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 62 days
Last Tested: 2026-10-19 18:44
Transport: Streamable HTTP · MCP 2025-06-18
URL

TDQS

A4/5.0

Scored 2026-10-19 18:25 across 3 tools

Disambiguation5/5

count_businesses, list_industries, and search_businesses have clearly distinct verbs and purposes: aggregate counting, reference listing, and sample retrieval. The descriptions explicitly differentiate their outputs and use cases, leaving no ambiguity.

Naming Consistency5/5

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

Tool Count4/5

Three tools is minimal but each earns its place for the server's narrow, contact-free scope. It could support one or two more (e.g., paginated search or multi-industry count), but the set is reasonably scoped.

Completeness3/5

The surface covers counting, industry discovery, and sampling, but lacks any way to retrieve contact details or detailed business records—a notable gap given the 'LeadQuasar' name implies lead generation, even though descriptions state contacts are intentionally excluded. Minor gaps like filtering by email/phone availability in search also exist.

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 9.5 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), Australia (AU), United Arab Emirates (AE), India (IN), Germany (DE), Spain (ES), France (FR), Italy (IT), Singapore (SG), Ireland (IE), Netherlands (NL), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), Mexico (MX), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG).
`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 'plumbers in the UK without a website' or 'trucking companies in the UK 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 cover read-only, idempotent and non-destructive safety, so the description's added value is the data-scope disclosure: 9.5M+ live records, mostly US, with much smaller non-US coverage. It also notes the response reports the resolved industry category. No mention of latency, rate limits or cost, so not a 5.

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?

Purpose and counted fields are front-loaded in the first sentence, usage examples follow, and every sentence carries information. No filler or redundant restatement of the title.

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?

There is no output schema, so the description must characterize returns; it names the counts and the email/phone split and the matched-category reporting, which covers the essentials. It does not state the concrete response shape (field names, whether a total is included), leaving a small gap.

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 schema already documents each parameter thoroughly (city, state, country ISO codes, industry wording normalization, no_website semantics). The description adds only the mild implication that filters combine with AND and reiterates the GB example; baseline 3 applies when the schema does the heavy lifting.

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 (count) plus resource (verified B2B businesses) and the exact dimension being counted (email/phone presence). The counting verb inherently separates it from search_businesses and list_industries without needing to name them.

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 three concrete example questions that map directly onto the filter combinations, making the intended use case unambiguous. It never says when to prefer search_businesses (to retrieve actual records) instead, or names alternatives, so it falls short of explicit exclusion guidance.

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), Australia (AU), United Arab Emirates (AE), India (IN), Germany (DE), Spain (ES), France (FR), Italy (IT), Singapore (SG), Ireland (IE), Netherlands (NL), Saudi Arabia (SA), Qatar (QA), Pakistan (PK), Mexico (MX), South Africa (ZA), Malaysia (MY), Israel (IL), Nigeria (NG).
`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 'plumbers in the UK without a website' or 'trucking companies in the UK with no website'.

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already cover readOnly, idempotent, non-destructive and closed-world behavior, so the bar is lower; the description nevertheless adds a genuinely important behavioral constraint by stating that contact details (name, company, email, phone, address) are never returned. It stops short of explaining sampling behavior (how rows are chosen, ordering, why a 'sample' rather than all matches), which is the remaining unknown for this tool.

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 front-loaded with the most important information: what is returned, then the critical exclusion of contact data. No filler or repetition of schema content.

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 no output schema, the description usefully characterizes the return shape (industry, location, data-quality signals) and explicitly rules out contact fields, which is exactly the negative disclosure an agent needs. It omits the sampling/ordering behavior and the fact that all six parameters are optional, so it is strong but not fully 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 description coverage is 100% and the schema already documents every parameter thoroughly (including valid countries and the no_website semantics). The description adds no parameter-level 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Return a sample of matching businesses') plus the facets it reports (industry, location, data-quality signals). The word 'sample' implicitly separates it from count_businesses, but the description never names or contrasts the siblings, so an agent must infer the distinction.

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

Usage Guidelines2/5

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

There is no explicit when-to-use statement and no mention of the alternatives count_businesses or list_industries, even though they are obvious substitutes for getting totals or enumerating industries. The agent must infer that this tool is for retrieving example rows rather than counts.

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