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Education Data

by io.github.pipeworx-io
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Education Child Poverty

education_child_poverty
Read-onlyIdempotent

CHILD POVERTY by school district — Census SAIPE estimates of the number and percentage of school-age children (5-17) in poverty, which drive federal Title I funding. Keyless. Use for "child poverty rate in ", "poorest school districts in ", "how many students in poverty in ". Pass a state to rank districts by poverty rate, or a leaid for one district.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "state": "MS"
    },
    "name": "education_child_poverty"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNoEstimate year. Default 2020.
leaidNoSpecific district LEA id from education_find_districts.
limitNoMax districts to return (1-100, default 25).
stateNoUS state name or 2-letter abbreviation. Required unless leaid is given.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds meaningful context beyond them: it is keyless (no auth/API key required) and the data is modeled Census SAIPE estimates rather than live measurements, which matters for interpretation. Rate limits and freshness are not addressed.

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?

Front-loads the resource and metric, then usage triggers, then the state-vs-leaid routing. Three tight sentences with no filler or repetition of the schema.

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 carries the return-value burden and does state the returned metrics (count and percentage) and the ranking behavior. It does not spell out the response shape or the effect of the default year/limit, which keeps it just short of complete.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds real semantics: state triggers a ranking of districts by poverty rate while leaid returns a single district, and it points leaid at education_find_districts for lookup. That clarifies the state/leaid relationship more than the schema's terse constraint does.

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 metric (number and percentage of school-age children 5-17 in poverty), a specific geography (school district), and a named data source (Census SAIPE), plus the downstream relevance (Title I funding). This clearly separates it from siblings like education_district_finance or education_find_districts.

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 concrete triggering phrasings ("child poverty rate in <district>", "poorest school districts in <state>") and states the two usage modes: state to rank districts, leaid for a single district. It stops short of naming the alternative tool to use for related district data, so no explicit exclusions.

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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