geographic-data

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.3.0
Disambiguation5/5
Every tool targets a distinct resource and action. The parallel families (demographics, mortgage, migration, employment) each have clear single-area, trend, and compare variants, so there is no ambiguity in purpose.
Naming Consistency5/5
All tool names follow a clean snake_case verb_noun pattern (lookup_zip_code, get_demographics, compare_employment, geocode_address). The names use consistent domain terminology, with only minor use of standard acronyms like FMR.
Tool Count3/5
23 tools is on the heavier side and falls into the 16-25 border historically considered 'heavy'. However, the breadth of geographic data covered (ZIP, demographic, housing, mortgage, migration, employment, geocoding) justifies each tool's existence.
Completeness5/5
The server provides comprehensive read-only coverage for its domain: single lookups, searches, trends, comparisons, and geocoding both directions. There are no obvious dead ends or missing operations needed for typical analytic workflows.
Average 4.1/5 across 23 of 23 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- Last stable release on April 1, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only and idempotent. The description adds useful context beyond annotations by detailing the response format (array sorted by year pair), specific fields included, AGI unit scaling, and data source, which helps the agent understand expected output and behavior.
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 three concise sentences, with the primary action stated first, followed by output details and units. Every sentence adds value with no redundancy or fluff.
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?
Without an output schema, the description adequately conveys the return structure: an array of yearly summaries with key fields, sorted by year pair, and AGI units. It is sufficient for a read-only data retrieval tool, though it could be more explicit about edge cases or exact breakdown composition.
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?
The input schema fully describes both parameters (level and fips) with high coverage. The description adds no additional parameter-specific meaning beyond what the schema already provides, so the baseline of 3 is appropriate.
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?
The description clearly states the tool retrieves migration summary data across all available year pairs for a county or state, specifying output fields and units. It is specific and informative, but it does not explicitly distinguish from sibling tools like get_migration_summary or get_migration_flows.
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?
No guidance is provided on when to use this tool versus alternatives such as get_migration_summary or get_migration_flows. The description does not mention scenarios, prerequisites, or exclusions, leaving the agent to infer usage from the name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds that it returns the 'full list of matching ZIP codes with details', which clarifies the return scope and behavior, but does not cover potential edge cases, result limits, or formatting.
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 two concise sentences, front-loaded with the action and scope, and wastes no words. It provides all necessary high-level information efficiently.
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 simple two-parameter search tool with good annotations and schema coverage, the description is nearly complete. It indicates the return type ('full list with details') and is sufficient for an agent to select and invoke it correctly, though it doesn't describe pagination or exact detail fields.
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% for both parameters, with clear descriptions for city and state. The description simply names 'city and state' without adding extra semantic detail beyond what the schema already provides, so the baseline of 3 is appropriate.
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?
The description uses a specific verb ('Search') and resource ('ZIP codes') with a clear scope ('all ZIP codes in a given city and state'). It distinguishes from siblings like lookup_zip_code (which likely returns a single ZIP) and find_zip_codes_in_radius (radius-based) by emphasizing the city/state input and 'full list' output, though it does not explicitly name alternatives.
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 when to use the tool (when you have a city and state and need all ZIP codes) but provides no explicit when-not-to-use or alternative tool references. It offers clear context but leaves exclusions unstated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that results include distance from the center. However, it says 'Find all' while the limit parameter (default 100, max 500) may truncate results, and it does not disclose whether the center ZIP itself is included or how units affect the distance values.
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 two sentences long, directly states the purpose and return format, and contains no filler or redundant information. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description does partially explain the return value ('Returns each nearby ZIP with its distance from the center'). However, it omits important behavior such as the limit truncation, whether the center ZIP appears in results, and how unit affects output. For a moderately complex 5-parameter tool, this is adequate 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% with clear descriptions for every parameter. The description only restates 'center ZIP code' and 'radius' implicitly, adding no new semantic information beyond what the schema already provides.
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 uses a specific verb ('Find') and names the exact resource ('all ZIP codes within a given radius of a center ZIP code'). This clearly distinguishes it from sibling tools like lookup_zip_code (single ZIP lookup) and calculate_zip_code_distance (distance between two specific ZIPs).
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 clearly implies when to use the tool: when you need all ZIP codes around a center point. It does not explicitly list alternatives or exclusions, but the purpose is specific enough to guide selection without confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds that the tool returns historical multi-year data for a single geographic area, which is useful context. But it does not disclose output format, pagination, or data availability details beyond that.
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 a single, front-loaded sentence that immediately states the action and resource. Every word contributes to understanding the tool's purpose, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 compensate for return-value expectations. It gives examples (population growth, income changes, housing trends) but does not enumerate the actual demographic variables returned or the response shape. This is a clear gap for a trend tool, though the invocation details are well covered by schema and annotations.
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%, and each parameter already has a clear description, including the area_type enum. The description only generically references multi-year ranges and does not add extra parameter-specific meaning, so it does not exceed the baseline set by 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 uses a specific verb ('Retrieve'), resource ('historical demographic data'), and scope ('across multiple years for a single geographic area'). This clearly distinguishes it from sibling tools like get_demographics (likely current snapshot) and compare_demographics (comparison across areas).
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 states it is useful for analysing population growth, income changes, or housing trends over time, giving clear context for when the tool is appropriate. However, it does not explicitly name alternative tools or state when not to use this tool, so it stops short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds output scope but does not disclose additional behavioral traits such as data source (ACS) or response format. It neither contradicts nor enriches beyond annotations, so a 3 is appropriate.
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 a single, tightly packed sentence that starts with the primary action and then enumerates both the response contents and accepted area types. No words are wasted, and the structure is easily scannable.
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 partially covers return value by listing categories, but it does not describe the response structure or edge cases (e.g., invalid area codes). Given the moderate complexity and abundant annotation coverage, it is nearly complete but could clarify what 'profile' means in terms of a single object or data layout.
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 each parameter is already explained. The description's list of area types mirrors the enum and adds little new meaning. It does provide context on what the output includes, but that does not enhance parameter understanding. Per rubric, baseline 3 is correct.
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 a 'full demographic profile' for a single geographic area, listing specific data categories (population, income, housing, education, employment, households) and valid area types. This distinguishes it from siblings like get_demographics_category or get_demographics_trend, which target narrower or comparative views.
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 implicitly communicates when to use this tool (when a complete profile for one area is needed) and contrasts with sibling tools by emphasizing 'full profile' and 'single geographic area'. It does not explicitly mention alternative tools or exclusions, but the context is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, and the description adds the WAC vs RAC data distinction, the workplace default, and the exact response content. No annotation contradiction exists, and the added behavioral detail exceeds what structured hints provide.
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 three sentences that front-load the purpose, then compactly list return contents and explain the critical perspective parameter. Every sentence adds necessary information with zero waste.
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 compensates by enumerating returned data categories and explaining the default perspective mode. The combination of annotations and schema covers safety, geography, and year semantics, leaving only minor gaps around response structure and multi-year behavior.
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?
All four parameters have schema descriptions (100% coverage), so the baseline is 3. The description re-explains the perspective parameter much like the schema does, adding only the WAC/RAC acronyms, and adds no new meaning for year, geoid, or area_type 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 opening sentence, 'Retrieve employment data for a single geographic area,' uses a specific verb and resource with explicit geographic scope. The return-value list (total jobs, NAICS industry mix, earnings distribution, age breakdown) makes the purpose concrete and distinguishes it from siblings like get_employment_trend or compare_employment.
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 never names alternatives or states when not to use this tool. The 'single geographic area' phrasing and the workplace/residence perspective explanation imply the intended usage context, but there is no explicit guidance for choosing this over get_employment_trend or compare_employment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable context by enumerating the return fields (origination count, denial rate, etc.) and citing the data source (CFPB/FFIEC HMDA). This gives the agent a clear picture of what the tool provides beyond the schema.
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: the first states the action and scope, the second lists outputs. No wasted words, front-loaded with the primary purpose. This is exemplary conciseness.
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 no output schema, the description explains return values in detail, listing six specific metrics. It covers the source and geographic scope. It does not discuss error cases or data coverage gaps, but for a read-only summary tool with good annotations, this is adequate.
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?
The input schema has 100% coverage, with descriptions for year, geoid, and area_type. The description adds no additional parameter semantics beyond saying 'census tract, county, or state,' which aligns with the area_type enum. Baseline 3 is appropriate because the schema carries the parameter documentation weight.
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's purpose: 'Retrieve the mortgage lending summary for a single geographic area (census tract, county, or state) from HMDA data.' It identifies the specific resource (mortgage lending summary), scope (single area), and data source. This differentiates it from sibling tools like get_mortgage_trends and compare_mortgage.
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 a single-area summary but does not explicitly specify when to use this tool over alternatives like mortgage trends or compare tools. It lacks explicit guidance such as 'for historical trends, use get_mortgage_trends.' The mention of 'single geographic area' is useful but not a complete usage guideline.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context: it returns top flow pairs with job counts and a 3-supersector industry breakdown, and explicitly notes the coarser granularity and tract-level unavailability, which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long, front-loaded with the core purpose, then elaborates on return content and limitations. It is efficient and every sentence carries useful information 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?
Given the moderate complexity and absence of an output schema, the description explains the return format (top flow pairs, job counts, industry breakdown), covers geographic and industry limitations, and clarifies what is not supported. This is sufficient for an agent to understand the tool's scope, though it could mention pagination or sorting behavior.
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?
The input schema has 100% parameter description coverage, so the schema already explains each parameter. The description reinforces the meaning of area_type and direction but does not add substantial new syntax or detail beyond the schema, placing it at the baseline of 3.
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's function with a specific verb ('Retrieve') and resource ('commute flow data'), and immediately distinguishes itself by mentioning origin-destination pairs, inbound/outbound directions, and the coarse industry breakdown. It also contrasts with the 20-sector NAICS detail in single-area employment summaries, effectively differentiating it from sibling 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?
The description provides clear usage context by specifying geographic scope (county or state), direction options, and that tract level is not supported. It also gives a comparative note about the industry breakdown versus single-area employment summaries, which helps agents decide when to use this tool instead of related ones, though it does not explicitly name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the safety profile is clear. The description adds value by enumerating the returned data categories (city, state, county, timezone, coordinates, etc.), which is especially important given no output schema exists. No behavioral traits beyond that are disclosed, but with annotations the bar is lower.
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?
Single sentence, front-loaded with the verb and resource, no filler. Every phrase contributes.
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?
Tool is simple with one parameter; annotations cover safety; the description lists the full scope of returned data in the absence of an output schema. This is sufficient for a low-complexity lookup tool.
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% (param 'zipCode' has its own description and pattern). The tool description repeats that it's a 5-digit US ZIP code, adding no new semantics 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 uses a specific verb ('Look up') and identifies the exact resource ('a single 5-digit US ZIP code'), listing the output fields. The word 'single' distinguishes it from sibling search/list tools that handle multiple ZIP codes.
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 via 'single' but provides no explicit guidance on when to choose this over sibling tools like search_zip_codes_by_city or find_zip_codes_in_radius. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already communicate the safe read-only, idempotent behavior. The description adds beyond that by disclosing the output structure ('each ZIP's demographic profile plus summary comparison ranges') and naming specific categories (population, income, housing). This is meaningful behavioral context not present in the schema or 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 concise, front-loaded sentences. The first sentence states the core action and constraints; the second describes the return value. No unnecessary words or repetition of schema details.
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's mention of the return structure ('demographic profile plus summary comparison ranges') is valuable. It also respects the input constraints and distinguishes from sibling tools. It could be more exhaustive (e.g., exact category filtering behavior, data_year defaults), but the combination of complete schema descriptions, annotations, and a clear summary makes it sufficiently complete for this read-only tool.
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 baseline is 3. The description reinforces the ZIP code count constraint and gives examples of categories, but it does not add deeper meaning for data_year or the categories enum beyond what the schema already provides. The mention of 'population, income, housing' partially overlaps with the categories enum but doesn't fully document parameter behavior.
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 uses a specific verb ('Compare demographics side by side') and clearly identifies the resource (ZIP codes) and scope (two to ten ZIP codes). It distinguishes itself from siblings like get_demographics or compare_employment by focusing on side-by-side demographic comparison across multiple ZIP codes.
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 side-by-side comparison of demographics across 2–10 ZIP codes. It does not explicitly name alternatives or exclusions, but the scenario is clear enough for an agent to infer the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: the area-type matching constraint and the specific return metrics (total jobs, industry mix, earnings, age breakdown). No contradictions found.
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 concise sentences: the first states the core purpose and scope, the second adds constraints and return summary. No fluff, front-loaded, every word earns its place.
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?
The description covers purpose, constraints, and high-level return content. Combined with rich annotations and full schema coverage, it provides enough context for correct selection and invocation. The absence of an output schema is mitigated by listing expected metrics.
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 the baseline is 3. The description adds minimal parameter-specific meaning beyond restating the geoid count (2-10) and area-type constraint already present in the schema. The return metrics mentioned are output-related, not parameter semantics.
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 compares employment data for multiple areas side by side, with a specific verb ('Compare') and resource ('employment data'). It distinguishes itself from sibling tools like get_employment (single area) and compare_demographics (different resource) by focusing on multi-area employment comparison.
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 provides clear usage context: use for comparing 2-10 areas and requires all geoids to share the same area type. It doesn't explicitly name alternatives or exclusion cases, but the use case is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. Description adds behavioral constraints (single year, same geographic level, side-by-side summaries with specific metrics) and source, going beyond the basic safety profile.
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?
Three concise sentences with all information front-loaded. The first sentence states the purpose, the second the constraint, the third the output and source. No filler 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?
No output schema exists, so description compensates by specifying the side-by-side summaries and included metrics. Covers purpose, constraints, output, and data source. Could mention data derivation or availability caveats, but overall adequate for a read-only comparison tool.
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 has 100% coverage and already explains area_type, geoids, and year. Description reinforces the 2-5 area count and same-level constraint, but adds no new parameter details beyond what the schema provides, so it sits at the baseline.
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?
Description uses specific verb 'Compare' with resource 'mortgage lending metrics' and scope '2-5 geographic areas' for a single HMDA year. It lists concrete metrics (origination count, denial rate, median loan amount) which distinguishes it from sibling tools like compare_demographics or get_mortgage_summary.
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?
Clearly implies use when comparing mortgage metrics across multiple areas in a single year, with the constraint that all areas be the same geographic level. Doesn't explicitly name alternatives or exclusions, but 'across 2-5 geographic areas' provides clear context compared to single-area mortgage tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: it discloses return payload details (coordinates, formatted address, address components, confidence score, provider name), the 'up to limit' behavior, and the fact that the country parameter biases rather than strictly filters results. This goes well beyond the readOnly and idempotent hints.
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 two sentences long, front-loaded with the core purpose, and every clause adds relevant information. No filler or redundancy exists.
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?
Despite having no output schema, the description sufficiently explains return values and key behaviors. It covers candidate results, output fields, and the country bias. Minor gaps include error handling or edge cases (e.g., no match), but these are not critical for a simple geocoding tool with good annotation coverage.
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 the schema already fully documents all three parameters. The description only restates the country parameter's bias behavior already present in the schema, adding no additional semantic meaning beyond what the input schema provides.
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 uses a specific verb ('Convert') and resource ('street address or place name into geographic coordinates'), making the tool's function immediately clear. It distinguishes itself from siblings like reverse_geocode (coordinates to address) and zip code tools by focusing on address-to-coordinate conversion.
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: when you have an address or place name and need coordinates, use this tool. However, it does not explicitly mention alternatives or exclusions, such as 'for reverse geocoding, use reverse_geocode instead,' leaving some ambiguity relative to its sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that the response is concise (because a subset is requested), but does not disclose additional behaviors such as response format, pagination, or rate limits. This is a modest addition beyond 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?
The description is two sentences long, front-loaded with the action and resource, and includes a clear usage directive. Every word earns its place, with no fluff or duplication.
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?
The tool is simple, with well-documented parameters and safety annotations. The description clearly states its purpose and when to use it relative to a sibling. While there is no output schema, the return value is straightforwardly tied to the requested categories. A small gap is that it does not mention how data_year omission behaves, though the schema 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% for all 4 parameters, each with clear explanatory text. The description's examples (population, income, housing) merely repeat enum values already documented in the schema. It adds no new parameter semantics 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 the tool retrieves demographic categories for a geographic area, with specific examples. It explicitly distinguishes itself from the sibling tool get_demographics by focusing on subsets of categories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: use this instead of get_demographics when a subset of categories is needed. This directly addresses when to choose this tool over an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate a safe read-only, idempotent, non-destructive operation, so the description does not need to repeat that. It adds valuable behavioral context: the sign convention for net returns (positive vs. negative), the proxy meaning of net exemptions, the unit for AGI (thousands of dollars), and the source (IRS Statistics of Income). This goes beyond the structured annotations and aids interpretation.
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 three sentences long, front-loading the primary purpose in the first sentence and then providing necessary interpretive details (metrics, sign, year format, source). Every sentence contributes meaningful information without redundancy or fluff, making it appropriately concise and well-structured.
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 summary tool with 3 parameters and no output schema, the description is fairly complete. It explains what is returned, the sign conventions, the year format, and the data source. It does not mention edge cases like missing data or pagination, but given the simple nature of a summary lookup and strong annotations, this is not a significant gap. Sibling differentiation is the only notable omission.
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 descriptions cover 100% of parameters, so the baseline is 3. The description adds important semantic detail, particularly for the 'year' parameter, explaining that it is a year pair representing tax filing year transitions (e.g., '2021-2022'). This is not present in the schema description and helps the agent format the parameter correctly.
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 a specific action ('Retrieve migration summary') with a defined scope ('for a county or state') and explicitly lists the returned metrics ('inflows, outflows, net migration', 'net returns', 'net exemptions', 'net AGI'). This effectively distinguishes it from sibling tools like get_migration_flows or get_migration_trends by focusing on the summary/aggregate nature.
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 provides clear context for when to use the tool (when a summary of migration at county/state level is needed) and explains the year-pair format. However, it does not explicitly discuss alternatives or exclusions, such as 'use get_migration_flows for detailed flow breakdowns' or 'use get_migration_trends for multi-year trends', leaving the agent to infer differentiation from the word 'summary' and sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds the trigram matching behavior, which is a useful algorithmic detail for predicting fuzzy-match responses. It does not describe return format or pagination, but with strong annotations the added behavioral context earns a solid score.
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, front-loaded with purpose, followed by a precise algorithmic note. No redundant wording; every phrase adds value. Excellent conciseness.
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 simple search tool with three parameters and no output schema, the description covers the core behavior (searches across boundary types, fuzzy matching) plus safety via annotations. It could mention return format, but the tool's purpose is clear enough for an agent to select and invoke it correctly.
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 baseline is 3. The description does not repeat parameter details but also adds no extra semantics beyond what the schema already provides. It aligns with the 'query' parameter (search by name), but the schema is sufficient.
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's purpose: searching for geographic areas by name across multiple boundary types (county, state, CBSA, census tract, ZIP). It uses a specific verb ('Search') and resource ('geographic areas'), and the boundary list distinguishes it from sibling zip-only 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?
The phrase 'across all boundary types' implies a general-use search tool, and 'fuzzy name search' indicates tolerance for typos. It does not explicitly mention when not to use it or name alternative tools, but the context is clear enough for an agent to infer it is the broad search option.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint, idempotentHint, and destructiveHint annotations, the description discloses that the tool supports Haversine or Vincenty formulas and that it returns origin/destination details and the computed distance. This adds meaningful behavioral context about calculation method and output contents.
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 two concise sentences, starting with the action verb and resource, and includes the key additional information (formulas and return contents) without unnecessary words.
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 tool's simplicity, full schema documentation, and strong annotations, the description provides sufficient context: it covers the core inputs, method variants, and the nature of the return value. No critical information is missing for an agent to select and invoke this tool correctly.
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?
The input schema already provides descriptions for all four parameters, covering 100% of the parameter semantics. The description does not add parameter-specific details beyond mentioning the formula option, so it does not exceed the baseline set by 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 a specific action ('Calculate the distance') on a specific resource ('two ZIP codes'), and mentions the two formula options. It distinguishes itself from sibling tools like lookup_zip_code and find_zip_codes_in_radius by focusing on direct distance calculation.
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 use case is clear: calculating distance between two ZIP codes. However, there is no explicit mention of when not to use it or explicit alternatives, such as find_zip_codes_in_radius for radius-based searches, which would make it a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare it read-only, idempotent, and non-destructive. The description adds valuable behavioral detail: returns an array ordered ascendingly by year, lists specific metrics, and clarifies the perspective parameter's default. This goes beyond the annotation-only baseline.
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, front-loaded with the main purpose, then concise detail on output structure and parameter usage. Every sentence earns its place with no 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?
For a read-only trend tool with no output schema, the description sufficiently covers what data is returned (array of yearly metrics with specific fields) and ordering. It also explains the perspective parameter. The tool's scope is clear, making it complete for agent selection.
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 all parameters thoroughly. The description adds little beyond the schema, mostly reinforcing the perspective default and 'single area' scope, which is already implied by 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 the tool retrieves employment data across all available years for a single area, with a specific verb and resource. It distinguishes itself from siblings like get_employment and compare_employment by emphasizing the time-series aspect and single-area scope.
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 conveys when to use this tool (for yearly trends of one area) and implies it is not for multi-area comparison or single-year snapshots. It does not explicitly name alternatives, but the context and sibling names provide sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare a safe read operation (readOnlyHint, destructiveHint false). The description adds useful behavioral context beyond annotations, such as the return structure (array of yearly FMR records) and the state-level limitation, which are not present in the 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?
The description is three concise sentences plus a source attribution. It is front-loaded with the main purpose, then returns output shape and a key limitation. Every sentence earns its place, with no redundant text.
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 read-only trend tool with no output schema and good annotations, the description covers the return format, supported geographies, the unsupported state case, and the data source. This is sufficient for an agent to select and invoke the tool effectively.
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 for parameters is 100%, so the schema already documents all four parameters. The description reinforces the area types and explains the state restriction, but does not add format examples or additional semantics beyond the schema. Baseline 3 is appropriate.
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 Fair Market Rent data across multiple years for a single area, with specific verbs and resource. It also distinguishes itself from sibling tools by listing supported area types (ZIP, county, CBSA) and explicitly noting state-level trends are not supported.
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?
Provides clear context for when to use the tool: for multi-year FMR trends on ZIP, county, or CBSA areas. It explicitly states when not to use (state-level) and explains why, but does not name alternative sibling tools like get_fair_market_rent for single-year queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive, and the description adds useful context by specifying the return format (array of yearly summaries with detailed metrics) and data provenance (CFPB/FFIEC HMDA). No contradictions 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?
The description is two sentences long, front-loads the verb and resource, and includes return content and source without any filler. Every sentence earns its 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 simple two-parameter read-only tool with rich schema coverage, the description is complete: it states purpose, scope, return structure, and data source. No output schema exists, but the listed yearly summary fields give adequate expectations.
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% with descriptive text for both parameters, including enum values for area_type and a clear format for geoid. The description adds no further parameter-specific details beyond referencing 'single area', so baseline 3 is appropriate.
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 multi-year mortgage lending data for a single area, listing specific metrics (origination count, denial rate, median loan amount, etc.) and citing the HMDA source. It distinguishes from sibling tools like get_mortgage_summary by emphasizing the multi-year trend aspect and single-area scope.
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 phrase 'multi-year' and 'across all available HMDA years' clearly implies use for historical trend analysis of mortgage lending for one geographic area. It does not explicitly mention alternatives or when-not-to-use, but the contextual scoping is clear enough for basic selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds behavioral context by stating it returns the 'best-match formatted address, address components, and coordinates,' and the phrase 'best-match' suggests approximate results. This goes beyond what annotations provide without contradicting them.
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 compact, consisting of two sentences that front-load the core action and follow with return details. Every word contributes to understanding, with no redundant information or filler.
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 simple tool with only two parameters and no output schema, the description is complete: it explains what the tool does, what inputs are expected (covered in schema), and what outputs are returned. The annotations provide safety and idempotency context, making this sufficient for an agent to select and invoke correctly.
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?
The schema fully describes both required parameters (latitude and longitude) with types and valid ranges, giving 100% coverage. The description merely refers to a coordinate pair without adding extra meaning beyond what the schema already provides. Therefore, a baseline score of 3 is appropriate.
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's primary function: converting a latitude/longitude coordinate pair into a street address. It also specifies the return values, distinguishing it from the sibling tool geocode_address, which performs the inverse operation. The verb 'convert' and resource 'latitude/longitude coordinate pair' are specific and unambiguous.
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 the intended usage (when you have coordinates and need an address) but does not explicitly mention alternatives or exclusions. For example, it does not state 'for address-to-coordinates, use geocode_address,' which would strengthen guidance. However, the context is clear enough for an agent to infer when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly and idempotent annotations, the description discloses that state-level queries return hundreds of records and that the output is monthly rent estimates by bedroom count. It also names the data source, adding useful context.
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 three sentences, each earning its place: purpose, caveat/guidance, and source. It is front-loaded with the primary action and resource, with no wasted words.
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?
Without an output schema, the description adequately covers the return type (monthly rent estimates by bedroom count) and the key operational caveat (state-level volume). Combined with the schema, it gives the agent sufficient context to use the tool correctly.
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?
The input schema fully describes all three parameters, including enum values and format notes, so the description adds no new parameter details. The mention of area types in the description only mirrors 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 looks up HUD Fair Market Rent rates for a geographic area, specifying the resource and verb. It distinguishes from sibling tools like get_fmr_trend by focusing on rates rather than trends, and mentions output by bedroom count.
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 provides explicit guidance on area type selection: 'Use ZIP or county lookups when only a single area is needed' and warns that state-level queries return hundreds of records. It doesn't name alternative tools, but the area-type guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only/idempotent. The description adds valuable behavioral context: potential IRS suppression for low returns, AGI amounts in thousands, and the array of flow records with fields. This exceeds what annotations provide.
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?
Four sentences, front-loaded with the core purpose. No filler—each sentence adds meaningful information (what, definitions, return structure, privacy caveat, source).
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 absence of an output schema, the description adequately specifies the return array contents (FIPS, area name, returns, exemptions, AGI) and data caveats (suppression). Combined with schema, it is fully complete for a data retrieval tool.
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 covers all 6 parameters with descriptions (100% coverage). The description adds meaning by detailing AGI units (thousands) and clarifying inflow/outflow semantics, which complements but does not duplicate schema info.
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 top inflow/outflow areas for a county or state, ranked by tax returns or AGI. It also defines inflow and outflow, which distinguishes it from sibling tools like get_migration_summary or get_migration_trends.
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?
Provides clear context for when to use (ranked migration areas by direction and level) but does not explicitly mention alternative tools or exclusion criteria. The focus on 'top areas' implies a specific use case, though contrast with siblings is absent.
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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