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Inferventis — Financial Data, News & Web MCP

financial_calculator_lite

Performs common financial calculations locally with no external API dependency. Supports compound interest, loan repayment, return on investment (ROI), present value, future value, and break-even analysis. Returns a single numeric result for the requested calculation type. This is a lightweight variant of financial_calculator — it returns only the result number rather than a full structured breakdown (monthly payment, total interest, annualised ROI, etc.). Use financial_calculator_lite when only the headline figure is needed. Prefer financial_calculator when the agent needs a full breakdown, multiple sub-values, or labelled output fields for compound interest earned, total repayable, or annualised returns. Neither this tool nor financial_calculator fetches live market data — for live prices use stock_quote, crypto_price, or currency_convert.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rateNoAnnual interest or discount rate as a decimal (e.g. 0.05 for 5%). Required for compound_interest, loan_repayment, present_value, and future_value.
periodsNoNumber of compounding periods (years) or loan term in months depending on calculator_type. Required for compound_interest, loan_repayment, future_value, and present_value.
principalNoStarting amount or loan principal in currency units. Required for compound_interest, loan_repayment, present_value, and future_value.
final_valueNoFinal investment value. Required for roi calculations.
initial_valueNoInitial investment amount. Required for roi calculations.
calculator_typeYesType of calculation to perform. Accepted values: 'compound_interest', 'loan_repayment', 'roi', 'present_value', 'future_value', 'break_even'.

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 fully carries the behavioral burden. It discloses local execution with no external API dependency, returns only a numeric result rather than a breakdown, and explicitly states it does not fetch live market data. This is comprehensive transparency for a calculator tool.

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 well-structured, starting with core functionality, then output format, differentiation, usage guidance, and limitations. Every sentence contributes valuable information with no redundancy, and the longest part is justified by the need to distinguish from sibling tools.

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 absence of an output schema and annotations, the description provides sufficient context: supported calculation types, output nature, and relationship to sibling tools. It also clarifies the lack of live data, which is critical for correct tool selection. The description is complete for its complexity.

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?

The input schema already provides 100% coverage, detailing each parameter and the accepted values for calculator_type. The description adds no additional parameter-level meaning beyond what the schema states, so the baseline of 3 is appropriate.

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 clearly states the tool performs common financial calculations locally, lists supported calculation types, and emphasizes it returns a single numeric result. It explicitly distinguishes itself from financial_calculator by being the lightweight variant, making the purpose unmistakable.

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

Usage Guidelines5/5

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

It provides explicit guidance: use this tool when only the headline figure is needed, prefer financial_calculator for full breakdowns, and use stock_quote, crypto_price, or currency_convert for live market data. This gives clear when-to-use and when-not-to-use instructions with named alternatives.

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
Disambiguation3/5

Many tools have overlapping purposes, especially in currency conversion (5 tools) and financial calculations (2 tools). While descriptions are detailed and try to differentiate, the sheer number of similar tools could confuse an agent. The platform_tool_finder tool helps but doesn't fully resolve ambiguity.

Naming Consistency3/5

Naming follows snake_case but is inconsistent: some tools use noun_verb (e.g., currency_convert), others noun_noun (e.g., bank_accounts). There are also variants with suffixes like '_lite' and '_open' which help, but the pattern varies across the set.

Tool Count4/5

20 tools is reasonable for a financial data and news server, covering stocks, crypto, fiat, banking, payments, calculations, and web content. However, there is redundancy (5 fiat converters) that could be streamlined, making the count slightly higher than ideal.

Completeness4/5

The tool set covers a broad range of financial tasks: real-time stocks, crypto, fiat conversion, bank transactions, payments, financial calculations, news, and web reading. Minor gaps exist, such as lack of historical stock data or portfolio tracking, but most common queries can be handled.