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clerk.create_machine_scope

Create a machine scope so one machine can access another.

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 created machine scope.

Cost = 8 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
machine_idYesClerk machine id (mch_...) to retrieve or modify.
to_machine_idYesTarget machine id (mch_...) to grant access to.
clerk_instance_idNoClerk instance id (ins_...) from clerk.get_connected_accounts. Omit to use the default connected account.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
machine_scopeNoCreated machine scope 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
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. It discloses the return value ('Returns the created machine scope') and the token cost, plus the prerequisite. However, it doesn't mention potential side effects beyond creation, error conditions, or permission requirements.

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?

Four concise sentences, front-loaded with the core purpose. Every sentence provides value, including the prerequisite, parameter guidance, return value, and cost. No fluff.

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?

For a 3-parameter create tool with an output schema, the description covers the essential usage context: purpose, prerequisite, parameter selection, and return. It could mention error cases but is otherwise sufficiently complete.

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?

Schema coverage is 100%, but the description adds meaningful context for the optional parameter clerk_instance_id, clarifying when to use it vs omit. This goes beyond the schema's basic field descriptions.

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 a specific action ('Create a machine scope') with a clear resource and purpose ('so one machine can access another'). It distinguishes from siblings like create_machine or delete_machine_scope by clarifying this is about granting access between machines.

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?

Provides prerequisite guidance ('Call clerk.get_connected_accounts first') and explains the optional parameter selection (use clerk_instance_id for a specific connection or omit for default). It doesn't explicitly mention alternatives, but the context is clear.

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.