About Flat Rate Math
Flat Rate Math is an MCP server for comparing software-work payment models by total cost per delivered request. It helps clients weighing a fixed bid, a fully loaded hire, or a monthly subscription, with single-lane pricing and ranked comparisons.
Key features
- 01Price a lane per request
- 02Compare up to three lanes
- 03Rank lanes by unit cost
Use cases
- 01Compare a subscription with a fixed bid
- 02Estimate the cost of a monthly hire
MCP support
It exposes `cost_per_request` and `compare_lanes` as tools for MCP clients over stdio.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
flatratemath MCP server
A Model Context Protocol server that prices three ways of paying for software work on one number: total cost per delivered request. It is the stdio version of the flatratemath CLI, one Python file, standard library only, nothing to install.
Tools
cost_per_request prices a single lane. Give it a mode (bid for a
one-time fixed price, salary for a fully loaded monthly hire, flat for a
monthly subscription), the dollar rate, the months the work stays alive,
and the number of discrete requests you expect to submit.
compare_lanes takes up to three lanes and ranks them by cost per
request. An $84,000 fixed bid, an $18,290/month hire, and a $5,995/month
flat subscription over 12 months and 30 requests come back as:
flat sub: $71,940 total, $2,398 per request
fixed bid: $84,000 total, $2,800 per request
hire: $219,480 total, $7,316 per request
The flat lane in that example is a real one: the unlimited development subscription at Poket Dev. I kept redrawing the same spreadsheet for clients choosing between those lanes, so the math exists as a CLI and now as a server an agent can call.
Install
Clone the repo and point your MCP client at the file:
git clone https://github.com/scrapertweeter3-prog/mcp-flatratemath
Client command: python3 /path/to/mcp-flatratemath/server.py
The wire side is JSON-RPC 2.0 over stdio with Content-Length framing. Handshakes answered: initialize, ping, tools/list, tools/call.
License
MIT
Comments
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.