cd_ladder_calculatorCD Ladder Calculator — Build a CD ladder: split your savings across staggered-maturity CDs and see year-one interest, per-rung breakdown, and your blended APY across the whole ladder.
| Name | Required | Description | Default |
|---|---|---|---|
| step | Yes | ||
| rungs | Yes | ||
| baseApy | Yes | ||
| principal | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the tool's primary outputs (interest, breakdown, blended APY) but doesn't mention assumptions, edge cases, or any side effects. For a calculator this is acceptable but not rich; no mention of how inputs like 'step' affect results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with a dash leading into output details. No wasted words, and it front-loads the core action ('Build a CD ladder') before listing results.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With four required parameters, no output schema, and no annotations, the description should provide richer context. It explains outputs but leaves key parameter semantics (especially 'step') ambiguous, and doesn't clarify the calculation model or input constraints beyond the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It indirectly explains 'principal' (savings) and 'rungs' (staggered-maturity CDs), but 'baseApy' and especially 'step' are not described at all. The agent would need to infer parameter meanings from context or external knowledge.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: building a CD ladder by splitting savings across staggered-maturity CDs. It names specific outputs (year-one interest, per-rung breakdown, blended APY), distinguishing it from sibling calculators like apy_calculator or compound_interest_calculator.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through its definition but provides no explicit guidance on when to use this tool versus alternatives. It doesn't mention exclusions or alternative tools, leaving the agent to infer context from the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many calculators occupy overlapping conceptual spaces, such as 'ai_roi_calculator' vs 'ai_automation_payback_calculator' and 'llm_self_host_vs_api_calculator' vs 'ai_build_vs_buy_calculator'. The boundaries between debt payoff, savings goal, and drawdown tools are also fuzzy, making it easy for an agent to select the wrong tool despite detailed descriptions.
Every tool follows the same <topic>_calculator pattern with lowercase snake_case, making the naming highly predictable and consistent. Even acronyms and numbers fit the pattern, so there is no mixing of conventions.
122 tools is an extreme number for a single MCP server, far exceeding the 50+ threshold for a severe mismatch. The tools span unrelated domains like AI costs, pet food, concrete, pizza dough, and turkey cooking, creating an unfocused kitchen-sink surface that overwhelms an agent's selection process.
The set covers many common calculator categories such as finance, construction, health, and AI costs, but several staple calculators are missing (e.g., BMI, tip, discount, simple interest, currency conversion). The AI cost cluster is over-saturated while other everyday calculations are absent, leaving minor but noticeable gaps.