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Education Data

by io.github.pipeworx-io
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Education Find Districts

education_find_districts
Read-onlyIdempotent

Find US PUBLIC SCHOOL DISTRICTS (local education agencies) by state, with optional name filter. Keyless federal data (NCES Common Core of Data). Returns each district's LEA id, name, county, city, number of schools, enrollment, and teacher counts. Use for "school districts in ", "how big is ", "how many students does serve". The returned leaid feeds education_district_finance and education_child_poverty.

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": "Illinois",
      "name_contains": "Chicago"
    },
    "name": "education_find_districts"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNoData year. Default 2020.
limitNoMax districts to return (1-100, default 25).
stateYesUS state name or 2-letter abbreviation. Required.
name_containsNoOptional substring match on district name, e.g. "Denver".

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 read-only, idempotent, non-destructive and open-world. The description adds value beyond them: the data provenance (keyless NCES Common Core of Data) and the exact return fields (leaid, name, county, city, school count, enrollment, teacher counts), which tells the agent what it can compute without a second call.

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-loaded with the capability and scope, then provenance, then returns, then routing — no sentence is filler. The inline query examples are compact and directly usable as triggering utterances.

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

Completeness5/5

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-shape burden and does so by enumerating the fields delivered. Combined with the downstream tool references, an agent has everything needed to select, call and chain this tool.

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 year, limit, state and name_contains are all already documented with ranges and defaults. The description only restates the state-scoping and optional name filter, adding no format or edge-case detail (e.g. ambiguous state strings) beyond the schema. 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 and resource — 'Find US PUBLIC SCHOOL DISTRICTS (local education agencies)' — and pins the scope to 'by state, with optional name filter.' The acronym parenthetical (LEA) and the distinction from education_find_schools make it unambiguous which sibling to pick.

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?

Provides concrete trigger phrases ('school districts in <state>', 'how big is <district>') and explicitly routes the returned leaid to education_district_finance and education_child_poverty, which is real chaining guidance. It stops short of naming education_find_schools as the alternative for when district-level is the wrong granularity.

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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