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clerk.update_machine

Update a machine in a connected Clerk instance.

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

Returns the updated machine.

Cost = 8 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoUpdated machine display name.
machine_idYesClerk machine id (mch_...) to retrieve or modify.
clerk_instance_idNoClerk instance id (ins_...) from clerk.get_connected_accounts. Omit to use the default connected account.
default_token_ttlNoUpdated default M2M token lifetime in seconds.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
machineNoUpdated machine object from the Backend API.

Schema Changelog

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

  1. Added

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It mentions the cost and that it returns the updated machine, but does not elaborate on mutating side effects, idempotency, or required permissions beyond the update action implied by the tool name.

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 front-loaded with the action ('Update a machine'), followed by essential usage guidance and cost. There is no wasted text; every sentence serves a purpose.

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?

Given the presence of an output schema and 100% schema description coverage, the description is reasonably complete. It covers the prerequisite, instance selection, and return value, but does not address error conditions or partial-update semantics, which would be useful for a mutation tool.

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 baseline is 3. The description adds little beyond the schema, repeating the clerk_instance_id behavior already stated in the schema. It provides no additional parameter syntax or format details beyond what the schema already documents.

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 tool updates a machine in a connected Clerk instance, using the verb 'update' and resource 'machine'. This distinguishes it from sibling tools like clerk.create_machine, clerk.get_machine, and clerk.delete_machine.

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 gives a clear prerequisite ('Call clerk.get_connected_accounts first') and explains how to target a specific instance or use the default account. It does not explicitly mention alternatives or when not to use the tool, but the provided context is sufficient for a distinct update operation.

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.