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by io.github.akiba-labs
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Replace paid SaaS with open source: savings and payback

saas_self_host_savings
Read-onlyIdempotent

Give monthly spend per paid tool (ids: mixpanel, semrush, freshbooks, calendly, chargebee, typeform, pipedrive, gohighlevel, intercom). Returns the open-source swap for each (repo, license, caveat), yearly savings after hosting, setup hours and cost, break-even months, and a SWITCH / MAYBE / KEEP verdict.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "spend": "<spend>"
    },
    "name": "saas_self_host_savings"
  }
}

Input Schema

NameRequiredDescriptionDefault
spendYesMap of tool id to monthly spend in dollars, e.g. {"mixpanel": 300, "intercom": 150}.
hourlyRateNoValue of an hour of setup time. Default 50.
hostingPerMonthNoEstimated monthly server cost to self-host. Default 20.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds real value beyond that by disclosing the return shape (repo, license, caveat, savings, break-even months, verdict tiers) and the hosting/rate assumptions behind the numbers, though it does not mention any input constraints on the spend map.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two dense sentences, front-loaded with the required input before the returned metrics. Every clause carries information, though the inline id enumeration makes it heavier than necessary when the schema already lists them.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description correctly compensates by naming the returned fields and verdict categories, and the parameter schema fully documents all three inputs. Only the missing sibling differentiation keeps it short of complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, including the enum of allowed tool ids and the defaults for hourlyRate (50) and hostingPerMonth (20). The description repeats the id list but adds no syntax or formatting meaning beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action (accept monthly spend per paid tool) and a concrete computed result (open-source swap, yearly savings, setup hours and cost, break-even, verdict). The enumerated tool ids and the SWITCH/MAYBE/KEEP output make the resource unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this versus alternatives such as list_open_source_saas_alternatives, which sits right next to it in the sibling set. The condition for choosing the savings/payback calculation over a plain alternatives listing is left entirely to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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