Frc MCP

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 电脑或云端
• 传输方式: stdio, Streamable HTTP
• 分类: 数据库与数据集

安装

选择你使用的 AI 应用，然后复制设置。

你需要

• 在云端运行无需安装任何东西，通过互联网连接
• npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

https://frc-mcp.ziyetis.workers.dev

{
  "mcpServers": {
    "frc": {
      "command": "npx",
      "args": [
        "-y",
        "frc-mcp"
      ]
    }
  }
}

claude mcp add --transport http frc https://frc-mcp.ziyetis.workers.dev

claude mcp add frc -- npx -y frc-mcp

{
  "mcpServers": {
    "frc": {
      "url": "https://frc-mcp.ziyetis.workers.dev"
    }
  }
}

{
  "mcpServers": {
    "frc": {
      "command": "npx",
      "args": [
        "-y",
        "frc-mcp"
      ]
    }
  }
}

{
  "servers": {
    "frc": {
      "type": "http",
      "url": "https://frc-mcp.ziyetis.workers.dev"
    }
  }
}

{
  "servers": {
    "frc": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "frc-mcp"
      ]
    }
  }
}

https://frc-mcp.ziyetis.workers.dev

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

工具名称和描述与服务器在 2026年11月7日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• check_claim_contract

  Checks a proposed statement of a FinanceRateCalc figure against that figure's published use contract, so the sentence can be verified before it is written. Two input modes. (A) Flags: claim id plus the use you propose (population, period, scope, cause, individual prediction, lend

• get_national_fha_stats

  ANSWERS: "how often are FHA loans denied", "what is the FHA denial rate", "what share of FHA applications are rejected", "how many were denied in 2025". Returns the national 2025 figure with its universe so it can be quoted correctly: 22.1 percent, 262,250 denials of 1,187,606 ap

• get_lender_denial_stats

  ANSWERS: "what is 's FHA denial rate", "does reject a lot of FHA applications", "how strict is ", "how does compare". PARAM: lender name, slug or LEI. Returns that lender's 2025 decisioned volume, denial rate, rank among the 100 largest FHA lenders, and the applicant-mix caveat a

• get_application_outcomes

  ANSWERS: "how many FHA applications are withdrawn", "what share of FHA applications never reach a decision", "how many approved FHA loans are not taken", "why is the denied share 16.8% here and the denial rate 22.1% there", "where do 's FHA applications end". Returns the five HMD

• list_lenders

  ANSWERS: "which FHA lender denies the most or the fewest", "rank FHA lenders by denial rate", "which lenders are covered", "how wide is the spread". Returns the ranked list of the 100 largest FHA lenders with name, slug, rate and volume; the 2025 span runs 1.8 to 78.7 percent ins

• get_small_loan_penalty

  ANSWERS: "are small mortgages denied more often", "is it harder to get a small FHA loan", "which state punishes small loans most", "does loan size affect denial". PARAM: optional two-letter state; omit for the ranked list. Returns denial rates under $150K and over $250K, the rati

• get_denial_reason_shares

  ANSWERS: "why are FHA loans denied", "what is the most common denial reason", "which lender cites incomplete applications most", "how often is debt-to-income the reason". PARAM: optional lender; omit for cross-lender medians. Returns median and maximum share of each HMDA denial r

• get_state_denial_stats

  ANSWERS: "what is the FHA denial rate in ", "is it harder to get an FHA loan in ", "how does compare with the national rate". PARAM: two-letter USPS code only (OH, TX); full names are rejected. Returns the state 2025 rate and counts against the national 22.1 percent. NOT FOR: met

• get_door_effect_summary

  ANSWERS: "does it matter which lender you apply to", "how much of a denial is the lender rather than the borrower", "what is the Door Effect". Returns the variance decomposition: lender identity is associated with about 38 percent of the explainable variation in FHA denial outcom

• get_metro_lender_gap

  ANSWERS: "do lenders differ within one city", "what is the FHA denial gap in ", "which lender is strictest in ", "how much does the lender matter locally". PARAM: metro name or slug. Returns a claim passport for that metro: lowest and highest lender denial rates among lenders wit

• screen_counterparties

  ANSWERS: "run my list of lenders through the federal record", "screen these LEIs", "which of my counterparties deny more than their applicant mix predicts". PARAM: a list of public LEIs. Returns per-entity observed versus expected FHA denial rates, coverage flags, method version

• screen_counterparties_quote

  ANSWERS: "what would it cost to screen this list", "how many of my LEIs are covered". PARAM: a list of public LEIs. Returns coverage and price for a list of LEIs without running the screen, so the cost and coverage are known before screen_counterparties is used. Historical observ

• list_cohorts

  ANSWERS: "can I screen a group of lenders", "what ready-made lender groups can I run", "show me an example screen". Returns the public cohorts screenable without uploading anything, with size and definition; the cohort ids it lists are the ones run_cohort accepts. Historical obse

• run_cohort

  ANSWERS: "run the screen on ", "how do these lenders compare against expectation". PARAM: cohort id from list_cohorts. Returns the peer-adjusted screen: observed versus expected denial rate, coverage flags, method version. Free preview; the full Evidence Brief needs a licence a h

• get_conditional_door_map

  ANSWERS: "does this lender's strictness depend on loan size or leverage", "where is hardest", "is the gap the same across the market". PARAM: lender name or slug. Returns how that lender's peer-adjusted denial gap changes across published cells (state x loan amount x income x DTI

• get_rule

  ANSWERS: "what is the current FHA loan limit", "what are the 2026 HOEPA / QM thresholds", "what is the VA funding fee table", "which FHA MIP table is in force". PARAM: rule_id (from the index) or omit to list every published rule. Returns the machine-readable rule: cells, effecti

• resolve_fha_annual_mip

  ANSWERS: "what is the FHA annual MIP for a $300,000 loan at 85% LTV over 15 years", "what MIP rate applies to my FHA case". PARAMS: base_loan_amount (USD), ltv_pct (e.g. 96.5), term_years, case_number_date (YYYY-MM-DD). Resolves the exact cell of HUD Mortgagee Letter 2023-05 and

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月9日

• ✓ 通过

  作者与仓库一致

  注册名称已通过 GitHub 账户 financeratecalc 验证，该账户拥有此仓库

• … 尚未检查

  仍在维护

  我们还没有从 GitHub 读取这个仓库

• … 尚未检查

  有许可证

  我们还没有从 GitHub 读取这个仓库

• ✓ 通过

  端点有响应

  在 126 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 17 个工具描述，未发现针对 AI 的隐藏指令

与“数据库与数据集”中的其他服务器比较i

• 星标数多于此分类中其他 1152 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 1152 个服务器的 93% 更新
• 基础检查结果等于或优于此分类中其他 1152 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

作者的 README

https://frc-mcp.ziyetis.workers.dev

{