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wuqunfei

github-mcp-server-js

by wuqunfei

get_copilot_organization_details

Retrieve GitHub Copilot seat breakdown and policy settings for an organization, including subscription plan, active seats, and code filtering configuration.

Instructions

Get GitHub Copilot seat information and policy settings for an organization.Returns seat breakdown (total, active, inactive, pending cancellation, pending invitation, added this cycle), subscription plan type (business or enterprise), and policy settings (public code suggestions filter, IDE chat, platform chat, CLI enablement, seat management mode). Requires the authenticated user to be an organization owner. Requires a token with manage_billing:copilot or read:org scope. Returns 404 if the organization does not have a Copilot Business or Enterprise subscription. Returns 403 if the token lacks sufficient scope or the user is not an org owner. Docs: https://docs.github.com/en/rest/copilot/copilot-user-management#get-copilot-seat-information-and-settings-for-an-organization

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orgYesThe organization login name (e.g. "my-company").
Behavior5/5

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

With no annotations provided, the description fully discloses behavior: it lists exactly what data is returned, requires specific permissions (org owner, token scopes), and documents both 404 and 403 error conditions. This goes well beyond the minimum and gives the agent a precise understanding of outcomes.

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 well-structured and front-loaded with the core purpose, then details returns, requirements, and errors. It is longer than the minimum but every sentence adds value. Slight redundancy with the docs link could be trimmed, but it remains appropriately sized.

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 the simple parameter set and absence of an output schema, the description is remarkably complete. It covers what is returned, prerequisites, and error scenarios, leaving little ambiguity for an agent deciding to call or interpret the result.

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?

The schema already provides 100% coverage for the single parameter 'org' with a clear example. The description adds no additional parameter-level meaning, so the baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states it retrieves GitHub Copilot seat information and policy settings for an organization, with a specific verb and resource. It lists detailed return fields, making the purpose concrete. However, it does not explicitly distinguish itself from sibling tools like list_copilot_seats or get_copilot_seat_details_for_user, though the organization-level scope is implied.

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 provides clear context on when to use the tool (for org-level Copilot management) and prerequisites (must be org owner, token scopes). It does not explicitly name alternatives or exclusions, but the clear context and requirements effectively guide usage.

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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