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MidOSresearch

MidOS Research Protocol MCP

agent_handshake

Onboard your agent by providing model, client, platform, languages, and project goal to receive optimal configuration for the MidOS environment.

Instructions

Personalized agent onboarding. Declare your environment and get optimal config.

Call this FIRST when connecting to MidOS. Pass as much info as you know. Unknown fields can be left empty -- you'll get sensible defaults.

Args: model: Your model ID (e.g. 'claude-opus-4-6', 'gemini-2.5-pro', 'opus') context_window: Your context window in tokens (e.g. 200000). 0 = auto-detect from model. client: Your CLI/IDE (e.g. 'claude-code', 'cursor', 'windsurf', 'cline') languages: Comma-separated languages (e.g. 'python,typescript') frameworks: Comma-separated frameworks (e.g. 'fastapi,react') platform: Your OS (e.g. 'windows', 'linux', 'macos') project_goal: What you're working on (e.g. 'manga engine with SVG rendering')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
clientNo
platformNo
languagesNo
frameworksNo
project_goalNo
context_windowNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must convey behavior. It discloses that the tool provides optimal config and gives defaults, but does not mention side effects, permissions, or whether it modifies state. This is adequate for a simple onboarding tool.

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 efficiently structured: a clear opening sentence, an imperative usage note, and a bullet-like list of arguments. Every sentence adds value without 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 the tool's complexity (7 parameters, 0 required, output schema present), the description covers all aspects: purpose, usage order, parameter details, and default behavior. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description lists all 7 parameters with concise explanations and examples (e.g., '0 = auto-detect from model'). This fully compensates for the lack of 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 clearly states 'Personalized agent onboarding' and 'get optimal config', with an explicit directive to 'Call this FIRST'. This differentiates it from siblings like agent_bootstrap, establishing a distinct purpose.

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 explicitly says to call this first and advises passing as much info as possible, with defaults for unknowns. It does not mention when not to use or alternative tools, but the usage 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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