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clerk.create_machine

Create a machine in a connected Clerk instance for machine-to-machine authentication.

Sensitive — the response may include a machine secret_key on creation; do not log or expose it.

Call clerk.get_connected_accounts first. Pass clerk_instance_id to target a specific connection, or omit it to use the default account.

Cost = 10 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesMachine display name (1–255 characters).
scoped_machinesNoMachine ids (mch_...) this machine may access (max 150).
clerk_instance_idNoClerk instance id (ins_...) from clerk.get_connected_accounts. Omit to use the default connected account.
default_token_ttlNoDefault M2M token lifetime in seconds (minimum 1).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
machineNoCreated machine object from the Backend API.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the disclosure burden. It warns that the response may include a sensitive secret_key, advising not to log or expose it, and notes the token cost. This is valuable context, though it could go further by mentioning permission requirements or idempotency.

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

Conciseness5/5

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

The description is concise and well-structured: it starts with the core purpose, then highlights the sensitive nature, prerequisites, and cost. Every sentence adds information without unnecessary bloat.

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

Completeness4/5

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

The description covers prerequisites, sensitive return value, and cost, and the output schema presumably handles return format. It does not mention potential errors or rate limits, but for a creation tool with rich schema and clear usage, it is reasonably complete.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters with clear descriptions. The tool description adds only a hint about clerk_instance_id usage, which is helpful but does not significantly enrich parameter meaning beyond the schema's baseline.

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 clearly states the verb and resource: "Create a machine in a connected Clerk instance for machine-to-machine authentication." It specifies the purpose (M2M auth) and distinguishes this from sibling tools like create_m2m_token (which creates a token, not a machine) and create_api_key.

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?

It provides explicit when-to-use guidance by instructing to call clerk.get_connected_accounts first and explaining how to target a specific instance versus the default. It does not explicitly mention when not to use this tool or alternatives, so it falls 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.

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a distinct purpose, further clarified by group prefixes and clear descriptions. Within each group, tools perform different operations (e.g., domains.lookup vs. domains.whois vs. domains.rdap) with no ambiguity.

Naming Consistency5/5

All tools follow a consistent group.tool_name pattern using snake_case. The naming is predictable and uniformly applied across all groups.

Tool Count4/5

78 tools is high, but the server aggregates multiple distinct API domains (11 groups). Each group has a reasonable number of tools, typically under 10, with TikTok having 17. The count reflects breadth, not bloat.

Completeness5/5

Each domain's tool set covers the primary expected operations (e.g., search, details, reviews, metrics, user info). There are no obvious gaps for read-only analytical use; features like posting are likely out of scope.