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minia2a-mcp

x402-irr-calc

IRR Calc: Calculate the Internal Rate of Return (IRR) from a cashflow series. Provide cashflows or flows as an array (e.g. [-1000,300,300,300,300]); returns the rate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
flowsNoFlows to process
cashFlowsNoCashFlows to process
cashflowsNoCashflows to process

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses the input shape and that it 'returns the rate,' but it does not specify whether the returned rate is a decimal or percentage, the required sign convention beyond the example, or edge-case behavior such as multiple IRRs or non-convergence.

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 description is a single front-loaded sentence with a concrete example and no wasted words. The 'IRR Calc:' prefix is mildly redundant, but it does not meaningfully detract from readability.

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?

The example makes the core usage actionable, but with no output schema and no annotations, the description should clarify output units, parameter selection among the three aliases, and sign convention. These gaps leave room for an agent to call the tool incorrectly despite the clear example.

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%, so the baseline is 3. The schema parameter descriptions are generic ('Flows to process'), and the description adds an array example and mentions 'cashflows or flows,' but it leaves the three aliases (flows, cashFlows, cashflows) ambiguous and does not reconcile the schema's 'string' type with the description's 'array' phrasing.

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 and resource: 'Calculate the Internal Rate of Return (IRR) from a cashflow series,' with a concrete example. It is clear what the tool does, though it does not explicitly differentiate it from sibling finance tools such as npv-calc or annualized-return.

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 intended use is clear: when an agent needs an IRR from a cashflow series, this tool performs that calculation. The example input format gives additional usage guidance. It does not name alternatives or explicitly state when not to use the tool, which keeps it from a 5.

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

D1.6/5.0
Disambiguation1/5

The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.

Naming Consistency2/5

Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.

Tool Count1/5

1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.

Completeness2/5

The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.

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