vertex AI account is connected"}# Express batch prediction jobs: list · read · 1 upstream call- 1,128actionsin one signed file
- 565readlook, never change
- 242writecreate and update
- 321wait for a yesdestructive, confirmed first
Set up
Connect Google Vertex AI in three steps.
- 01
Add actionlake to Claude.
Run the command once in Claude Code; its consent page can create a free sandbox: 100,000 calls a month, no card. In the Claude app, add a custom connector with the URL below and paste your key on the consent page. Creating a new sandbox from the Claude app is not yet tested by us.
https://connect.actionlake.com/mcp - 02
Connect Google Vertex AI.
OAuth 2.0, authorization code with PKCE. You register your own OAuth app with Google Vertex AI, an admin signs in once, and you paste the app's client id and secret and the tokens from that sign-in; actionlake renews the access token from then on. actionlake stores what you paste write-only, sealed under your tenant's key.
Before the first sign-in, Google Vertex AI needs an OAuth app of your own, registered in its developer portal. actionlake has no Google Vertex AI app to lend you.
access_tokenAccess token from one sign-in with your own OAuth apprefresh_tokenRefresh token from the same sign-in; the access token is renewed from itclient_idGoogle OAuth client id (Google Cloud console > APIs & Services > Credentials; enable the Vertex AI API on the project)client_secretGoogle OAuth client secrethostWhere your Google Vertex AI lives: your host
account { op: "connections.requirements", service: "gcp_vertex_ai" } # free: exactly what to paste, and what is found for you - 03
Make the first call. Check before a write.
Ask in plain words; Claude picks the action. Before anything changes,
checkvalidates the call and prices it, for free.do { action: "gcp_vertex_ai.express_batch_prediction_jobs.list" }# express batch prediction jobs: list · read · 1 upstream callcheck { action: "gcp_vertex_ai.endpoints.predict", input: { … } }# free: inputs, permissions, cost and confirmations, nothing sent
Google Vertex AI at a glance
- Sign-in
- OAuth 2.0, authorization code with PKCE. You register your own OAuth app with Google Vertex AI, an admin signs in once, and you paste the app's client id and secret and the tokens from that sign-in; actionlake renews the access token from then on.
- You provide
access_tokenrefresh_tokenclient_idclient_secret- Setup fields
host- Limits we pace to
- 600 calls a minute, per connection
- API covered
- Google Vertex AI (Gemini generation, model endpoints, model registry, datasets, training, pipelines, RAG, Agent Engine) API aiplatform v1 (Discovery revision 20261012)
aiplatform.googleapis.com - Built from
- Google Vertex AI's documentation, read 22 October 2026aiplatform.googleapis.comcloud.google.com
- Service file
- Version 1.0.0, signed (Ed25519)
- Cached reads
- 217 of 565 reads, for up to 5 minutes; a write clears what it affects
- Shared entities
- 2, so ask can line Google Vertex AI up with your other systems
- Data
- Processed and stored in Sydney, Australia. No US region yet
Capabilities
What an AI agent can do in Google Vertex AI.
1,128 actions in 218 areas. Every one carries its effect, so a key can allow the reads and hold the rest.
- 565readlook, never change
- 242writecreate and update
- 321destructivewait for a confirmation
Endpoints
215 read · 14 write · 2 destructive
Endpoints: generate content · Endpoints: predict · Endpoints: raw predict · Endpoints: compute tokens
Locations
185 read · 13 write · 0 destructive
Locations: list · Locations: get · Locations: ask contexts · Locations: async retrieve contexts
Models
156 read · 7 write · 2 destructive
Models: copy · Models: export · Models: list · Models: list checkpoints
Featurestores entity types
146 read · 6 write · 2 destructive
Featurestores entity types: create · Featurestores entity types: export feature values · Featurestores entity types: import feature values · Featurestores entity types: list
Feature online stores feature views
136 read · 6 write · 1 destructive
Feature online stores feature views: create · Feature online stores feature views: direct write · Feature online stores feature views: fetch feature values · Feature online stores feature views: generate fetch access token
Reasoning engines
124 read · 6 write · 2 destructive
Reasoning engines: async query · Reasoning engines: create · Reasoning engines: execute code · Reasoning engines: list
Datasets
115 read · 5 write · 1 destructive
Datasets: list · Datasets: create · Datasets: export · Datasets: import
Featurestores
105 read · 4 write · 1 destructive
Featurestores: batch read feature values · Featurestores: create · Featurestores: list · Featurestores: search features
112 more areas · 1,014 actions
- Index endpoints 10
- Metadata stores contexts 10
- Publishers models 10
- Reasoning engines memories 10
- Studies trials 10
- Express reasoning engines 9
- Express reasoning engines sandbox environments 9
- Memory banks memories 9
- Reasoning engines sandbox environments 9
- Agents 8
- Express publishers models 8
- Feature groups 8
- Feature online stores 8
- Metadata stores executions 8
- Model deployment monitoring jobs 8
- Notebook runtime templates 8
- Tensorboards 8
- Tensorboards experiments runs time series 8
- Express endpoints 7
- Indexes 7
- Metadata stores artifacts 7
- Notebook runtimes 7
- Online evaluators 7
- Pipeline jobs 7
- Reasoning engines sessions 7
- Schedules 7
- Tensorboards experiments 7
- Tensorboards experiments runs 7
- Datasets dataset versions 6
- Deployment resource pools 6
- Evaluation sets 6
- Express datasets dataset versions 6
- Feature groups features 6
- Featurestores entity types features 6
- Persistent resources 6
- Batch prediction jobs 5
- Cached contents 5
- Custom jobs 5
- Custom jobs operations 5
- Data labeling jobs 5
- Data labeling jobs operations 5
- Datasets annotation specs operations 5
- Datasets data items annotations operations 5
- Datasets data items operations 5
- Datasets operations 5
- Datasets saved queries operations 5
- Deployment resource pools operations 5
- Endpoints operations 5
- Evaluation runs 5
- Express custom jobs operations 5
- Express data labeling jobs operations 5
- Express datasets 5
- Express datasets annotation specs operations 5
- Express datasets data items annotations operations 5
- Express datasets data items operations 5
- Express datasets operations 5
- Express datasets saved queries operations 5
- Express deployment resource pools operations 5
- Express endpoints operations 5
- Express featurestores entity types features operations 5
- Express featurestores entity types operations 5
- Express featurestores operations 5
- Express hyperparameter tuning jobs operations 5
- Express index endpoints operations 5
- Express indexes operations 5
- Express memory banks memories operations 5
- Express memory banks operations 5
- Express metadata stores artifacts operations 5
- Express metadata stores contexts operations 5
- Express metadata stores executions operations 5
- Express metadata stores operations 5
- Express migratable resources operations 5
- Express model deployment monitoring jobs operations 5
- Express models evaluations operations 5
- Express models operations 5
- Express notebook execution jobs operations 5
- Express notebook runtime templates operations 5
- Express notebook runtimes operations 5
- Express online evaluators operations 5
- Express operations 5
- Express persistent resources operations 5
- Express pipeline jobs operations 5
- Express rag corpora operations 5
- Express rag corpora rag files operations 5
- Express rag engine config operations 5
- Express reasoning engines memories operations 5
- Express reasoning engines operations 5
- Express reasoning engines sessions operations 5
- Express schedules operations 5
- Express semantic governance policies operations 5
- Express serving profiles operations 5
- Express skills operations 5
- Express specialist pools operations 5
- Express studies operations 5
- Express studies trials operations 5
- Express task stores operations 5
- Express tensorboards experiments operations 5
- Express tensorboards experiments runs operations 5
- Express tensorboards experiments runs time series operations 5
- Express tensorboards operations 5
- Express training pipelines operations 5
- Express tuning jobs operations 5
- Featurestores entity types features operations 5
- Featurestores entity types operations 5
- Featurestores operations 5
- Hyperparameter tuning jobs 5
- Hyperparameter tuning jobs operations 5
- Index endpoints operations 5
- Indexes operations 5
- Memory banks memories operations 5
- Memory banks operations 5
- 99 smaller areas 364
Top actions
The Google Vertex AI API, shaped for agents.
The ids your agent types, what each does, and the one name it shares with other systems. 9 of Google Vertex AI's 9 action names are shared with at least one other connector, so an agent learns them once.
| Action | What it does | Effect | Same name in |
|---|---|---|---|
gcp_vertex_ai.endpoints.predictEndpoints: predict | Endpoints: predict. | write | analytics.inference.create17 connectors |
gcp_vertex_ai.endpoints.raw_predictEndpoints: raw predict | Perform an online prediction with an arbitrary HTTP payload. | write | analytics.inference.create17 connectors |
gcp_vertex_ai.batch_prediction_jobs.listBatch prediction jobs: list | Lists BatchPredictionJobs in a Location. | read | automation.background_job.list34 connectors |
gcp_vertex_ai.batch_prediction_jobs.createBatch prediction jobs: create | Creates a BatchPredictionJob. | write | automation.background_job.create29 connectors |
gcp_vertex_ai.locations.listLocations: list | Lists information about the supported locations for this service. | read | infra.region.list16 connectors |
gcp_vertex_ai.batch_prediction_jobs.cancelBatch prediction jobs: cancel | Cancels a BatchPredictionJob. | destructive | automation.background_job.cancel17 connectors |
gcp_vertex_ai.datasets.listDatasets: list | Lists Datasets in a Location. | read | this connector |
gcp_vertex_ai.custom_jobs.listCustom jobs: list | Lists CustomJobs in a Location. | read | this connector |
gcp_vertex_ai.locations.getLocations: get | Gets information about a location. | read | infra.region.get4 connectors |
gcp_vertex_ai.express_datasets.listExpress datasets: list | Lists Datasets in a Location. | read | this connector |
gcp_vertex_ai.express_datasets.createExpress datasets: create | Creates a Dataset. | write | this connector |
gcp_vertex_ai.express_endpoints.predictExpress endpoints: predict | Express endpoints: predict. | write | this connector |
Try it
Prompts to paste into Claude.
Each one names the Google Vertex AI actions it will call, so you know what it reads and what it changes before you send it.
- Readread
Express batch prediction jobs: list in Google Vertex AI and tell me what stands out.
calls
gcp_vertex_ai.express_batch_prediction_jobs.list - Writewrite
Endpoints: predict in Google Vertex AI. Show me exactly what you will send before you save it.
calls
gcp_vertex_ai.endpoints.predict
Governance for Google Vertex AI
Destructive actions wait for a yes.
321 Google Vertex AI actions are destructive. The first 10:
- Batch prediction jobs: cancel
gcp_vertex_ai.batch_prediction_jobs.cancel - Batch prediction jobs: delete
gcp_vertex_ai.batch_prediction_jobs.delete - Models: delete
gcp_vertex_ai.models.delete - Indexes: delete
gcp_vertex_ai.indexes.delete - Datasets: delete
gcp_vertex_ai.datasets.delete - Endpoints: delete
gcp_vertex_ai.endpoints.delete - Operations: delete
gcp_vertex_ai.operations.delete - Custom jobs: delete
gcp_vertex_ai.custom_jobs.delete - Rag corpora: delete
gcp_vertex_ai.rag_corpora.delete - Featurestores: delete
gcp_vertex_ai.featurestores.delete
A destructive action runs only with confirm: true. Under the human policy the agent cannot confirm for itself: the run stops until a named person approves, in the console or from a signed link in an email. The cap on a key is refused before the call is sent, never after.
Batch prediction jobs: cancel?
intent: the user asked me to batch prediction jobs: cancel
account { op: "keys.create", name: "gcp-vertex-ai-agent", allowed_actions: ["gcp_vertex_ai.*"], cap_calls: 2000, confirm_policy: "human_for_writes", approval_rules: [ { match: "gcp_vertex_ai.batch_prediction_jobs.cancel", approver: "[email protected]" }, { match: "gcp_vertex_ai.batch_prediction_jobs.delete", approver: "[email protected]" }, { match: "gcp_vertex_ai.models.delete", approver: "[email protected]" } ]}Cost
What Google Vertex AI costs on actionlake.
Every Google Vertex AI action is one upstream call, the only thing actionlake bills. Searching, check, cost, account and any call Google Vertex AI refused with 401, 403, 429 or a 5xx are free. Paid plans start at US$49 a month for 250,000 calls, with overage from US$0.30 down to US$0.18 per 1,000.
- 1upstream call per actionall 1,128 Google Vertex AI actions
- 196actions pageeach page of a long list is one call
- 217reads can be cacheda cache hit costs nothing
- 100,000calls a month, freepaid plans from US$49 a month for 250,000
Action index
Every Google Vertex AI action.
1,128 actions by area: the id your agent calls, what it does, and its effect.
Endpoints21
gcp_vertex_ai.endpoints.compute_tokensEndpoints: compute tokensreadgcp_vertex_ai.endpoints.generate_contentEndpoints: generate contentwrite- and 19 more in this area
Locations18
gcp_vertex_ai.locations.ask_contextsLocations: ask contextswritegcp_vertex_ai.locations.async_retrieve_contextsLocations: async retrieve contextswrite- and 16 more in this area
Models15
gcp_vertex_ai.models.copyModels: copywritegcp_vertex_ai.models.exportModels: exportwrite- and 13 more in this area
Featurestores entity types14
gcp_vertex_ai.featurestores_entity_types.createFeaturestores entity types: createwritegcp_vertex_ai.featurestores_entity_types.export_feature_valuesFeaturestores entity types: export feature valueswrite- and 12 more in this area
Feature online stores feature views13
gcp_vertex_ai.feature_online_stores_feature_views.createFeature online stores feature views: createwritegcp_vertex_ai.feature_online_stores_feature_views.direct_writeFeature online stores feature views: direct writewrite- and 11 more in this area
Reasoning engines12
gcp_vertex_ai.reasoning_engines.async_queryReasoning engines: async querywritegcp_vertex_ai.reasoning_engines.createReasoning engines: createwrite- and 10 more in this area
Datasets11
gcp_vertex_ai.datasets.createDatasets: createwritegcp_vertex_ai.datasets.exportDatasets: exportwrite- and 9 more in this area
Featurestores10
gcp_vertex_ai.featurestores.batch_read_feature_valuesFeaturestores: batch read feature valueswritegcp_vertex_ai.featurestores.createFeaturestores: createwrite- and 8 more in this area
Index endpoints10
gcp_vertex_ai.index_endpoints.createIndex endpoints: createwritegcp_vertex_ai.index_endpoints.deploy_indexIndex endpoints: deploy indexwrite- and 8 more in this area
Metadata stores contexts10
gcp_vertex_ai.metadata_stores_contexts.add_context_artifacts_and_executionsMetadata stores contexts: add context artifacts and executionswritegcp_vertex_ai.metadata_stores_contexts.add_context_childrenMetadata stores contexts: add context childrenwrite- and 8 more in this area
Publishers models10
gcp_vertex_ai.publishers_models.compute_tokensPublishers models: compute tokensreadgcp_vertex_ai.publishers_models.count_tokensPublishers models: count tokensread- and 8 more in this area
Reasoning engines memories10
gcp_vertex_ai.reasoning_engines_memories.createReasoning engines memories: createwritegcp_vertex_ai.reasoning_engines_memories.generateReasoning engines memories: generatewrite- and 8 more in this area
Studies trials10
gcp_vertex_ai.studies_trials.add_trial_measurementStudies trials: add trial measurementwritegcp_vertex_ai.studies_trials.check_trial_early_stopping_stateStudies trials: check trial early stopping statewrite- and 8 more in this area
Express reasoning engines9
gcp_vertex_ai.express_reasoning_engines.async_queryExpress reasoning engines: async querywritegcp_vertex_ai.express_reasoning_engines.createExpress reasoning engines: createwrite- and 7 more in this area
Express reasoning engines sandbox environments9
gcp_vertex_ai.express_reasoning_engines_sandbox_environments.authorize_accessExpress reasoning engines sandbox environments: authorize accesswritegcp_vertex_ai.express_reasoning_engines_sandbox_environments.createExpress reasoning engines sandbox environments: createwrite- and 7 more in this area
Memory banks memories9
gcp_vertex_ai.memory_banks_memories.createMemory banks memories: createwritegcp_vertex_ai.memory_banks_memories.generateMemory banks memories: generatewrite- and 7 more in this area
Reasoning engines sandbox environments9
gcp_vertex_ai.reasoning_engines_sandbox_environments.authorize_accessReasoning engines sandbox environments: authorize accesswritegcp_vertex_ai.reasoning_engines_sandbox_environments.createReasoning engines sandbox environments: createwrite- and 7 more in this area
Agents8
gcp_vertex_ai.agents.createAgents: createwritegcp_vertex_ai.agents.getAgents: getread- and 6 more in this area
Express publishers models8
gcp_vertex_ai.express_publishers_models.compute_tokensExpress publishers models: compute tokensreadgcp_vertex_ai.express_publishers_models.count_tokensExpress publishers models: count tokensread- and 6 more in this area
Feature groups8
gcp_vertex_ai.feature_groups.createFeature groups: createwritegcp_vertex_ai.feature_groups.getFeature groups: getread- and 6 more in this area
Feature online stores8
gcp_vertex_ai.feature_online_stores.createFeature online stores: createwritegcp_vertex_ai.feature_online_stores.getFeature online stores: getread- and 6 more in this area
Metadata stores executions8
gcp_vertex_ai.metadata_stores_executions.add_execution_eventsMetadata stores executions: add execution eventswritegcp_vertex_ai.metadata_stores_executions.createMetadata stores executions: createwrite- and 6 more in this area
Model deployment monitoring jobs8
gcp_vertex_ai.model_deployment_monitoring_jobs.createModel deployment monitoring jobs: createwritegcp_vertex_ai.model_deployment_monitoring_jobs.getModel deployment monitoring jobs: getread- and 6 more in this area
Notebook runtime templates8
gcp_vertex_ai.notebook_runtime_templates.createNotebook runtime templates: createwritegcp_vertex_ai.notebook_runtime_templates.getNotebook runtime templates: getread- and 6 more in this area
Tensorboards8
gcp_vertex_ai.tensorboards.batch_readTensorboards: batch readreadgcp_vertex_ai.tensorboards.createTensorboards: createwrite- and 6 more in this area
Tensorboards experiments runs time series8
gcp_vertex_ai.tensorboards_experiments_runs_time_series.createTensorboards experiments runs time series: createwritegcp_vertex_ai.tensorboards_experiments_runs_time_series.export_tensorboard_time_seriesTensorboards experiments runs time series: export tensorboard time seriesread- and 6 more in this area
Express endpoints7
gcp_vertex_ai.express_endpoints.compute_tokensExpress endpoints: compute tokensreadgcp_vertex_ai.express_endpoints.count_tokensExpress endpoints: count tokensread- and 5 more in this area
Indexes7
gcp_vertex_ai.indexes.createIndexes: createwritegcp_vertex_ai.indexes.getIndexes: getread- and 5 more in this area
Metadata stores artifacts7
gcp_vertex_ai.metadata_stores_artifacts.createMetadata stores artifacts: createwritegcp_vertex_ai.metadata_stores_artifacts.getMetadata stores artifacts: getread- and 5 more in this area
Notebook runtimes7
gcp_vertex_ai.notebook_runtimes.assignNotebook runtimes: assignwritegcp_vertex_ai.notebook_runtimes.getNotebook runtimes: getread- and 5 more in this area
Online evaluators 7Pipeline jobs 7Reasoning engines sessions 7Schedules 7Tensorboards experiments 7Tensorboards experiments runs 7Datasets dataset versions 6Deployment resource pools 6Evaluation sets 6Express datasets dataset versions 6Feature groups features 6Featurestores entity types features 6Persistent resources 6Batch prediction jobs 5Cached contents 5Custom jobs 5Custom jobs operations 5Data labeling jobs 5Data labeling jobs operations 5Datasets annotation specs operations 5Datasets data items annotations operations 5Datasets data items operations 5Datasets operations 5Datasets saved queries operations 5Deployment resource pools operations 5Endpoints operations 5Evaluation runs 5Express custom jobs operations 5Express data labeling jobs operations 5Express datasets 5Express datasets annotation specs operations 5Express datasets data items annotations operations 5Express datasets data items operations 5Express datasets operations 5Express datasets saved queries operations 5Express deployment resource pools operations 5Express endpoints operations 5Express featurestores entity types features operations 5Express featurestores entity types operations 5Express featurestores operations 5Express hyperparameter tuning jobs operations 5Express index endpoints operations 5Express indexes operations 5Express memory banks memories operations 5Express memory banks operations 5Express metadata stores artifacts operations 5Express metadata stores contexts operations 5Express metadata stores executions operations 5Express metadata stores operations 5Express migratable resources operations 5Express model deployment monitoring jobs operations 5Express models evaluations operations 5Express models operations 5Express notebook execution jobs operations 5Express notebook runtime templates operations 5Express notebook runtimes operations 5Express online evaluators operations 5Express operations 5Express persistent resources operations 5Express pipeline jobs operations 5Express rag corpora operations 5Express rag corpora rag files operations 5Express rag engine config operations 5Express reasoning engines memories operations 5Express reasoning engines operations 5Express reasoning engines sessions operations 5Express schedules operations 5Express semantic governance policies operations 5Express serving profiles operations 5Express skills operations 5Express specialist pools operations 5Express studies operations 5Express studies trials operations 5Express task stores operations 5Express tensorboards experiments operations 5Express tensorboards experiments runs operations 5Express tensorboards experiments runs time series operations 5Express tensorboards operations 5Express training pipelines operations 5Express tuning jobs operations 5Featurestores entity types features operations 5Featurestores entity types operations 5Featurestores operations 5Hyperparameter tuning jobs 5Hyperparameter tuning jobs operations 5Index endpoints operations 5Indexes operations 5Memory banks memories operations 5Memory banks operations 599 smaller areas 364
This index shows a sample of each area. All 1,128 actions, with their inputs and prices, are in the catalogue, which a free sandbox key can search.
Questions
The Google Vertex AI MCP server, answered.
Is there a Google Vertex AI MCP server for Claude?
Yes. actionlake is an MCP gateway: a remote MCP server that gives Claude, or another MCP client, 1,128 Google Vertex AI actions across endpoints, locations, models and featurestores entity types, through the same eight tools it uses for every other connector. actionlake is independent; it is not made or endorsed by Google Vertex AI.
What do I need to connect Google Vertex AI?
Your own Google Vertex AI OAuth app, registered with Google Vertex AI (its client id and client secret), and the access and refresh tokens from one admin sign-in. actionlake has no Google Vertex AI app of its own, so this is not a one-step consent. actionlake renews the access token as it expires. Plus your host. The free call account { op: "connections.requirements", service: "gcp_vertex_ai" } lists exactly what to paste.
Can an AI agent delete or change things in Google Vertex AI?
Only if its key allows it, and only with an explicit confirmation. 321 Google Vertex AI actions are destructive, including batch prediction jobs: cancel, batch prediction jobs: delete and models: delete. Each runs only with confirm: true. Under the human confirm policy, or an approval rule on the key, the run waits until a named person approves it.
Will an AI agent use up my Google Vertex AI API limit?
actionlake paces every call to the limits in the Google Vertex AI service file: 600 calls a minute per connection. Repeated reads are answered from cache (217 Google Vertex AI actions can be), which never reaches Google Vertex AI.
What does a Google Vertex AI action cost?
One upstream call, the only unit actionlake bills. A list that pages bills one call per page (196 Google Vertex AI actions page). Cache hits, searches, dry runs and calls Google Vertex AI refused with 401, 403, 429 or a 5xx are free. The Free plan includes 100,000 calls a month with no card. Paid plans start at US$49 a month for 250,000 calls, with overage from US$0.30 down to US$0.18 per 1,000.
Which Google Vertex AI API does it cover?
The Google Vertex AI (Gemini generation, model endpoints, model registry, datasets, training, pipelines, RAG, Agent Engine) API aiplatform v1 (Discovery revision 20261012) at aiplatform.googleapis.com by default, or the base URL set when you connect. It is built from Google Vertex AI's documentation as read on 22 October 2026. 1,128 actions in 218 areas, each one in the signed service file (version 1.0.0).
Does actionlake keep my Google Vertex AI data, and where?
actionlake processes and stores data in Sydney, Australia; there is no US region yet. Reads may be cached for up to 5 minutes, and a write clears the reads it affects. Each run's request and response is kept, sealed, for your plan's trace window: 1 day on Free, 3 days on Starter, 7 days on Pro, 14 days on Team, 30 days on Business and 30 days on Enterprise. Your Google Vertex AI credential is sealed under your tenant's key, never logged and never returned. Your traffic is not used to train models.
Which AI clients can use it?
Claude Code, tested by us end to end, and the Claude app as a custom connector, tested by us with an existing key. Other MCP clients that support remote servers over Streamable HTTP (MCP revisions 2025-06-18 and 2025-11-25) and OAuth dynamic client registration, or a bearer header, should connect the same way; we have not tested them. Or plain REST, under the same action names.
Give your agent Google Vertex AI today.
Start free - 100,000 calls/month, no card. No signup. Hard caps on every key. Cached answers are free.
