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Check autocoding agent prerequisites

check_agent_prerequisites
Read-only

Checks if your account has everything needed to run autonomous coding agents. Returns a checklist: API key validity, private network, SSH keypair, and billing. Safe to call anytime — read-only. Call this first before create_controller.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
github_patNoGitHub Personal Access Token to validate (repo + workflow scopes required)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
all_okNo
checksNo

TDQS

A4.5/5.0
Behavior5/5

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

Description correctly states read-only behavior, matching annotations. Adds value by disclosing the return type (checklist of specific prerequisites), going beyond what annotations alone provide.

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 concise sentences: first explains purpose and output, second advises on when to call. No redundancy or fluff.

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 output schema exists and the tool has only one optional parameter, the description is complete. It covers usage context, safety, and expected results without needing to detail return fields.

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 covers 100% of the single optional parameter (github_pat) with a clear description. The tool description does not add any additional parameter semantics, meeting the baseline for high schema coverage.

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?

Description clearly states the tool checks account prerequisites for autocoding agents and returns a specific checklist (API key, network, SSH, billing). It distinguishes itself from sibling check_deploy_prerequisites by specifying 'autocoding agents'.

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?

Explicitly says 'Safe to call anytime — read-only' and 'Call this first before create_controller', providing clear when-to-use guidance. Lacks explicit when-not-to-use or alternative tool mentions, but the context is sufficient for the agent.

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

A3.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, and the descriptions include specific details (e.g., create_database for Postgres vs create_relational_database for MySQL/MariaDB) that prevent confusion. Even similar tools like plan_deploy, plan_instance, and plan_managed_datastore are well-differentiated by their contexts.

Naming Consistency5/5

The vast majority of tools follow the verb_noun pattern with underscores (create_*, list_*, delete_*, etc.). Only minor deviations like 'whoami' exist, but the overall pattern is highly consistent and predictable.

Tool Count2/5

With 69 tools, the server has an excessive number for an MCP context, far exceeding the typical 3-15 range. While it may mirror the full redu.cloud API, the sheer count overwhelms agents and increases selection latency.

Completeness4/5

The tool set covers the majority of cloud management operations: CRUD for instances, volumes, databases, deployments, and more. However, update operations are missing for several resources (e.g., instance resize, volume resize), and there is no tool to modify deployments without redeploying.

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