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Launch Engine

by ZionHopkins
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

autonomy

Read-only

Score a market across 7 dimensions to determine its viability for AI-built products.

Instructions

Agent Autonomy Score — scores a market across 7 dimensions for AI-buildable product viability.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "market_name": "<market_name>"
    },
    "name": "autonomy"
  }
}

Input Schema

NameRequiredDescriptionDefault
market_nameYesThe market/pipeline slug

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true and destructiveHint=false, indicating safe read operations. The description adds that it scores across 7 dimensions, which is useful behavioral context, but does not elaborate on other traits like rate limits or auth needs. With annotations covering the safety profile, a score of 3 is appropriate.

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 a single, front-loaded sentence that efficiently communicates the tool's purpose with no wasted words. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool (one parameter, read-only, no output schema), the description is adequate but minimal. It does not describe the return format or how the score is presented, which could be useful for the agent. Still, for a basic scoring tool, it is sufficient.

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 coverage is 100% with the single parameter 'market_name' described as 'The market/pipeline slug'. The description does not add any extra meaning or context for this parameter, so the baseline of 3 is justified.

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 the verb 'scores', the resource 'a market', and the specific focus 'across 7 dimensions for AI-buildable product viability'. This distinguishes it from sibling tools like market_intel or scout, which likely serve different purposes.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or context that would help an agent decide between this and sibling tools like market_intel or scout.

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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