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Education Data

by io.github.pipeworx-io
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Education District Finance

education_district_finance
Read-onlyIdempotent

School district FUNDING AND SPENDING — revenue by source (federal / state / local) and expenditure by function, plus computed PER-PUPIL spending. Keyless federal data (NCES CCD school district finance survey, F-33). Use for "how much does spend per student", "school funding by district in ", "which districts get the most federal money", "local vs state share of school funding". Pass a state to rank districts, or a leaid (from education_find_districts) for one district. Note the finance survey lags the directory data by 1-2 years.

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": {
      "limit": 10,
      "state": "NY",
      "sort_by": "per_pupil_spending"
    },
    "name": "education_district_finance"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNoFiscal year. Default 2019 (the finance survey lags).
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.
sort_byNoRanking field, largest first. Default per_pupil_spending.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, openWorld, and non-destructive. The description adds genuinely useful context beyond that: it is keyless, uses a specific federal source, and lags the directory data by 1-2 years. It does not describe pagination or response shape, so it stops short of full transparency.

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?

Front-loads what the tool returns before usage notes, and every sentence carries information. The four quoted example queries are slightly repetitive but effectively anchor intent; overall dense rather than bloated.

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?

No output schema exists, so the description must convey return content, which it does by listing revenue sources, expenditure functions, and computed per-pupil spending. It covers the lag caveat and both access modes. Minor gaps remain around result ordering/pagination beyond the sort_by enum.

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%, so every parameter (year, leaid, limit, state, sort_by) is already documented in the schema. The description reinforces the state-vs-leaid relationship and provenance of leaid, but adds no syntax or semantics not already present. 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+resource (school district funding and spending data), enumerates the exact metrics returned (revenue by source, expenditure by function, per-pupil spending), and names the underlying dataset (NCES CCD F-33). This clearly distinguishes it from sibling education tools like education_find_districts and education_child_poverty.

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

Usage Guidelines5/5

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

Provides explicit example queries that map to real user intents ('how much does <district> spend per student', 'which districts get the most federal money'), and states the two invocation modes: pass a state to rank, or a leaid for one district. It also flags the data-lag caveat for when results may be stale.

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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