Skip to main content
Glama
Sign In
errorbar

errorbar

by omnia-v
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

create dedicated endpoint

create_dedicated_endpoint

Provision a dedicated inference endpoint with reserved GPUs at a frozen per-GPU-hour price, ensuring guaranteed capacity and predictable latency for custom fine-tuned models.

Instructions

Provisions a new dedicated inference endpoint (a model served on reserved GPUs at a frozen per-GPU-hour price) — use it for guaranteed capacity, custom fine-tuned weights, or predictable latency; billing starts as soon as it is running. POST /v1/dedicated (API-key scope: platform:write). SPENDS MONEY: this starts billable work on the workspace wallet. Returns: 201 with { id: }. Poll GET /v1/dedicated/{id} for status and routing_key. Notes: 400 'Invalid JSON body' or 'Missing required field(s): ...' when any of name, model_name, flavor_name, gpu_type, gpu_count, region, min_replicas, max_replicas is absent/null (checked after alias lifting). Requires an OWNER/ADMIN minting user (403). 400 when the model/flavor/GPU/region/count combo is not in the catalog, replica range invalid, or no price is configured for the GPU/region. MONEY: 402 'Insufficient balance' unless the wallet covers at least DEDICATED_PREPAY_HOURS (default 1 hour) of runway at min_replicas x gpu_count x sell rate; the per-GPU-hour price is FROZEN on the endpoint at create time; GPU-hours are metered continuously while the endpoint is enabled and RUNNING — stop (PATCH enabled=false) or DELETE to stop billing. Scope note: the dedicated routes' local apiKeyActor does not enforce key scopes on this branch.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "model_name": "<model_name>",
      "flavor_name": "<flavor_name>",
      "gpu_type": "<gpu_type>",
      "gpu_count": "<gpu_count>",
      "region": "<region>",
      "min_replicas": "<min_replicas>",
      "max_replicas": "<max_replicas>"
    },
    "name": "create_dedicated_endpoint"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesDisplay name (trimmed, non-empty).
regionYesMust be in the GPU configuration's allowed_regions.
gpu_typeYesA key of the flavor's available_configurations.gpu_configurations. Alias: gpuType.
gpu_countYesMust be > 0 and in the GPU configuration's allowed_gpu_counts. Alias: gpuCount.
model_nameYesA template `name` from GET /v1/dedicated/templates. camelCase alias modelName also accepted (camelCase wins if both present).
descriptionNoOptional description (trimmed).
flavor_nameYesA key of that template's `flavors` map. Alias: flavorName.
max_replicasYes>= min_replicas and <= the configuration's max_replicas_allowed. Alias: maxReplicas.
min_replicasYes>= 1. Alias: minReplicas. Sizes the prepay/wallet gate (min_replicas x gpu_count x hourly price x prepay hours).
custom_weights_idNoServe a fine-tuned model's merged weights: must start with 'model-artifact_' (the artifact id from a completed fine-tune), else 400. Alias: customWeightsId. Omit for stock base models.
fine_tuning_job_idNoThe source fine-tuning job to record on the endpoint, when deployed from one. Alias: fineTuningJobId.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description is exceptionally transparent about behavior beyond annotations: it warns that money is spent, explains that billing starts once running, states the prepay requirement, notes that the price is frozen at create time, and explains how to stop billing via PATCH or DELETE. This goes far beyond the annotations and gives the agent critical operational knowledge.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but densely informative, front-loading the core purpose and billing warning. There is slight redundancy between 'billing starts as soon as it is running' and the later 'SPENDS MONEY' and 'MONEY' sections, but every substantive detail earns its place given the financial and operational stakes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a high-complexity tool with 11 parameters, no output schema, and significant cost implications, the description is remarkably complete. It covers the success response, polling flow, auth requirements, validation failures, catalog constraints, prepay wallet gate, and billing-stop mechanisms. An agent has enough information to call this tool correctly and anticipate consequences.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% parameter coverage, so the baseline is 3. The description adds meaningful cross-parameter context, such as the prepay calculation involving min_replicas x gpu_count x hourly price, the required-field validation after alias lifting, and catalog/replica-range failure modes. This adds value beyond the schema without duplicating it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with a specific verb and resource: 'Provisions a new dedicated inference endpoint', and expands on what that means (reserved GPUs, frozen per-GPU-hour price, guaranteed capacity, custom weights, predictable latency). It is clearly distinct from sibling tools like get, update, delete, and list_dedicated_endpoints.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'use it for guaranteed capacity, custom fine-tuned weights, or predictable latency'. It also gives important usage context around billing, auth scope, and polling for status, but it does not explicitly name alternative tools or say when not to use it, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools