GIS Data Conversion MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. Each tool handles a specific conversion between two formats (e.g., csv_to_geojson vs. geojson_to_csv) or a unique operation (coordinates_to_location), making it easy for an agent to select the correct tool without confusion.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, using snake_case uniformly. All names clearly indicate the source and target formats (e.g., geojson_to_kml, wkt_to_geojson), making them predictable and readable.
Tool Count5/5
With 9 tools, the count is well-scoped for a GIS data conversion server. Each tool earns its place by covering essential format conversions and a reverse geocoding operation, avoiding bloat while providing comprehensive coverage for the domain.
Completeness5/5
The tool set offers complete coverage for GIS data conversion, including bidirectional conversions between GeoJSON and other key formats (CSV, KML, TopoJSON, WKT), plus a reverse geocoding tool. There are no obvious gaps, ensuring agents can handle common workflows without dead ends.
Average 3/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
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- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool performs reverse geocoding but doesn't describe what 'location name' entails (e.g., address, city, country), potential accuracy issues, rate limits, or error handling. This leaves significant gaps in understanding the tool's 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 a single, efficient sentence that directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of geocoding (which can involve accuracy, data sources, and output formats), the description is incomplete. With no annotations and no output schema, it fails to explain what 'location name' means in the return value or any behavioral nuances, leaving the agent with insufficient context for reliable use.
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 both parameters (latitude and longitude) adequately. The description adds no additional meaning beyond what the schema provides, such as coordinate format (e.g., decimal degrees) or valid ranges, which aligns with the baseline score when schema coverage is high.
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's purpose as converting coordinates to a location name using reverse geocoding, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools, which are all format converters rather than geocoding tools, so it misses the opportunity to clarify this distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or context for usage, such as when reverse geocoding is appropriate compared to other geospatial operations available in the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the conversion action but lacks details on error handling, output format specifics, performance considerations, or any side effects. This is a significant gap for a tool with no annotation coverage.
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, efficient sentence with zero waste—it directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of data conversion and lack of annotations or output schema, the description is incomplete. It doesn't explain the GeoJSON output structure, error cases, or how it interacts with sibling tools, leaving gaps that could hinder effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters thoroughly. The description adds no additional meaning beyond implying geographic data conversion, which is covered by the schema's field descriptions. This meets the baseline for high schema coverage.
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's purpose with a specific verb ('Convert') and resources ('CSV with geographic data' to 'GeoJSON'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'geojson_to_csv' or 'kml_to_geojson', which handle related conversions but in opposite directions or between other formats.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'geojson_to_csv' for reverse conversions or 'wkt_to_geojson' for other geographic data formats, leaving the agent to infer usage context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the conversion action but lacks details on error handling, performance, output structure, or limitations (e.g., handling of complex geometries). For a data transformation tool with zero annotation coverage, this is a significant gap.
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, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of GeoJSON-to-CSV conversion (handling nested objects, geometry types, etc.), no annotations, and no output schema, the description is inadequate. It doesn't explain what the CSV output looks like (e.g., how geometries are represented), potential data loss, or usage constraints, leaving critical gaps for the agent.
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 both parameters ('geojson' and 'includeAllProperties') with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, resulting in the baseline score of 3.
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's function with a specific verb ('Convert') and resource ('GeoJSON to CSV format'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'csv_to_geojson' or 'geojson_to_kml' beyond the output format, which prevents a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'csv_to_geojson' (reverse conversion) and 'geojson_to_kml' (different output format), there's no indication of context, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'Convert' implies a transformation operation, but the description doesn't disclose any behavioral traits: no information about what the tool returns (KML string? file?), whether it validates input, performance characteristics, error conditions, or any side effects. For a format conversion tool with zero annotation coverage, this is inadequate.
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 perfectly concise at just 4 words: 'Convert GeoJSON to KML format'. Every word earns its place - it states the action, source format, and target format with zero waste. It's front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a format conversion tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (KML data structure? string? file path?), doesn't mention error handling for invalid GeoJSON, and provides no context about typical conversion scenarios. The 100% schema coverage helps with parameters, but the overall context is incomplete for practical use.
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 5 parameters thoroughly with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters, provide examples of valid GeoJSON objects, or clarify how properties map to KML elements beyond what the parameter descriptions already state.
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's purpose as 'Convert GeoJSON to KML format', which is a specific verb+resource action. It distinguishes from some siblings like 'geojson_to_csv' or 'geojson_to_topojson' by specifying the target format, but doesn't explicitly differentiate from all conversion tools in the family.
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. The description doesn't mention when KML format is preferred over other formats (like CSV, TopoJSON, or WKT) or when to use the reverse conversion tool 'kml_to_geojson'. There's no context about typical use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the conversion action but doesn't describe any behavioral traits such as error handling (e.g., invalid GeoJSON input), performance characteristics (e.g., speed or limitations), or output specifics (e.g., WKT version or precision). This leaves significant gaps for an agent to understand how the tool behaves beyond the basic function.
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, efficient sentence that directly states the tool's purpose without any wasted words. It's front-loaded with the core action ('Convert'), making it easy to parse quickly. Every part of the sentence earns its place by specifying the input and output formats.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a data conversion tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., WKT string format), potential errors, or how it relates to sibling tools. For a tool that transforms geospatial data, more context is needed to ensure reliable use by an agent.
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% description coverage, with the 'geojson' parameter documented as 'GeoJSON object to convert'. The description doesn't add any meaning beyond this, such as examples of valid GeoJSON structures or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 verb 'Convert' and the resource 'GeoJSON to Well-Known Text (WKT) format', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'geojson_to_csv' or 'geojson_to_kml', which perform similar format conversions but to different targets.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where WKT is preferred over other formats (e.g., CSV, KML) or when to choose sibling tools like 'wkt_to_geojson' for the reverse operation. There's no context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions conversion but doesn't describe what happens during processing (e.g., error handling, validation, performance limits) or the output format details. This is a significant gap for a tool with mutation-like behavior (format transformation).
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, efficient sentence with zero wasted words. It is front-loaded with the core purpose and appropriately sized for a straightforward conversion tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (format conversion with no annotations or output schema), the description is incomplete. It lacks details on output structure, error cases, or behavioral traits, leaving the agent with insufficient context for reliable use.
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% description coverage, with the single parameter 'kml' documented as 'KML content to convert'. The description adds no additional meaning beyond this, such as format requirements or examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Convert') and resources ('KML to GeoJSON format'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'geojson_to_kml' or 'csv_to_geojson' beyond the input format, which prevents a perfect score.
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?
The description provides no guidance on when to use this tool versus alternatives like 'csv_to_geojson' or 'wkt_to_geojson', nor does it mention prerequisites or exclusions. It only states what the tool does, leaving usage context entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden but only states the conversion action without behavioral details. It doesn't disclose error handling, performance implications, output structure, or any side effects, which is inadequate for a tool with nested objects and no output 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?
The description is a single, efficient sentence with zero waste, front-loading the core action ('Convert TopoJSON to GeoJSON format'). It's appropriately sized for a straightforward conversion tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (nested objects, no output schema) and lack of annotations, the description is incomplete. It doesn't explain the conversion process, output format, or potential issues, leaving gaps that could hinder correct tool invocation in a broader context.
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 fully documents both parameters. The description adds no additional meaning beyond implying conversion, which aligns with the schema but doesn't compensate for gaps. Baseline 3 is appropriate as the schema handles parameter semantics effectively.
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's purpose with a specific verb ('Convert') and resources ('TopoJSON to GeoJSON format'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'geojson_to_topojson' or 'kml_to_geojson' that perform related but inverse or alternative conversions, missing explicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'csv_to_geojson' and 'kml_to_geojson' available, there's no indication of context, prerequisites, or exclusions, leaving the agent to infer usage based on input format alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states the conversion action but doesn't describe error handling (e.g., invalid WKT input), performance characteristics, or output specifics beyond format. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves in practice.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('Convert') and clearly specifies input and output formats. Every word earns its place, making it highly concise and well-structured.
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?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic conversion purpose but lacks details on output structure, error cases, or differentiation from siblings. For a straightforward format conversion tool, it meets the minimum viable threshold but doesn't provide rich contextual information.
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 the single parameter 'wkt' fully documented in the schema. The description adds no additional parameter semantics beyond implying the input is a WKT string. Since the schema already covers the parameter adequately, the baseline score of 3 is appropriate—the description doesn't add value but doesn't need to compensate for gaps.
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's function: converting WKT to GeoJSON. It specifies both the input format (Well-Known Text) and output format (GeoJSON), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'geojson_to_wkt' or 'kml_to_geojson', which perform related but inverse or different format conversions.
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?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools for format conversions (e.g., csv_to_geojson, kml_to_geojson), there's no indication of when WKT is the appropriate input format or what distinguishes this from other conversion tools. Usage is implied by the name but not explicitly stated.
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?
No annotations are provided, so the description carries the full burden. It mentions the conversion behavior and a key trait (compactness with shared boundaries), which adds useful context. However, it lacks details on potential side effects, error handling, performance considerations, or output format specifics, leaving gaps in behavioral disclosure for a transformation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action and includes a parenthetical benefit. Every word earns its place with no redundancy or unnecessary elaboration, making it highly concise and well-structured.
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?
Given the tool's complexity (data format conversion with 3 parameters, no output schema, and no annotations), the description is minimally adequate. It covers the purpose and a key benefit but lacks details on output behavior, error cases, or usage scenarios. With no annotations or output schema, more context would improve completeness for safe agent invocation.
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 fully documents all three parameters. The description does not add any parameter-specific details beyond what the schema provides (e.g., it doesn't explain the implications of 'quantization' or 'objectName' in context). Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Convert GeoJSON to TopoJSON format') and distinguishes it from sibling tools like 'geojson_to_csv' or 'geojson_to_kml' by specifying the target format. It also adds value by mentioning the key benefit ('more compact with shared boundaries'), which helps differentiate it from the inverse tool 'topojson_to_geojson'.
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 context by mentioning the benefit of TopoJSON (compactness with shared boundaries), which suggests when this format might be preferred. However, it does not explicitly state when to use this tool versus alternatives like 'geojson_to_csv' or 'geojson_to_kml', nor does it provide exclusions or prerequisites for use.
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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