lazymac-mcp

startup_valuation

Calculate startup valuations using seven methods including DCF, Berkus, Scorecard, VC, and comparables to support investment decisions.

Instructions

7 valuation methods (DCF, Berkus, Scorecard, VC, comparables)

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": "startup_valuation"
  }
}

Input Schema

Name	Required	Description	Default
`params`	No	Free-form params object — passed as query string for GET, JSON body for POST

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Apr 15, 2026

TDQS

D1.6/5.0

Behavior1/5

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

No annotations are provided, so the description must fully disclose behavioral traits. However, it does not explain what the tool does operationally (e.g., calculates, estimates, analyzes), what inputs it expects beyond the generic 'params', or what outputs it produces. There is no mention of rate limits, authentication needs, or other behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (a single phrase listing methods) but lacks structure and front-loading of key information. It does not start with a clear purpose statement. While it's brief, it under-specifies rather than being efficiently informative.

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

Completeness1/5

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

Given the complexity implied by valuation methods and the lack of annotations and output schema, the description is highly incomplete. It does not explain what the tool does, how to use it, what results to expect, or how it differs from siblings. This is inadequate for a tool that likely performs calculations or analyses.

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

Parameters2/5

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

The input schema has 100% description coverage but only describes 'params' as a free-form object passed as query string or JSON body. The tool description does not add any meaning about what parameters are needed (e.g., financial data, method preferences) or their semantics. With 1 parameter and high schema coverage, the baseline is 3, but the description fails to compensate by explaining parameter usage.

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

Purpose2/5

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

The description lists 7 valuation methods (DCF, Berkus, Scorecard, VC, comparables) but does not state what the tool actually does with them. It's unclear whether this tool calculates valuations, explains methods, compares them, or something else. The name 'startup_valuation' suggests it calculates valuations, but the description is vague about the specific action.

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or comparisons with sibling tools like 'ai_budget_planner' or 'ai_cost_calculator'. This leaves the agent without direction on appropriate usage scenarios.

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