A
licenseNot graded
qualityB
maintenanceINEGI DENUE MCP — Mexico's directory of economic units (~6M businesses).
163 npm
MIT
Census County Business Patterns
by com.olyport
Server Details
Establishment counts, employment, and payroll by geography and NAICS code from Census CBP
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- Status
- Healthy
- Uptime
- 100.0% over 43 days
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
A4.3/5.0
Scored across 3 tools
Disambiguation5/5
Each tool targets a distinct granularity: compare_county_industries focuses on a breakdown of all industries in a single county, get_county_business_patterns provides county-level data with optional industry filters (including totals for all industries), and get_state_business_summary offers state-level aggregates. Descriptions clearly differentiate their use cases.
Naming Consistency4/5
All tool names use a verb_noun pattern (compare_county_industries, get_county_business_patterns, get_state_business_summary). The only minor inconsistency is the use of 'compare' vs. 'get', but the pattern is otherwise uniform and readable.
Tool Count5/5
With only 3 tools, the set is focused and well-scoped for the Census County Business Patterns domain. Each tool provides essential functionality (state-level, county-level with filtering, and full industry breakdown), and no tool seems redundant or unnecessary.
Completeness5/5
The tools cover the core operations needed for business patterns data: state summary, county-level data with optional industry filtering, and a comprehensive industry breakdown for a county. The ability to retrieve all counties in a state via get_county_business_patterns adds flexibility, and the year parameter allows time-series access. No obvious gaps for the stated purpose of grant narratives.
Available Tools
3 toolscompare_county_industriesAInspect
Get a breakdown of all industries in a county.
Returns business pattern data for all 2-digit NAICS sectors in a
specific county. Useful for understanding the economic composition
of a community for grant narratives.
Args:
state: Two-letter state abbreviation (e.g. 'CA', 'TX', 'NY').
county_fips: 3-digit county FIPS code (e.g. '037' for Los Angeles County).
year: Data year (default 2021). Available: 2012-2021.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes | ||
| county_fips | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that data covers all 2-digit NAICS sectors and includes year range, but does not mention rate limits, auth requirements, or pagination. Adequate but not exhaustive.
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?
Description is concise (4 lines plus Args section), front-loaded with purpose, and structured in a clear 'what it does, what it returns, when to use' flow. The Args section is well-organized and adds value without redundancy.
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?
Output schema exists, so no need to document return values. Description covers purpose, usage context, and parameters thoroughly. Lacks mention of edge cases (e.g., large county performance) but sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description fully compensates. It clearly documents state format (two-letter abbreviation), county_fips format (3-digit code with example '037'), and year (default 2021, range 2012-2021). Every parameter is explained with concrete details.
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?
Description clearly states 'Get a breakdown of all industries in a county' and specifies returns business pattern data for all 2-digit NAICS sectors. It explains the purpose for grant narratives but does not explicitly differentiate from sibling tools like get_county_business_patterns or get_state_business_summary.
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?
The description implies usage for understanding economic composition and grant narratives, but does not provide explicit when-to-use or when-not-to-use guidance, nor contrasts with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_county_business_patternsAInspect
Get business establishment, employment, and payroll data by county.
Returns County Business Patterns data from the Census Bureau showing
the number of business establishments, employees, and annual payroll
for a given geography and optional industry filter.
Args:
state: Two-letter state abbreviation (e.g. 'CA', 'TX', 'NY').
county_fips: 3-digit county FIPS code (e.g. '037' for Los Angeles).
If omitted, returns data for all counties in the state.
naics_code: NAICS 2017 industry code to filter by (e.g. '72' for
Accommodation/Food, '62' for Healthcare, '23' for Construction).
If omitted, returns totals across all industries.
year: Data year (default 2021). Available: 2012-2021.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes | ||
| naics_code | No | ||
| county_fips | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It mentions the data source (Census Bureau) and default behaviors for optional parameters. However, it does not discuss error conditions, data freshness, or any limitations beyond year range. For a read-only data retrieval tool, this is adequate but not thorough.
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 concise (~100 words), well-structured with a summary followed by parameter descriptions. It front-loads the main purpose and avoids redundancy.
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 presence of an output schema (not shown) and thorough parameter documentation in the description, the description is complete enough for correct usage. It covers the return content, parameters, and defaults.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides detailed docstring for each parameter, including examples (e.g., 'CA', '037', '72') and explanations of defaults. This adds significant meaning beyond the raw schema.
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 retrieves business establishment, employment, and payroll data by county. It distinguishes from siblings (compare_county_industries and get_state_business_summary) which focus on comparison and state-level summaries respectively.
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 explains that the tool returns data for a given geography and optional industry filter. It provides examples and default behaviors for optional parameters. However, it does not explicitly state when to use this tool versus siblings or 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.
get_state_business_summaryAInspect
Get state-level business pattern summary.
Returns aggregate establishment, employment, and payroll data for an
entire state, optionally filtered by industry. Useful for state-level
economic overviews in grant applications.
Args:
state: Two-letter state abbreviation (e.g. 'CA', 'TX', 'NY').
naics_code: NAICS 2017 industry code to filter by (e.g. '72' for
Accommodation/Food, '62' for Healthcare). Omit for all industries.
year: Data year (default 2021). Available: 2012-2021.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes | ||
| naics_code | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It explains the tool returns data via 'Returns aggregate...' and details the optional filtering. It does not mention side effects or error conditions, but for a read-only query tool this is sufficient.
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 concise with two summary sentences and an Args section. Every sentence adds value; no fluff. Structure is clear and easy to read.
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?
Given the existence of an output schema (not provided) and the tool's simplicity, the description covers the return type and parameter logic. It is complete enough, though could mention potential missing data for invalid states.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates by explaining each parameter: state with examples, naics_code with industry code examples, year with default and range. This adds significant meaning beyond the schema.
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 retrieves state-level business pattern summaries with aggregate establishment, employment, and payroll data, optionally filtered by industry. This directly distinguishes it from sibling tools that operate at county level or compare counties.
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 mentions usefulness for 'state-level economic overviews in grant applications' and implies when to use (state aggregation). It does not explicitly state when not to use or provide alternatives, but the sibling tool names naturally differentiate usage levels.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- First observed
compare_county_industries - First observed
get_county_business_patterns - First observed
get_state_business_summary
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