leadquasar

Server Details

Counts and firmographics for 7.5M+ verified US B2B businesses, by industry, state and city.

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Status: Healthy
Uptime: 99.8% over 42 days
Last Tested: 2026-09-29 13:40
Transport: Streamable HTTP · MCP 2025-06-18
URL

TDQS

A4.4/5.0

Scored 2026-09-28 23:30 across 3 tools

Disambiguation4/5

count_businesses and search_businesses both accept industry/location filters but return different outputs (counts vs. sample records), and descriptions clearly distinguish them; list_industries is a distinct helper. Minor overlap in filter parameters could cause brief hesitation, but overall purposes are clear.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern: count_businesses, list_industries, search_businesses. No deviations or mixed conventions.

Tool Count5/5

Three tools is well within the typical 3-15 range, and each serves a distinct, earned purpose: counting, industry discovery, and sampling. The set is lean but appropriately scoped for a read-only analytical interface.

Completeness4/5

The tools cover counting, industry listing, and sampling with data-quality signals, but notably omit any way to retrieve full business details or contact information (explicitly by design). For a lead database, this is a gap, though the server appears intentionally limited to market research rather than lead export.

Available Tools

3 tools

count_businessesCount businessesA

Read-onlyIdempotent

Inspect

Count verified B2B businesses in the LeadQuasar database matching an industry, country, US state and/or city, with how many have an email address and a phone number. Use this to answer questions like 'how many managed service providers are there in Texas', 'how many dentists in Chicago have a business email' or 'how many movers are there in London' (country GB). Returns real counts from 7.6 million+ live records, most of them US.

ParametersJSON Schema

Name	Required	Description
`city`	No	City name, e.g. "Chicago", or "London" with country GB.
`state`	No	US state, either full name ("Texas") or two-letter code ("TX"). US only.
`country`	No	Country, as an ISO code ("US", "GB", "CA") or a name ("United Kingdom"). Omit to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), United Arab Emirates (AE), Australia (AU), India (IN).
`industry`	No	Industry or business type in plain words, e.g. 'managed service provider', 'dentist', 'HVAC contractor', 'plumbers'. Everyday wording is resolved to the stored categories, so 'HVAC contractor' finds HVAC and 'plumbers' covers both Plumbers and Plumbing. The response reports which category it matched.
`no_website`	No	true = only businesses whose Google Maps listing shows no website of their own (read from the listing; a Facebook-only listing counts as none). For questions like 'restaurants in Dubai without a website' or 'plumbers in Texas with no website'.

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld=false, so the safety profile is covered. The description adds genuinely useful behavior beyond that: data source and freshness ('7.6 million+ live records'), a coverage caveat ('most of them US'), and that everyday industry wording is resolved to stored categories. It does not mention cost, rate limits, or exact response shape.

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 single core sentence followed by example phrasings and a short data-scale note; each sentence carries distinct value (capability, invocation examples, result trustworthiness). No filler or restatement of the title.

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 must convey what comes back, and it does: counts, plus how many records have an email and a phone. Coverage limitations and the category-resolution behavior are disclosed, and annotations handle safety, so nothing essential for correct invocation is missing.

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 all five parameters are already documented in the schema with examples and semantics (ISO codes, state name vs code, no_website Google-Maps interpretation). The description restates the matchable facets but adds no syntax or edge-case detail beyond the schema, so the 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?

Specific verb+resource+scope: 'Count verified B2B businesses in the LeadQuasar database matching an industry, country, US state and/or city,' plus the email/phone availability breakdown. The counting/aggregation nature is unambiguous, so an agent can tell it apart from the search-oriented sibling without opening either schema.

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?

Explicit usage context is supplied via three worked example questions ('how many managed service providers are there in Texas', etc.), which tightly maps to the count-question use case. It stops short of naming the alternative (search_businesses) or stating when NOT to use it, so it is clear context without exclusions.

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

list_industriesList industriesA

Read-onlyIdempotent

Inspect

List the industries LeadQuasar covers and how many verified businesses each holds. Use this first when unsure what industry wording will match.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive intent. The description adds useful behavioral context by specifying that it returns industry coverage and verified business counts, and positions it as a vocabulary-discovery step. It stops short of describing output shape, but the annotations lower that 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?

Two sentences with no unnecessary content. The first sentence states purpose and output content, and the second provides usage guidance. Both earn their place.

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?

For a zero-parameter read-only list tool, the description covers what it lists, how results are aggregated (per-industry verified business counts), and when to use it. No output schema is needed, and annotations handle the safety profile, so nothing essential is missing.

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?

The tool has zero parameters and the schema is effectively complete. The baseline for a parameterless tool is 4, and there is nothing for the description to add beyond what the schema already conveys.

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: 'List the industries LeadQuasar covers and how many verified businesses each holds.' The added detail about per-industry verified business counts clearly distinguishes it from business-counting or searching tools.

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?

Gives explicit, actionable guidance: 'Use this first when unsure what industry wording will match.' It clearly conveys when to call the tool, though it does not explicitly name alternatives or state when not to use it.

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

search_businessesSample matching businessesA

Read-onlyIdempotent

Inspect

Return a sample of matching businesses with industry, location and data-quality signals. Contact details (name, company, email, phone, address) are never returned by this tool — it shows what exists and how reachable it is, not who to call.

ParametersJSON Schema

Name	Required	Description
`city`	No	City name, e.g. "Chicago", or "London" with country GB.
`limit`	No	How many rows to return, 1-25. Defaults to 10.
`state`	No	US state, either full name ("Texas") or two-letter code ("TX"). US only.
`country`	No	Country, as an ISO code ("US", "GB", "CA") or a name ("United Kingdom"). Omit to count every country. Coverage outside the US is much smaller. Countries with data: United States (US), United Kingdom (GB), Canada (CA), United Arab Emirates (AE), Australia (AU), India (IN).
`industry`	No	Industry or business type in plain words. Everyday wording is resolved to the stored categories, and `matchedIndustry` in the response says which one.
`no_website`	No	true = only businesses whose Google Maps listing shows no website of their own (read from the listing; a Facebook-only listing counts as none). For questions like 'restaurants in Dubai without a website' or 'plumbers in Texas with no website'.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it states that contact details are never returned, explicitly framing this as a sampling tool that shows what exists and how reachable it is. This goes beyond the annotations by setting expectations about what data is excluded. It lacks details on why contact details are excluded (privacy? licensing?) but is otherwise transparent. No contradiction with annotations.

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?

Two sentences, both earning their place. The first sentence states the core function and output categories. The second sentence is a critical exclusion disclosure that prevents an agent from expecting contact info. No filler, no repetition of schema details, and the most important behavioral constraint (no contact details) is front-loaded in the second sentence.

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?

For a read-only sampling tool with six parameters (all optional), the description is largely complete. It explains what the tool returns (sample, industry, location, quality signals), what it excludes (contact details), and gives a worked example of the no_website parameter. It doesn't describe the response structure (no output schema exists), but the description mentions 'matchedIndustry' and 'data-quality signals' which hint at the response shape. For a tool with no output schema and no required parameters, the coverage is adequate. It could mention pagination or default limit, but the limit parameter documentation covers that.

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 the input schema already documents all six parameters thoroughly with examples. The description adds marginal value: the 'no_website' parameter's purpose is reiterated in the description with concrete examples, and the mention of 'data-quality signals' hints at what the response will contain. However, the description doesn't significantly enhance understanding beyond the schema's parameter docs – it mostly restates them. Baseline 3 is appropriate given full schema coverage.

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 ('Return'), a resource ('a sample of matching businesses'), and the key characteristics (industry, location, data-quality signals). It also clearly distinguishes itself by explicitly stating what it does NOT return (contact details), which differentiates it from siblings like count_businesses. The purpose is unmistakable.

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 implies when to use it: when you need a sample of matching businesses with quality signals, not contact details. It doesn't explicitly name sibling alternatives or conditions for when-not-to-use, but the contrast with 'Contact details... are never returned' clearly signals this tool is for reconnaissance, not outreach. The no_website parameter adds specific use cases. Clear context, though it could be more explicit about routing to count_businesses for totals.

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