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Glama

Scout

by io.github.Flikah

scout.find_brokerage

Search the public Scout brokerage/office directory. Returns offices with public contact info (main phone, website, address) and volume stats where available. Covers SC + GA natively and partial NC/FL via the office import. Every result includes a claim_url — brokerages can claim their listing at scout.realestate/claim to curate their AI profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoLatitude for radius search.
lngNoLongitude for radius search.
mlsNoFilter to a specific MLS board (e.g. 'CCAR', 'TRIDENT', 'FMLS').
zipNo5-digit ZIP code.
cityNoCity name (e.g. 'Greenville', 'Myrtle Beach', 'Atlanta').
limitNoMax results (1-50, default 20).
stateNoTwo-letter state code (SC, GA, NC, FL).
radius_milesNoSearch radius from lat/lng (default 25, max 100).
min_total_volumeNoMinimum total sales volume in USD (filters to top-producing offices).

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions public nature, returned data, and the claim_url feature, but lacks details on rate limits, authentication, or data completeness. Moderate transparency.

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?

Extremely concise: two sentences that cover purpose, output, and a standout feature. Every word adds value, and the most important information is front-loaded.

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 9 parameters and no output schema, the description covers geographic scope, returned fields, and the claim_url feature. It could mention pagination or default ordering, but overall is fairly complete.

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%, so baseline is 3. The description adds minimal parameter-specific meaning; it does not explain parameter interactions or dependencies. Adequate but not enhanced.

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 it searches a public brokerage directory, lists the return fields (contact info, volume stats, claim_url), and specifies geographic coverage. This differentiates it from sibling tools like scout.find_agent which search for individual agents.

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?

Description implies usage for finding brokerages, but does not explicitly state when not to use it or mention alternatives. However, the sibling tool list provides sufficient context for differentiation.

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.

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