Skip to main content
Glama

Small Business Intelligence by Brick & Mortar

by io.github.2016judea

Market Opportunity Scan

market_opportunity_scan
Read-only

Gap analysis for a category x metro: detects underserved demand, oversaturation, and genuine whitespace using only public signals — for someone deciding whether/where to open, expand, or invest.

Example invocations:

  • "Is there whitespace for a new brewery taproom in the North Loop, Minneapolis?"

  • "Scan the nail salon market in Aurora, CO for underserved demand"

  • "Where in Wichita, KS is full-service restaurant demand outrunning supply?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesThe business category/vertical to scan for whitespace, e.g. 'coffee shop', 'massage spa'.
city_metroYesCity + state/region defining the market, e.g. 'Aurora, CO'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
noticeNoPresent ONLY when the request was denied by usage policy instead of executed. When present, every other field is an empty placeholder and must not be reported as a framework.
caveatsYes
subjectNo
frameworkYes
output_schemaYes
quality_rubricYes
research_procedureYes

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so no contradiction exists. The description adds meaningful behavioral context by stating the analysis uses 'only public signals' and describing the nature of the output (underserved demand, oversaturation, whitespace), which is useful beyond the annotation fields.

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 front-loaded with a single dense purpose sentence followed by compact, varied examples. There is no filler or repetition, and every sentence contributes to understanding what the tool does and how to invoke it.

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?

Given the full input schema, readOnly annotations, and presence of an output schema, the description covers the essential decision context, example usage, and analytical scope. Nothing critical is missing for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds value by framing the inputs as 'category x metro' and providing three concrete example invocations that demonstrate how to populate category and city_metro with realistic values.

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 states a specific verb ('detects') and resource ('category x metro') and clearly defines the tool's focus: underserved demand, oversaturation, and whitespace. The three example invocations make the purpose concrete and help distinguish it from sibling tools like competitor_landscape or pricing_benchmark.

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?

The description gives explicit context for when to use the tool ('for someone deciding whether/where to open, expand, or invest') and shows realistic user queries. It does not explicitly name alternatives or exclusion conditions, but the use case is clear enough that an agent can choose it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Small Business Intelligence by Brick & Mortar toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0
Disambiguation4/5

Most tools target clearly distinct tasks — teardowns, competitor mapping, pricing, reviews, visibility, and data lookup are all separable. The main ambiguity is that business_teardown is a comprehensive single-business tool that overlaps with local_visibility_audit, review_intelligence, and pricing_benchmark, and twin_cities_datasets vs twin_cities_records could be confused at first glance.

Naming Consistency4/5

Names are almost uniformly descriptive snake_case noun phrases like business_teardown, competitor_landscape, and market_opportunity_scan. The exceptions are compose_report and request_a_feature, which are verb-first, creating a minor but noticeable convention break.

Tool Count5/5

Twelve tools is well within the ideal range for a server with this scope. Each tool covers a meaningful part of the small-business investigation workflow, from research planning and data lookup to analysis, diligence, and report assembly.

Completeness4/5

The surface covers the full investigative lifecycle: data sourcing, market and competitor analysis, business teardown, pricing, reviews, local visibility, broker diligence, and client-ready reporting. Minor gaps exist around direct valuation/financial modeling and non-Twin-Cities dataset access, but data_source_atlas and request_a_feature help agents work around them.

Resources