mcp-helper-tools
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap. For example, decodeBase64, decodeHtml, and decodeUrl all handle different encoding formats, while geolocate and clearGeoCache target specific geolocation tasks. The descriptions make it easy to differentiate between tools like hashData and compareHashes.
Naming Consistency4/5Most tools follow a consistent verb_noun pattern (e.g., decodeBase64, generateQRCode, listTimezones). However, clearGeoCache uses a verb_adjective_noun structure, and geolocate is a single verb, which are minor deviations from the predominant pattern.
Tool Count5/5With 14 tools, the count is well-scoped for a utility server covering encoding, hashing, geolocation, and QR/UUID generation. Each tool serves a specific function without redundancy, making the set comprehensive yet manageable for agents.
Completeness5/5The tool set provides complete coverage for its utility domain, including encoding/decoding for Base64, HTML, and URL formats, hashing and comparison, timezone conversion and listing, QR code generation, UUID creation, and geolocation with cache management. No obvious gaps exist for these common helper tasks.
Average 3.1/5 across 14 of 14 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No issues in the last 6 months
- No commit activity data available
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Apache 2.0.
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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This repository includes a glama.json configuration file.
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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. While it mentions 'constant time' comparison (a key behavioral trait for security), it doesn't describe other important aspects: what happens if inputs aren't valid hashes, whether there are length/format constraints, error conditions, or what the return value indicates (e.g., boolean equality result). For a tool with zero annotation coverage, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (5 words) and front-loaded with the core functionality. Every word earns its place: 'Compare' (action), 'two hashes' (resource), 'in constant time' (key constraint). There's no wasted verbiage or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, no output schema, no annotations), the description is incomplete. While it mentions the constant-time behavior, it doesn't explain the return value (presumably a boolean indicating equality), error handling, or practical use cases. For a comparison tool that might be used in security contexts, more context about why constant-time matters would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters ('hash1', 'hash2') clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Compare two hashes in constant time'. It specifies the verb ('compare') and resource ('two hashes'), and adds the important constraint 'in constant time' which distinguishes it from naive string comparison. However, it doesn't explicitly differentiate from sibling tools like 'hashData', which might be used for generating hashes rather than comparing them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'hashData' (for generating hashes) or explain scenarios where constant-time comparison is necessary (e.g., security-sensitive contexts to prevent timing attacks). There's no indication of prerequisites, exclusions, or recommended contexts for usage.
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 full burden for behavioral disclosure. It mentions Luxon as the underlying library but doesn't disclose error handling, validation behavior, performance characteristics, or what happens with invalid timezone identifiers. This leaves significant gaps in understanding how the tool behaves in edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that directly states the tool's purpose. There's zero waste or redundancy, and it's front-loaded with the essential information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (converted date/time string? object?), doesn't mention error conditions, and provides no examples. The 100% schema coverage helps, but the description should do more to compensate for missing annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 no additional parameter semantics beyond what's in the schema - it doesn't explain parameter relationships, provide examples, or clarify usage patterns. 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: converting date/time between timezones using Luxon. It specifies the verb (convert) and resource (date/time between timezones), but doesn't explicitly differentiate from sibling tools like listTimezones, which serves a different but related function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 listTimezones for timezone lookup, or clarify scenarios where this conversion is needed versus other date/time manipulation tools that might exist elsewhere.
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 what the tool does but doesn't describe how it behaves: error handling for invalid input, output format (string vs binary), character encoding assumptions, or performance characteristics. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just three words, front-loading the core purpose with zero wasted text. Every word earns its place by directly communicating the tool's function without redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, 100% schema coverage) but lack of annotations and output schema, the description is incomplete. It doesn't explain what the decoded output looks like, error conditions, or encoding details. For a decoding operation, knowing the result format is critical, making this description insufficient despite the straightforward schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'input' documented as 'Data to decode'. The description adds no additional parameter semantics beyond what the schema provides. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even without parameter details in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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 ('decode') and resource ('Base64 input data'), making it immediately understandable. It distinguishes from siblings like 'encodeBase64' by specifying the opposite operation. However, it doesn't explicitly mention what the output is (decoded text/binary), 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 Guidelines2/5Does 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 when to choose decodeBase64 over decodeUrl or decodeHtml, nor does it specify prerequisites like ensuring the input is valid Base64. Without any usage context or exclusions, the agent must infer everything from the tool 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Encode' implies a transformation rather than a destructive operation, the description doesn't mention any behavioral traits such as error handling, performance characteristics, or what happens with invalid input. It lacks details about the output format or any constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single, clear sentence that directly states the tool's function. There is no wasted language or unnecessary elaboration, making it front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a Base64 string), how errors are handled, or any usage limitations. For a transformation tool, this leaves significant gaps in understanding its behavior and output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'input' documented as 'Data to encode'. The description adds no additional meaning beyond this, as it doesn't elaborate on what constitutes valid 'input data' (e.g., text, binary data) or any formatting requirements. This meets 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.
Purpose4/5Does 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 ('Encode') and resource ('input data'), and specifies the encoding format ('Base64'). However, it doesn't explicitly distinguish this tool from its sibling 'decodeBase64' beyond the obvious encoding vs. decoding distinction, which is why it doesn't reach 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 Guidelines2/5Does 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 'encodeHtml' or 'encodeUrl', nor does it mention any prerequisites or context for usage. It simply states what the tool does without indicating appropriate scenarios.
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 action ('Generate') but doesn't describe what the tool returns (e.g., image data, file, or display), any side effects, error handling, or performance considerations. 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., QR code as text, SVG, or base64 string), which is critical for a generation tool. The schema covers inputs well, but the overall context for the agent to use the tool effectively is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for all three parameters (data, errorCorrectionLevel, type). The description adds no additional parameter semantics beyond what the schema provides, such as examples or usage tips. 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate a QR code from input data' clearly states the verb ('Generate') and resource ('QR code'), making the purpose immediately understandable. It distinguishes from siblings like encodeBase64 or hashData by specifying QR code generation, though it doesn't explicitly differentiate from potential similar tools not present in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, use cases, or comparisons with sibling tools like encodeBase64 for other encoding needs. The agent must infer usage solely from the tool name and description.
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. It mentions the Node.js crypto module but doesn't disclose behavioral traits such as performance characteristics, error handling, or security implications (e.g., using weaker algorithms like md5). This leaves gaps in understanding the tool's operation beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is 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 is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error conditions, or practical usage scenarios. For a tool with 3 parameters and no structured behavioral hints, more context is needed to ensure proper agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 (algorithm, encoding, input) with enums and defaults. The description adds no additional meaning beyond what the schema provides, such as examples or edge cases, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Hash') and resource ('input data'), specifying the implementation method ('using Node.js crypto module'). It distinguishes from siblings like encodeBase64 or generateUUID by focusing on hashing. However, it doesn't explicitly differentiate from compareHashes, which might be a related sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like compareHashes or encodeBase64. The description lacks context about use cases, prerequisites, or exclusions, leaving the agent without direction on appropriate application.
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, the description carries full burden. It implies a destructive mutation ('clear'), but doesn't disclose behavioral traits like whether this requires permissions, what 'clear' entails (e.g., deletion vs reset), side effects on 'geolocate', or error handling. This is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a destructive operation with no annotations or output schema), the description is incomplete. It lacks details on behavior, outcomes, or error cases, leaving the agent under-informed. The conciseness doesn't compensate for these gaps in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100% (empty schema). The description doesn't need to add parameter details, so it meets the baseline of 4 for zero-parameter tools. No additional semantic value is required or provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Clear the geolocation cache' clearly states the action (clear) and target resource (geolocation cache). It distinguishes from siblings like 'geolocate' (which likely reads from the cache) by specifying a destructive operation. However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 prerequisites (e.g., after geolocation errors), exclusions, or related tools like 'geolocate' for cache reads. This leaves the agent with minimal context for decision-making.
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. It states the operation but doesn't disclose behavioral traits like error handling, performance characteristics, output format, or whether it handles partial/malformed input. 'Decode' implies a transformation, but no additional context about the process is given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function with zero wasted words. It's appropriately sized for a simple transformation tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema and no annotations, the description is minimally adequate but lacks completeness. It doesn't explain what the decoded output looks like, potential side effects, or error conditions. Given the simplicity, it meets basic needs but leaves gaps an agent might need to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'input' documented as 'Data to decode'. The description adds no additional meaning beyond this, such as examples of HTML-encoded data or format constraints. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'decode' and the resource 'HTML-encoded input data', making the purpose immediately understandable. It distinguishes from siblings like decodeBase64 and decodeUrl by specifying HTML encoding, but doesn't explicitly contrast with encodeHtml beyond the inverse operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like decodeUrl or decodeBase64. The description implies usage for HTML-encoded data but doesn't specify scenarios, prerequisites, or exclusions. The sibling tool list includes related encoding/decoding tools, but no comparative context is given.
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 full burden for behavioral disclosure. It states the operation ('Decode') but doesn't describe what happens with invalid input (e.g., malformed URL encoding), whether it's idempotent, or any 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose, making it easy to parse quickly. This is an excellent example of conciseness for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple decoding tool with one parameter and no output schema, the description covers the basic purpose adequately. However, it lacks context about error cases, output format (e.g., decoded string), and differentiation from similar tools, which would be helpful given the sibling tools performing related operations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'input' documented as 'Data to decode'. The description adds no additional semantic context beyond this, such as examples of URL-encoded strings or formatting requirements. The baseline score of 3 reflects adequate but minimal value added over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Decode') and resource ('URL-encoded input data'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'decodeBase64' or 'decodeHtml', which perform similar decoding operations on different encoding formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'decodeBase64' for base64 decoding or 'decodeHtml' for HTML entity decoding, nor does it specify scenarios where URL decoding is appropriate (e.g., handling encoded query parameters).
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. It states the action 'encode' but doesn't disclose behavioral traits like whether it's idempotent, what happens with invalid input (e.g., null or non-string), error handling, or performance characteristics. For a transformation tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and format, making it easy to parse. Every word earns its place by conveying essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally complete. It states what the tool does but lacks context on usage, behavior, or output. For a simple encoder, this might suffice, but it doesn't fully compensate for the absence of annotations or output schema, leaving the agent to guess about results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single parameter 'input' documented as 'Data to encode'. The description adds no additional meaning beyond this, such as examples of what constitutes valid input or encoding specifics (e.g., which characters are encoded). Baseline 3 is appropriate since the schema adequately covers the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'encode' and the resource 'input data' with the target format 'HTML-encoded format'. It distinguishes from siblings like encodeBase64 and encodeUrl by specifying HTML encoding, though it doesn't explicitly contrast them. The purpose is 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 Guidelines2/5Does 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 encodeBase64 or encodeUrl. It doesn't mention use cases (e.g., sanitizing user input for web display) or prerequisites. Without context, the agent must infer usage from the name and sibling tools 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 the full burden of behavioral disclosure. It states the tool encodes data but doesn't mention any behavioral traits such as error handling, performance characteristics, or side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it operates 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.
Conciseness5/5Is 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 unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given 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 but lacks depth. It covers the basic purpose but doesn't address usage context, behavioral details, or output expectations, leaving room for improvement in completeness for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'input' documented as 'Data to encode'. The description adds no additional meaning beyond this, as it only reiterates the encoding action without detailing parameter constraints or usage. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('encode') and resource ('input data') with the specific format ('URL-encoded format'). It distinguishes from siblings like encodeBase64 and encodeHtml by specifying the encoding type, though it doesn't explicitly contrast them. The purpose is unambiguous but lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 encodeBase64 or encodeHtml, nor does it mention any prerequisites or context for usage. It simply states what the tool does without indicating appropriate scenarios 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves information but doesn't cover aspects like rate limits, authentication needs, error handling, or data freshness. For a lookup tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Get geolocation information for an IP address or domain.' It's front-loaded with the core purpose, has zero waste, and is appropriately sized for a simple lookup tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, or output format. For a geolocation tool, more information on what data is returned (e.g., coordinates, city) would enhance completeness, but it's not essential given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'query' parameter documented as 'IP address or domain to lookup.' The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get geolocation information for an IP address or domain.' It specifies the action ('Get'), resource ('geolocation information'), and target ('IP address or domain'). However, it doesn't explicitly differentiate from sibling tools like 'listTimezones' or 'convertTimezone' that might involve location data, though those serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, exclusions (e.g., invalid inputs), or compare it to siblings like 'listTimezones' for broader location-related tasks. Usage is implied by the purpose but lacks explicit context.
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 states the tool lists timezones but doesn't mention any behavioral traits such as whether it's read-only, has rate limits, returns paginated results, or requires authentication. This leaves significant gaps in understanding how the tool behaves beyond its 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.
Conciseness5/5Is 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 and resource, making it easy to parse and understand immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits and usage context, which are important for a tool that might be used in timezone-related workflows with siblings like 'convertTimezone'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'region' parameter clearly documented as an optional filter. The description doesn't add any semantic details beyond what the schema provides, such as examples of region values or how filtering works. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all available IANA timezones'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'convertTimezone' or 'geolocate' which might also involve timezone operations, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 'convertTimezone' or 'geolocate'. It lacks context about typical use cases, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and description.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It specifies the randomness source ('crypto.randomUUID()'), which implies cryptographic quality and platform dependency. However, it doesn't mention performance characteristics, error conditions, or what the output format looks like (e.g., UUID version). The description adds some behavioral context but leaves gaps 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the essential purpose and implementation method with zero wasted words. It's appropriately sized for a simple, parameterless tool and is perfectly front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete for basic understanding. However, for a cryptographic tool with no annotations, it should ideally mention the output format (e.g., UUID string format) and any platform dependencies. The description covers the 'what' but could better address the 'what you get' aspect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, which is correct for this case. Baseline would be 4 for zero parameters, as there's nothing to compensate for.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate a random UUID') and the implementation method ('using crypto.randomUUID()'), which precisely distinguishes it from sibling tools like hashData or generateQRCode. It uses a concrete verb+resource combination that leaves no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the technical implementation detail ('crypto.randomUUID()'), suggesting it's for generating unique identifiers in cryptographic contexts. However, it doesn't explicitly state when to use this tool versus alternatives like hashData for different purposes, nor does it provide any exclusion criteria or prerequisites for usage.
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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