Gwinnett Index

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

With no annotations provided, the description carries the full burden of behavior disclosure. It lists the contents of the returned record (staff recommendation, decision, parcels, deep link), which is helpful, but it doesn't mention potential errors, behavior for missing case numbers, access permissions, or pagination. It provides a partial picture but not exhaustive behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the primary purpose and then lists the specific record contents. Every word contributes to understanding the tool's output, with no unnecessary padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequately complete for a simple retrieval tool: it names the key fields returned and the schema fully documents parameters. While it doesn't mention error handling or jurisdiction-related behavior, these are minor gaps given the tool's simplicity and full schema coverage. The absence of an output schema is somewhat compensated by the explicit list of returned contents.

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

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

The input schema already provides 100% coverage of parameter descriptions, including an example for case_number and the disambiguation purpose for jurisdiction. The tool description adds no extra meaning about parameters beyond what the schema already states, so the baseline score is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('get') and resource ('full record for a case number'). It distinguishes from siblings by focusing on retrieving a complete record by case number, while search_cases is for finding cases and get_code_section for code sections.

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

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

The description implies usage when you have a known case number and want the full record, but it does not explicitly contrast with alternatives like search_cases or mention when not to use it. No exclusions or alternative tools are named, so the guidance is more implied than explicit.

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

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

With no annotations, the description carries the transparency burden. It discloses a useful behavioral trait: unverified tables are flagged in the response. However, it doesn't mention error handling, required permissions, or whether the operation is read-only, leaving some uncertainty.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose, and then provides concrete examples plus a transparency note. Every sentence earns its place with zero fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a get-style tool with no output schema or annotations, the description adequately explains the return content (ordinance text/table, dates, verification status) and example inputs. It doesn't cover edge cases like invalid citations or jurisdiction issues, but given the simplicity, it's reasonably complete.

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

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

Schema coverage is 100%, but the description adds valuable meaning by showing accepted citation formats (e.g., '402.03', '§ 402.03', 'Duluth UDC § 402.03') and indicating the response contains adoption/amendment dates. This goes beyond the plain schema examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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

The description clearly states the tool retrieves ordinance text or dimensional-standards tables by citation, with adoption and amendment dates. It distinguishes itself from sibling tools like get_case and search_cases by focusing specifically on code sections and tables.

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

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

The description provides clear context on what the tool does and gives concrete citation formats, implying when to use it. However, it doesn't explicitly mention alternatives or exclusions, such as 'use search_cases for case law instead', though the sibling names offer some context.

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

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

With no annotations, the description carries the full burden. It discloses the exact data set covered (all 19 jurisdictions, enumerated) and mentions that each includes codes and boundary sources, giving a clear picture of what the tool returns. It does not explicitly state that it is read-only, but its list semantics imply no side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that immediately states the main point ('All 19 jurisdictions indexed') and then provides useful detail. Every word contributes to understanding the tool's scope, with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless list tool with no output schema, the description fully explains what is returned and the composition of the result set (container vs. jurisdiction, municipalities). It is complete enough for an agent to select and invoke the tool correctly.

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

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

The tool has zero parameters, so the baseline is 4. The description does not need to add parameter details, and none are missing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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

The description clearly identifies the tool as a listing operation for jurisdictions, specifying the exact scope: all 19 jurisdictions, including Gwinnett County as a container, unincorporated Gwinnett, and 17 municipalities. This distinguishes it from sibling tools like resolve_jurisdiction or search_cases by making it the canonical enumeration endpoint.

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

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

The description implies usage for obtaining a complete list of jurisdictions with their codes and boundary sources, but it does not explicitly contrast with alternatives. However, its scope statement is clear enough that an agent would understand when to select this tool over the siblings.

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

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

With no annotations, the description carries the full burden. It discloses the return values (jurisdiction, code citation, confidence band, distance) and the 'always call first' behavior. It doesn't mention potential errors or side-effect safety, but the tone implies a read-only query, and the output details provide substantial transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no wasted words. Front-loaded with the critical 'ALWAYS CALL THIS FIRST' directive, followed by precise output details and a high-value caveat. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description explains key return fields and the core caveat. It could explicitly state that pin and address are alternative identifiers, but this is implied by 'any Gwinnett County address or parcel'. Overall, quite complete for its complexity.

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

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 meaningful context beyond the schema, particularly the warning that a mailing address does not determine jurisdiction, which directly informs how to interpret the 'address' parameter. It also implicitly ties 'confidence band' to 'band_m'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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

The description clearly states the tool's function: resolving governing jurisdiction for Gwinnett County addresses or parcels. It specifies the output (one of 17 cities or unincorporated county, code citation, confidence band, distance to boundary) and distinguishes itself from siblings by declaring 'ALWAYS CALL FIRST' as a prerequisite.

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

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

Provides explicit usage guidance: 'ALWAYS CALL THIS FIRST' for any Gwinnett County address or parcel, establishing its role relative to sibling tools. Adds a crucial caveat about mailing addresses not being equivalent to jurisdiction, which helps agents avoid common errors.

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

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

With no annotations, the description adds valuable behavioral context: the case count, date range (1970–2026), and the jurisdictional limitation. It does not describe pagination or response format, but for a non-destructive search tool the main operational constraints are disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the primary purpose, and packs in all essential scope details without repeating schema information or adding filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with seven optional parameters and no output schema, the description gives a solid overview of what can be searched and the data boundary. It could be more explicit about return shape or filter combination behavior, but it is sufficiently complete for a read-only search tool.

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

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

Schema coverage is 71% and the description compensates by naming concrete search fields ('applicant, use, address, case number, type, year range or zoning district'), which also gives meaning to the undocumented year_from/year_to parameters. The jurisdiction limitation clarifies the jurisdiction parameter's intent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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

The description uses a specific verb ('Search') and a resource ('11,848 zoning and land-use cases'), then enumerates the main search dimensions. It clearly distinguishes search_cases from the sibling get_case, which is for retrieving individual cases.

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

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

The description gives clear context about the tool's scope, including useful filters and the critical limitation that 'County records cover UNINCORPORATED Gwinnett only.' It does not explicitly name alternatives or contrast with get_case, but it provides enough boundary for when this search tool applies.

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