Pool Log — CDC-range water test log (no AI)

check_readings

Check a list of pool or spa water readings for things that cannot be true, and compare pH, free chlorine and bromine against the ranges published on the US CDC pages. Deterministic arithmetic only - no AI. It does not decide whether a pool is legally compliant, and it does not know your local code.

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": {
      "readings": "<readings>"
    },
    "name": "check_readings"
  }
}

Input Schema

Name	Required	Description	Default
`as_of`	No	Treat this date as "today" (YYYY-MM-DD) when rejecting future timestamps. Defaults to the server date.
`readings`	Yes	Readings to check. Each: {at:"YYYY-MM-DDTHH:MM", kind:"pool\|hot_tub\|splash_pad\|wading_pool\|other", ph?:number, free_chlorine_ppm?:number, total_chlorine_ppm?:number, bromine_ppm?:number, cyanuric_acid_ppm?:number, water_temp_f?:number, disinfectant?:"chlorine\|bromine\|none", method?:"dpd\|test_strip\|meter\|other", by?:"who tested"}.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 1, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses deterministic arithmetic, no AI involvement, and the explicit boundaries of what the tool will not judge. It still omits operational details such as whether problems are returned as a list, severity levels, or auth requirements.

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

Conciseness5/5

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

Three short sentences, each earning its place: capability, method, and limitations. The core purpose is front-loaded and nothing is redundant.

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?

For a checker with no output schema and no annotations, the description covers intent, method, and limits well. It would be stronger if it sketched the shape of the result (e.g., list of flagged readings/failed range checks), which is the one thing an agent cannot infer from structured fields.

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 description coverage is 100% – both parameters, including the nested reading shape and the as_of default, are fully documented in the schema. The description adds no parameter syntax or format detail beyond that, so the baseline 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 verb (check) and resource (pool/spa water readings) plus two distinct scopes: internal plausibility ('things that cannot be true') and comparison against US CDC pH/free chlorine/bromine ranges. This differentiates it cleanly from siblings like add_readings and get_rules.

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

Usage Guidelines4/5

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

Gives clear when-not guidance ('does not decide whether a pool is legally compliant', 'does not know your local code'), which steers the agent away from over-trusting the result. It does not explicitly route to a sibling (e.g., get_rules for jurisdiction-specific ranges), so it falls short of a 5.

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

All Pool Log — CDC-range water test log (no AI) tools Inspect

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