counterparty-credit

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counterparty.health

Evaluate financial risk of an energy counterparty. Returns transparent 0-100 credit score, factor breakdown, and public sources for decision support.

Instructions

Assess the credit health of a public energy company from public data.

Use this when asked how financially sound or risky an energy counterparty is — a regulated utility, merchant generator/IPP, midstream operator, or power/gas marketer. Accepts a company name or ticker.

Returns a 0-100 health score and descriptive band (Strong/Stable/Watch/Stressed/ Distressed), a factor-by-factor breakdown with the public source behind each factor, a plain-language summary, the methodology version, and an as-of date. This is transparent decision-support, not a credit rating.

Raises rather than guessing when the company cannot be resolved or its public data cannot be retrieved. A score is only ever returned when it was actually computed.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`band`	Yes	Descriptive band derived from the score
`name`	Yes	The query as the caller sent it
`as_of`	Yes	As-of date for the underlying data (YYYY-MM-DD)
`score`	Yes	Composite 0–100 health score
`ticker`	No	Equity ticker, if resolved
`factors`	Yes	Factor-by-factor breakdown, each independently sourced
`summary`	Yes	Plain-language read a desk could act on
`disclaimer`	No	Non-negotiable framing — this is not a rating.	Decision-support, not a credit rating. Transparent public-data methodology; outputs are explainable and intended for human review.
`resolved_name`	Yes	Canonical entity name after resolution
`methodology_version`	Yes	Versioned methodology id, e.g. 'tenor-0.1.0-stub'

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 28, 2026

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It explains the return contents (health score, band, factor breakdown, sources, summary, methodology version, as-of date), frames the output as 'transparent decision-support, not a credit rating,' and explicitly states it 'raises rather than guessing' when resolution or data retrieval fails.

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 front-loaded with the main action, immediately followed by usage context, output details, and an explicit failure behavior. Every sentence contributes necessary information without redundancy, and the structure makes it easy for an agent to quickly determine purpose and call behavior.

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 single parameter, no annotations, no siblings, and an output schema that can carry return structure, the description covers all key contextual needs: input type, applicable domain, output semantics, methodological transparency, and error behavior. An agent has enough information to invoke the tool correctly and interpret its result.

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?

The input schema only provides a required string property 'name' with 0% description coverage, so the description must compensate. It does by adding that the tool 'Accepts a company name or ticker.' This is meaningful semantic guidance for the single parameter, though slightly more detail about accepted formats would push it higher.

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 starts with a specific verb and resource: 'Assess the credit health of a public energy company from public data.' It further clarifies the exact scope by listing company types (regulated utility, merchant generator/IPP, midstream operator, marketer) and the accepted inputs (company name or ticker). Even without siblings, the purpose is unambiguous.

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 states when to use the tool: 'Use this when asked how financially sound or risky an energy counterparty is.' It gives clear context and enumerates the applicable counterparty types, but it does not explicitly state when not to use it or name alternatives. Since there are no sibling tools, this is a clear and sufficient usage guideline.

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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