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Glama

Scout

by io.github.Flikah

scout.school_district

Look up public schools in a zip code via the National Center for Education Statistics (NCES) Common Core of Data. Returns up to 25 schools (elementary/middle/high) with grade level, enrollment, district, and a Department of Education NCES ID. Useful for buyer queries like 'is this a good school zone?'. Free, all 50 states.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipYes5-digit ZIP code. Required.
levelNoOptional filter: 'elementary' | 'middle' | 'high'.

TDQS

A4/5.0
Behavior4/5

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

With no annotations, description discloses key behaviors: returns up to 25 schools, free, covers all 50 states, uses NCES data. Missing rate limits or authorization details, but covers safety and scope well.

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 sentences, no filler, front-loaded with main action. Every sentence adds value.

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, but description explains return fields and data source. Covers limit, scope, and use case. Lacks return format details but suffices for agent invocation.

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 descriptions for both parameters. The description adds no extra meaning beyond what's in the schema, earning baseline 3.

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 looks up public schools in a zip code via NCES data, specifies return fields (grade level, enrollment, district, NCES ID), and contrasts with sibling tools that deal with agents, brokerages, etc.

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?

Provides a use case ('useful for buyer queries like is this a good school zone?') but lacks explicit guidance on when not to use or alternatives. Implies usage but no 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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from agent profiles and comparisons to market data and lead management. Overlaps like find_agent vs find_public_agent are explicitly differentiated by live vs directory status.

Naming Consistency4/5

All tools use snake_case and the 'scout.' prefix, but they mix verb_noun (e.g., compare_agents, search_listings) and noun_noun (e.g., agent_profile, coverage) patterns. However, the naming remains predictable and readable overall.

Tool Count5/5

With 18 tools, the server covers agent discovery, brokerage info, market data, listings, lead management, referrals, and licensing—well-scoped for a real estate assistant without redundancy.

Completeness5/5

The tool set covers all key workflows: agent and brokerage search, market insights, school data, listing search, lead generation and purchase, referral management, and license verification, with no obvious gaps for its domain.

Resources