Conecto

Score a company's financial health

valuation_scores

Read-onlyIdempotent

From one or more years of figures: the Altman Z-score (public or private manufacturer, or the non-manufacturing Z'') and its zone, the Piotroski F-score with each of its nine signals, the Beneish M-score with its eight indexes (both need two years), and whether the company is growing, mature or distressed, with the reasons. A figure that is missing is named, never guessed. Free.

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": {
      "years": "<years>"
    },
    "name": "valuation_scores"
  }
}

Input Schema

Name	Required	Description	Default
`years`	Yes	One object per year; companies_financials gives these fields.
`altman`	No		non_manufacturing
`growth_threshold`	No	Revenue growth a year that counts as growing. A decimal: 0.09 is 9%.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 28, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world). The description adds real behavioral context beyond that: missing figures are named rather than guessed, two-year data requirement for two of the scores, and that the operation is free. These are useful non-obvious traits.

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

Conciseness4/5

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

The core output list is front-loaded, followed by concise data-integrity and cost notes. The long first sentence is dense but every clause carries information; nothing is wasted.

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 carries the burden of describing returns and does so thoroughly by naming each score and its sub-components. Input expectations and data-integrity behavior are covered; only explicit sibling routing is missing.

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?

Schema coverage is 67%, and the description adds meaning by explaining that 'years' is a per-year set of figures and that the Altman option distinguishes public/private manufacturer vs. non-manufacturing. It would be stronger if it referenced growth_threshold, but it does clarify the intent of the input data.

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

Purpose4/5

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

The description states a specific verb (score) and resource (financial health) and enumerates the exact outputs: Altman Z-score, Piotroski F-score, Beneish M-score, and growth stage. It is easy to distinguish from siblings like valuation_dcf or valuation_multiples, though it does not explicitly name those siblings.

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

Usage Guidelines3/5

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

It gives meaningful data requirements (Altman variant selection, two years needed for F and M scores, one or more years of figures), which implies when the tool is applicable. It does not, however, say when to use this vs. valuation_benchmark or valuation_dcf, leaving that to inference.

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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