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lzinga

US Government Open Data MCP

naep_gap_variable_years

Read-only

Compare achievement gaps between demographic groups across years to determine if gaps are widening or narrowing. Select subject, grade, variable, and years.

Instructions

Compare how achievement gaps between demographic groups change over time. Example: Is the racial achievement gap in reading getting bigger or smaller since 2017? Returns innerdiff1 (group gap for focal year), innerdiff2 (group gap for target year), and the gap between them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subjectYesSubject: 'reading', 'math', 'science', etc. Aliases accepted.
gradeYesGrade: 4, 8, or 12.
variableYesNon-TOTAL variable with 2+ categories: 'SDRACE', 'GENDER', 'SLUNCH3'
yearsYes2+ years comma-separated: '2022,2019' or '2022,2017'
jurisdictionNo'NP' (default), or state/district code
Behavior3/5

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

Annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds that the tool returns specific metrics (innerdiff1, innerdiff2, gap between), which is useful but limited. No mention of rate limits, permissions, or edge cases; however, the annotations reduce the burden.

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?

The description is concise: two sentences plus a line about returns. It front-loads the main purpose and includes a helpful example. Every sentence adds value without redundancy.

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?

Given no output schema, the description explains the returned fields (innerdiff1, innerdiff2, gap between). It lacks full interpretation of the 'gap between gaps' metric but provides an example that aids understanding. Overall, it is fairly complete for a straightforward comparison 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 coverage is 100% with detailed descriptions for each parameter (e.g., subject, grade, variable, years, jurisdiction). The description does not add new meaning beyond the schema; the example illustrates usage but does not clarify any parameter semantics further.

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?

The description clearly states the tool compares achievement gaps across years, provides a concrete example, and explains the returned metrics (innerdiff1, innerdiff2, gap between them). This differentiates it from sibling tools like naep_compare_years (compares overall scores) and naep_gap_variable_jurisdiction (compares gaps across jurisdictions).

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

Usage Guidelines3/5

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

The description implies when to use the tool (when comparing gaps over time) through the example, but does not explicitly state when not to use it or suggest alternatives such as naep_compare_years for overall trends or naep_gap_variable_jurisdiction for cross-jurisdiction gap comparisons.

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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