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MCP Server

Connect Claude Desktop or Claude.ai to Hemrock's financial modeling prompt library and concept guides. Context primers, task prompts, sanity checks, and the methodology behind the math, pulled automatically into every AI conversation about your model.

Free
Open, no auth, no limits
Connect in Claude.ai

Settings → Connectors → Add MCP Server

Endpoint URL›https://mcp.hemrock.com/mcp
Claude Desktop config›
{
  "mcpServers": {
    "hemrock": {
      "type": "url",
      "url": "https://mcp.hemrock.com/mcp",
      "name": "Hemrock"
    }
  }
}
Claude.ai connector›

Settings → Connectors → Add MCP Server, then paste:

https://mcp.hemrock.com/mcp
ChatGPT / OpenAI›

In ChatGPT, open Settings → Connectors → Add connector, choose MCP Server, then paste:

https://mcp.hemrock.com/mcp

OpenAI's connector docs

Or use the Skill (no connector)

What is MCP?

MCP stands for Model Context Protocol, an open standard that lets AI apps call external tools and data sources over a single, portable API. Instead of copy-pasting primers and cheat sheets every conversation, you connect once and point your AI client at an MCP server and its tools become available as native calls. Claude, Cursor, Open AI, and a growing list of others all speak MCP.

The Hemrock MCP is a public server listed in the MCP registry – no auth, no limits, free to connect.

What it does

Built to edit means using AI to customize a proven model instead of generating from scratch. For that to work, the AI needs to understand the model's structure, conventions, and guardrails before it starts making changes. Copy-pasting a primer every time is friction.

The MCP server exposes Hemrock's prompt library as tools any MCP-compatible AI client can call. Ask Claude to "use the Hemrock context primer for the cap table model" and it pulls the right orientation, the right format conventions, and the right warnings automatically.

It also exposes Hemrock's library of modeling concept guides — the reasoning behind the numbers. Where the template primers tell the AI where a calculation lives, the concept guides explain how it should work: how an exit waterfall distributes proceeds, how anti-dilution resets conversion prices, how a circular reference resolves. The AI can pull the relevant guide before it builds or audits the math.

Available tools

Six tools in two groups: the prompt library — context, task prompts, checks, best practices — and the concept guides, which browse and return the methodology behind the math.

get_context

Orient your AI before it touches a spreadsheet. Pulls universal modeling context plus any template-specific primer.

  • template_namestring, optional- One of standard, saas, ecommerce, unit_economics, cap_table, venture_fund, venture_fund_quarterly, fund_economics, venture_valuation, runway. Omit for universal context only.
get_prompts

Task-specific prompts for a given template.

  • template_namestring, required- One of standard, saas, ecommerce, unit_economics, cap_table, venture_fund, venture_fund_quarterly, fund_economics, venture_valuation, runway.
  • task_typestring, optional- One of orientation, revenue, expenses, fundraising, analysis, presentation. Omit to return all task types for the template.
get_checks

Sanity-check prompts to catch hallucinated formulas, hard-coded values, confused metrics, and other common AI mistakes.

  • template_namestring, required- One of standard, saas, ecommerce, unit_economics, cap_table, venture_fund, venture_fund_quarterly, fund_economics, venture_valuation, runway. Use "all" for universal checks only.
get_best_practices

Modeling best practices, organized by topic.

  • topicstring, optional- One of formatting, structure, inputs, assumptions, review, sharing. Omit to return all topics.
list_concepts

Browse Hemrock's financial-modeling concept guides — the methodology behind the math: exit waterfalls, anti-dilution, convertible notes, cohorts, circular references, unit economics, and more. Returns a compact index to pair with get_concept.

  • template_namestring, optional- One of standard, saas, ecommerce, unit_economics, cap_table, venture_fund, venture_fund_quarterly, fund_economics, venture_valuation, runway. Filters to concepts relevant to that template.
  • tagstring, optional- Filter to a topic tag, e.g. "cap tables". Omit to list everything.
get_concept

Read the full text of a single concept guide — how a calculation actually works, not just where it lives in the template. Pull it before building or auditing the math.

  • slugstring, required- A concept slug from list_concepts, e.g. "waterfall" or "anti-dilution".

Use cases

Four concrete scenarios where the MCP changes the quality of what Claude can do for you. Paste any prompt straight into Claude (Desktop or claude.ai) once the connector is added.

1. Set up a new financial model from scratch

A founder just opened the Standard template and wants Claude to walk them through configuring it for a B2B SaaS business with annual contracts.

“I just bought Hemrock's Standard template and I'm building a model for a B2B SaaS business with annual contracts. Use the Hemrock MCP to get the universal context plus the standard-template primer, then pull the orientation and revenue prompts and walk me through setting up Get Started and the Revenues tab.”

What the connector contributes: get_context(template_name="standard") + get_prompts(template_name="standard", task_type="orientation") + get_prompts(template_name="standard", task_type="revenue").

Value: Without the connector, Claude gives generic modeling advice that doesn't map to Hemrock's specific sheet structure. With it, Claude knows which cells exist, which assumptions drive them, and the order to configure them in.

2. Audit an AI-generated model for common errors

A VC analyst let Claude edit a portfolio model last week and wants a second pass to catch the typical mistakes AI makes in financial modeling.

“I've been working with Claude on this venture fund model for the past week. Use the Hemrock MCP to pull the sanity-check prompts for the venture_fund template, then run them against the model I've shared and flag anything that looks wrong.”

What the connector contributes: get_checks(template_name="venture_fund") returns the universal checks plus template-specific validation prompts – e.g. “does management fee step-down happen at the right vintage year,” “are distributions net of carry,” etc.

Value: Claude doesn't natively know where AI-generated financial models tend to break. The checks tool encodes Hemrock's institutional knowledge of those failure modes so Claude can find them.

3. Build a cap table for a SAFE-then-priced-round scenario

A founder has raised on SAFEs and is planning a priced Series A. They want Claude to model dilution correctly using Hemrock's cap table template.

“I've raised $2M on post-money SAFEs at $15M cap and I'm about to do a Series A at $40M pre. Use the Hemrock MCP to pull the cap_table context and the fundraising prompts, then help me set up the scenario so I can see founder dilution, option pool top-up impact, and SAFE conversion.”

What the connector contributes: get_context(template_name="cap_table") + get_prompts(template_name="cap_table", task_type="fundraising"), optionally with get_best_practices(topic="assumptions") for assumption-layering guidance.

Value: SAFE conversion math is a well-known place Claude gets wrong (pre vs post-money, option pool shuffle, most-favored-nation clauses). The cap_table prompts give Claude the exact input fields and conversion logic Hemrock's template uses, so the answer matches what the spreadsheet will actually compute.

4. Learn how a calculation works before trusting it

An operator is building an exit waterfall and wants to understand how proceeds actually flow — liquidation preferences, participation, conversion — not just which cells to fill in.

“I'm working in the cap table and exit waterfall tool. Use the Hemrock MCP to pull the concept guide on exit waterfalls, explain in plain terms how proceeds get distributed across preferred and common, then check whether participating preferred is handled correctly in the model I've shared.”

What the connector contributes: list_concepts(template_name="cap_table") to find the relevant guides, then get_concept(slug="waterfall") for the full methodology.

Value: Template primers tell the AI which cells exist; the concept guides tell it how the finance is supposed to work. With both, Claude can explain the math in plain terms and catch logic errors the sheet map alone would miss.

Why this matters

AI without context makes plausible-looking mistakes. Wrong cell references, hard-coded values where formulas should be, confused metrics, silent breakage. The context primer and prompts catch most of that before the AI ever touches a cell.

If you're editing a Hemrock model with AI, connect the MCP. If you're editing something else, the patterns still apply: orient first, prompt with guardrails, verify before trusting the output.

Troubleshooting

If Claude shows “couldn't reach MCP server” or the tool list is empty after adding the connector, remove the connector, fully restart the Claude app (not just close the window – quit it), then re-add it. The server is publicly available, no auth required, so connection failures are almost always a stale client-side state. If problems persist, confirm https://mcp.hemrock.com/mcp loads in a browser (you should see a JSON response, not an error page).

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