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clerk.revoke_actor_token

Revoke an actor token in a connected Clerk application so it can no longer be used for impersonation.

Sensitive — invalidates a high-privilege token.

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 revoked actor token object.

Cost = 5 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actor_token_idYesClerk actor token id to revoke.
clerk_instance_idNoClerk instance id (ins_...) from clerk.get_connected_accounts. Omit to use the default connected account.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
actor_tokenNoRevoked Clerk actor token object from the Backend API.

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the sensitive, invalidating nature ('Sensitive — invalidates a high-privilege token'), notes the return value, and even mentions cost. This is good behavioral context, though it could go deeper on irreversibility 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?

The description is compact and front-loaded with the main action. Every sentence earns its place: purpose, sensitivity warning, prerequisite/parameter guidance, return value, and cost. No fluff or redundancy.

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?

Given that an output schema exists and both parameters are fully documented, the description is complete enough. It covers the high-level effect, the prerequisite workflow, the optional connection targeting, and the return value, making it self-sufficient for an agent to invoke correctly.

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%, so the baseline is 3. The description adds value by explaining the optional/default behavior of clerk_instance_id ('omit it to use the default account') and the prerequisite step to retrieve it, which goes beyond the raw schema 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 uses a specific verb+resource+effect: 'Revoke an actor token in a connected Clerk application so it can no longer be used for impersonation.' This clearly distinguishes it from sibling tools like create_actor_token and other revoke_* tools.

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 explicit guidance: 'Call clerk.get_connected_accounts first. Pass clerk_instance_id to target a specific connection, or omit it to use the default account.' This is clear context and prerequisite instruction, though it does not explicitly state when not to use this tool or name alternatives.

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.