closing_cost_calculatorMortgage Closing Cost Estimator — Estimate US mortgage closing costs before you buy. See itemized origination, appraisal, title, and prepaid fees with typical ranges and cash to close.
| Name | Required | Description | Default |
|---|---|---|---|
| titleFee | Yes | ||
| homePrice | Yes | ||
| loanAmount | Yes | ||
| appraisalFee | Yes | ||
| recordingFee | Yes | ||
| prepaidEscrow | Yes | ||
| originationFeePct | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that the tool provides an estimate with typical ranges and cash to close, which is useful behavioral context. It does not detail assumptions or limitations, but the main output behavior is covered.
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, front-loaded sentence that efficiently conveys purpose and output. No unnecessary words or repetition.
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?
Given the tool is a calculator with no output schema, the description adequately explains the return value (itemized fees, ranges, cash to close). However, the lack of any parameter guidance and no mention of underlying assumptions makes it slightly incomplete for an agent without further hints.
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% and all 7 parameters are undocumented in the description. The description mentions fee categories loosely matching the parameters but does not explain each parameter's meaning, units, or how to set them. This is a significant gap.
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 it estimates US mortgage closing costs before purchase, listing specific fee categories (origination, appraisal, title, prepaid) and outputs (typical ranges, cash to close). This distinguishes it from sibling tools like mortgage_piti_calculator or down_payment_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 'before you buy' but provides no explicit alternatives or exclusions. It does not mention when to prefer this over other mortgage-related calculators, leaving the agent to infer the context.
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.