mcp-github-enterprise

Recommend enterprise setup

recommend_enterprise_setup

Read-onlyIdempotent

Returns a staged secure-setup template for GitHub Enterprise, covering enterprise, organization, and repository controls with rationale and the organization-settings baseline used to harden organizations.

Instructions

Return the staged secure-setup template for a GitHub Enterprise — enterprise, organization, and repository level controls with the rationale for each, plus the exact org-settings baseline that harden_org_settings applies. Pure guidance; use assess_org_posture to measure a specific org against it.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "recommend_enterprise_setup"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Nov 7, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds genuinely new behavior: the return is a template with per-control rationale, and it is the exact baseline that harden_org_settings applies — a useful cross-tool relationship. It stops short of describing the shape/format of the returned template, which is the one remaining behavioral gap.

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?

Two sentences, front-loaded with the return value, then the usage mode and the alternative. No filler, no repetition of the title, and every clause carries information an agent needs.

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?

With no output schema, the description must carry the return-value burden, and it largely does: levels covered, inclusion of rationale, and the baseline relationship. It could be more complete by indicating the return format (structured object vs. markdown guidance) so the agent knows how to consume it.

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?

The tool takes zero parameters, so there is no parameter meaning to convey and the baseline of 4 applies. Nothing in the description misdescribes inputs, and it correctly signals a no-argument invocation.

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?

Specific verb and resource: 'Return the staged secure-setup template for a GitHub Enterprise', and it enumerates the contents (enterprise/organization/repository level controls with rationale plus the org-settings baseline). This is clearly distinguishable from assess_org_posture, which the description explicitly names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States the usage mode ('Pure guidance') and routes the agent to the alternative: 'use assess_org_posture to measure a specific org against it.' The when-to-use vs. when-to-use-the-sibling split is explicit rather than inferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.