Hermes Plant

BondLens — bond & loan analytics

bond_analyze

Idempotent

BondLens (x402-paid, $0.25): deterministic fixed-income and loan analytics. Bond mode solves price<->yield-to-maturity, duration, and convexity; loan mode (send principal+annualRate+termMonths) returns an amortization schedule.

Input Schema

TableJSON Schema

Name	Required	Description
`payer`	No	Optional wallet/account identifier; stored only as a hash
`price`	No	Bond price; supply to solve yield
`yield`	No	Annual yield; supply to solve price
`apiKey`	No	Free-tier / plan API key (hp_free_… or a pass key). Forwarded as X-API-Key so paid tools serve from your monthly quota with NO x402 wallet. Get a free key (250 calls/mo) at https://hermesplant.com/pricing.
`channel`	No	Discovery channel or source tag
`periods`	No	Periods to maturity
`campaign`	No	Campaign tag for downstream telemetry
`xPayment`	No	Raw X-PAYMENT proof from an x402-compatible wallet/client
`actorType`	No	Caller type for analytics: agent, human, synthetic, system, or unknown
`faceValue`	No	Bond face value (default 1000)
`frequency`	No	Coupons per year (default 2)
`principal`	No	Loan principal (selects loan mode)
`synthetic`	No	Mark this paid retry as an internal test/probe for analytics exclusion
`annualRate`	No	Loan annual rate, e.g. 0.06
`couponRate`	No	Annual coupon rate, e.g. 0.05
`termMonths`	No	Loan term in months
`paymentSignature`	No	x402 payment proof to forward as PAYMENT-SIGNATURE and X-PAYMENT on retry
`paymentIdentifier`	No	Optional x402 payment identifier for idempotency/retry correlation

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	True when the upstream storefront call returned a 2xx response
`httpStatus`	Yes	Upstream HTTP status code
`paymentRequired`	No	True when the response is an x402 HTTP 402 payment challenge

TDQS

A4.1/5.0

Behavior4/5

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

The description adds important context beyond the annotations: it is x402-paid and deterministic, indicating cost and predictable behavior. Annotations only indicate idempotent and non-destructive, so the payment detail is valuable complementary information.

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 two sentences, front-loaded with the tool's identity and payment requirement, and each clause contributes to understanding the tool's capabilities. No filler or redundancy.

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?

Given the complexity of the tool with 18 parameters and an output schema, the description covers the essential mode selection and core analytics, while leaving parameter details to the schema. It is sufficient for an agent to choose and invoke correctly, though it could note required parameters for bond mode.

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 schema covers all parameters with descriptions, but the description adds useful grouping for loan mode (principal+annualRate+termMonths) and explains the bond mode's price/yield relationship. This goes beyond the individual parameter descriptions.

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 clearly identifies the tool as providing bond and loan analytics, with specific functions: solving price-to-yield, duration, convexity, and amortization schedules. It is clear about the two modes, but does not explicitly differentiate from sibling analytics tools like cashflowlens_analyze.

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 gives concrete guidance on when to use each mode: loan mode requires principal, annualRate, and termMonths, while bond mode handles price/yield relationships. It implies context for usage but does not mention when not to use this tool or name alternative tools.

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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TDQS

A3.9/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose. Financial tools (bond, cashflow, deal, options, portfolio, wallet, waterfall) each target a specific analysis type, email tools are batch vs single, and e-commerce tools are separate. No two tools could be easily confused.

Naming Consistency4/5

Most tools follow a noun_verb pattern (e.g., bond_analyze, emailguard_validate), but there is some inconsistency: some use verb_noun (get_product, list_products) and brand names like cashflowlens_analyze break the pattern slightly. Overall, it is still readable and mostly predictable.

Tool Count4/5

With 19 tools spanning finance, email, security, and e-commerce, the count is slightly high but reasonable for a pay-per-use server offering diverse deterministic analytics. Each tool serves a distinct function, and the number is not overwhelming.

Completeness4/5

The set covers major financial analysis types, email validation, and basic e-commerce operations. Minor gaps (e.g., no tool for portfolio rebalancing or more advanced email features) exist, but the core advertised services are well-covered.

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