Sys8 MCP Server

Server Quality Checklist

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Latest release: v0.4.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap; for example, analyze_language focuses on text language analysis while analyze_logs targets error detection in logs, and format_json handles JSON operations separate from format_text_case for text casing. The descriptions precisely differentiate their functions, eliminating any ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., analyze_language, convert_color, generate_password) using snake_case throughout. This uniformity makes the set predictable and easy to navigate, with no deviations in naming conventions.
Tool Count4/5
With 20 tools, the count is slightly high for a utility server but reasonable given the broad scope covering text analysis, encoding, formatting, and system info. Each tool serves a specific, non-redundant function, though it borders on being heavy compared to more focused servers.
Completeness4/5
The toolset comprehensively covers common utility operations like encoding/decoding, formatting, and validation, with no obvious gaps for its domain. Minor omissions might include more advanced data transformations or system monitoring tools, but core workflows are well-supported.
Average 3.1/5 across 20 of 20 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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 what the tool does but doesn't describe how it behaves: it doesn't specify the output format (e.g., percentages, counts), whether it handles mixed-language text, error conditions, or performance characteristics. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Analyze text for language distribution and character types') followed by a specific list. There's no wasted verbiage, though it could be slightly more structured (e.g., separating languages from character categories).
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 language analysis, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a breakdown by language, character counts), how it handles ambiguous cases, or any limitations (e.g., supported encodings). This leaves significant gaps for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'text' parameter clearly documented. The description adds marginal value by implying the text should contain content relevant to the listed languages and character types, but doesn't provide additional syntax, format, or constraints beyond what the schema already states. 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: 'Analyze text for language distribution and character types' followed by a specific list of languages and character categories. It uses a specific verb ('analyze') and resource ('text'), though it doesn't explicitly differentiate from sibling tools like 'format_text_case' or 'validate_data' which might also process text.
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 when this analysis is appropriate (e.g., for multilingual content, character encoding checks) or when other tools like 'format_text_case' or 'validate_data' might be better suited. No exclusions or prerequisites are stated.
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 analyzes text for errors and warnings but doesn't describe what the analysis entails (e.g., pattern matching, severity levels, output format), whether it's read-only or has side effects, or any limitations (e.g., performance, supported log formats). 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 front-loads the core purpose ('Analyze text for errors and warnings in logs') and includes helpful examples in parentheses. There's no wasted verbiage, making it easy to parse and understand 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 (analyzing logs for errors/warnings), lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the analysis returns, how errors/warnings are identified, or any behavioral traits. This leaves the agent with inadequate information to use the tool effectively beyond basic 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?
The schema description coverage is 100%, with the single parameter 'text' well-documented in the schema. The description adds minimal value beyond the schema by implying the text should contain logs, but doesn't provide additional semantics like format requirements or examples. 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: 'Analyze text for errors and warnings in logs' with specific examples of log types (compilation, npm, Docker, runtime). It uses a specific verb ('analyze') and resource ('text for errors and warnings in logs'), but doesn't explicitly distinguish from sibling tools like 'analyze_language' or 'validate_data', which might have overlapping domains.
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 mentions log types but doesn't specify use cases, prerequisites, or exclusions. With sibling tools like 'analyze_language' and 'validate_data' present, there's no indication of how this tool differs or when it's the appropriate choice.
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. 'Safely' hints at error handling but doesn't specify what safety means (e.g., sandboxed evaluation, input validation, error types). No information about performance, rate limits, or output format is provided.
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's appropriately sized for a simple tool and gets straight to the point without unnecessary elaboration.
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?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what mathematical operations are supported, how errors are handled, what the return format looks like, or any limitations. The 'safely' hint is too vague to provide meaningful 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 already documents the single 'expression' parameter with examples. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 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 ('calculate') and resource ('mathematical expression'), and the 'safely' qualifier adds useful context. However, it doesn't distinguish this tool from potential mathematical siblings (though none exist in the provided 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 Guidelines2/5
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. While there are no obvious mathematical siblings in the list, there's no mention of what types of mathematical expressions are supported, limitations, or when other tools might be more appropriate.
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 but offers minimal information. It states the conversion action but doesn't cover error handling (e.g., invalid timezone names), performance aspects, or output specifics like default formats. This leaves significant gaps for an agent to understand tool 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 extremely concise with a single, clear sentence that front-loads the core purpose without any wasted words. It efficiently communicates the essential function, making it easy for an agent to parse and understand 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 lack of annotations and output schema, the description is incomplete for a tool with four parameters and no structured behavioral hints. It doesn't explain return values, error cases, or usage constraints, leaving the agent with insufficient context to reliably invoke the tool beyond basic parameter passing.
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 four parameters. The description adds no additional parameter semantics beyond implying datetime and timezone conversion, which is already covered by the schema. This meets the baseline score of 3 for high schema coverage without extra value from the description.
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 resource ('datetime between timezones'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_current_datetime' or 'format_json', which could also handle datetime operations in different contexts.
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 'get_current_datetime' for current time operations or 'format_json' for datetime formatting, nor does it specify prerequisites or exclusions for timezone conversion 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 ('decode') but lacks details on error handling (e.g., invalid Base64 input), performance characteristics, or output behavior (e.g., what happens with binary data). This is a significant gap for a tool with potential edge cases in decoding.
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 extremely concise ('Decode Base64 string')—just three words—with zero wasted language. It is front-loaded and directly communicates the core function without unnecessary elaboration, making it efficient for quick understanding.
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 decoding operations (which can involve errors or varied outputs) and the lack of annotations and output schema, the description is incomplete. It doesn't cover what the tool returns (e.g., decoded string, error messages) or behavioral aspects like input validation, leaving gaps that could hinder correct usage by an AI 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 schema description coverage is 100%, with clear descriptions for both parameters ('input' and 'encoding'), including an enum for 'encoding'. The description adds no additional parameter semantics beyond what the schema provides, such as examples of valid Base64 strings or guidance on choosing encodings. Baseline 3 is appropriate given 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 'Decode Base64 string' clearly states the verb ('decode') and resource ('Base64 string'), making the purpose immediately understandable. It distinguishes from sibling tools like 'encode_base64' by specifying the opposite operation, though it doesn't explicitly mention the output format or contrast with other decoding tools like 'decode_url'.
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 Base64 decoding is appropriate (e.g., handling encoded data from APIs or files) or when to choose other tools like 'decode_url' for URL decoding or 'analyze_logs' for log analysis involving encoded content.
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 what the tool does but doesn't describe important behavioral aspects like what encoding standard is used (e.g., percent-encoding), whether it handles Unicode characters, what happens with invalid input, or what the output format looks like. For a transformation tool with zero annotation coverage, this is insufficient.
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 extremely concise with just one parenthetical phrase adding clarification. Every word earns its place, and it's front-loaded with the core purpose. There's zero waste or redundancy in this minimal description.
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 transformation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the encoded output looks like, what encoding standard is used, or provide any examples. For a tool that transforms data, users need to understand the output format, which isn't addressed.
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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions URL encoding generally but doesn't explain the 'component' parameter's significance or provide examples of when to use different component types.
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 ('encode') and resource ('string for URL'), specifying URL encoding as the operation. It distinguishes from sibling tools like encode_base64 by specifying URL encoding, but doesn't explicitly differentiate from decode_url beyond the obvious encode/decode 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?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention when URL encoding is needed, what scenarios require it, or how it differs from other encoding tools like encode_base64. There's no context about appropriate use cases.
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 lists actions but doesn't disclose behavioral traits like error handling (e.g., what happens with invalid JSON), performance characteristics, side effects, or output format details. The description is functional but lacks operational context needed for informed tool selection.
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 extremely concise with zero wasted words - a four-word phrase listing all key actions. It's front-loaded with all essential information and every word earns its place by specifying distinct functionalities. This is a model of efficiency for a multi-action utility 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?
For a tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'format' versus 'prettify' means, how validation results are communicated, what minified output looks like, or error conditions. The agent lacks crucial context about tool behavior and outputs despite the complete parameter schema.
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%, providing complete parameter documentation. The description adds minimal value beyond the schema, only implying that 'input' is JSON and 'action' defines the operation type. It doesn't explain parameter interactions (e.g., 'indent' relevance to 'minify' vs 'prettify') or provide usage examples, meeting 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 specific verbs (format, validate, minify, prettify) and resource (JSON). It distinguishes itself from siblings like format_bytes or format_text_case by specifying JSON as the target data format. However, it doesn't explicitly differentiate from potential JSON-specific siblings that might exist on other servers.
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, when not to use it, or compare it to similar tools like validate_data or format_text_case for different data types. The agent must infer usage from the action enum and parameter descriptions 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 full burden for behavioral disclosure. It states what types of data can be generated but doesn't mention important behavioral traits like whether generation is deterministic, what the default output format is, whether there are rate limits, or what happens with invalid parameters. For a tool with no 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently lists all four data generation options. Every word earns its place with zero waste. It's appropriately sized for a straightforward utility tool and front-loads the core functionality.
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 no annotations and no output schema, the description should do more to explain what the tool returns. It mentions what can be generated but not what the output looks like (e.g., string format, encoding). For a data generation tool with 3 parameters and no structured output documentation, this leaves the agent guessing about return values.
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 three parameters thoroughly. The description mentions the four data types that map to the 'type' parameter enum but doesn't add meaningful semantic context beyond what the schema provides. No additional parameter guidance is given for length or format parameters.
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 'generate' and the resource 'random data', listing specific types (UUID v4, hex strings, base64 strings, raw bytes). It distinguishes from siblings like generate_password or generate_slug by focusing on raw random data generation rather than structured outputs. However, it doesn't explicitly differentiate from all siblings in the list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 when to choose generate_random over generate_password for security contexts or when raw bytes might be preferred over formatted strings. There's no explicit when/when-not usage or named alternatives.
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: it doesn't mention error handling (e.g., for invalid inputs), output format, or any constraints like rate limits or permissions. For a tool with no annotations, this leaves significant gaps in understanding its operational traits.
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 extremely concise and front-loaded: 'Convert between color formats (hex, RGB, HSL)' is a single, efficient sentence that directly states the purpose with zero waste. Every word earns its place, making it easy to parse and understand quickly.
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 moderate complexity (3 parameters, no nested objects) and the absence of annotations and output schema, the description is minimally adequate. It covers the basic function but lacks details on behavior, error handling, and output format, which are important for a conversion tool. It's complete enough to understand what it does but not how it works fully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions for all three parameters ('input', 'from', 'to') and enums for 'from' and 'to'. The description adds minimal value beyond the schema by listing the formats (hex, RGB, HSL), but it doesn't provide additional context like examples or edge cases. Given the high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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: 'Convert between color formats (hex, RGB, HSL)'. It specifies the action (convert) and the resources involved (color formats), making the function immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'convert_timezone' beyond the domain difference, 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 Guidelines2/5
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 any prerequisites, limitations, or comparisons to other tools in the server (e.g., when to choose this over other conversion tools like 'convert_timezone'). The usage is implied by the purpose but lacks explicit context 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 but only states the basic action. It doesn't cover important aspects like error handling (e.g., for malformed input), performance characteristics, or what the output looks like (e.g., decoded string format). This leaves gaps for an AI agent to understand tool 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 with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple utility tool, making it easy for an AI agent to parse quickly.
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 low complexity (a straightforward decoding operation) and the schema's full parameter coverage, the description is minimally adequate. However, with no output schema and no annotations, it lacks details on return values or behavioral traits, leaving room for improvement in completeness for reliable agent 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 clear documentation for both parameters ('input' and 'component'), including an enum for 'component'. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 without compensating for any schema 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 'Decode URL-encoded string' clearly states the verb (decode) and resource (URL-encoded string), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'encode_url', which performs the inverse operation, though the distinction is implied through the verb choice.
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 'decode_base64' for other encoding types or 'encode_url' for the reverse operation, nor does it specify contexts where URL decoding is needed (e.g., handling web data or 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?
With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic function without mentioning error handling, performance characteristics, or output format details. It doesn't address whether invalid inputs cause errors or what the Base64 output looks like, which are important for an encoding operation.
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 extremely concise at just four words, front-loading the core purpose with zero wasted language. Every word earns its place, making it efficient for quick comprehension without unnecessary elaboration.
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?
For a simple encoding tool with two parameters and no output schema, the description covers the basic purpose adequately. However, it lacks context about typical use cases, error scenarios, or output format, which would help an agent understand when and how to use it effectively. The absence of annotations means the description should do more heavy lifting.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, so parameters are fully documented in structured form. The description adds no additional parameter information beyond what's in the schema, which is acceptable given the high coverage. However, it doesn't explain the relationship between 'input' and 'encoding' parameters or provide usage examples.
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 ('encode') and resource ('string to Base64'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling 'decode_base64' beyond the obvious inverse operation, which would require mentioning the complementary relationship for full clarity.
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 'encode_url' or 'hash_string', nor does it mention prerequisites or typical use cases. It states what the tool does but not when it's appropriate, leaving the agent to infer usage 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 full burden for behavioral disclosure. It mentions the two format types (binary/decimal) but doesn't describe what 'human-readable format' means in practice (e.g., returns strings like '1.5 MB'), doesn't mention default behaviors beyond what's in the schema, and doesn't address edge cases like negative bytes or performance characteristics. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Format bytes' (action), 'to human-readable format' (outcome), '(binary or decimal)' (key options). No wasted words or unnecessary elaboration for this straightforward utility tool.
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 this is a simple data formatting utility with 3 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. However, without annotations and with no output schema, the description should ideally clarify what 'human-readable format' returns (e.g., string with units) and mention any default behaviors. It's complete enough for basic understanding but lacks output details that would help the agent use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters (bytes, format, precision). The description adds minimal value beyond the schema - it mentions 'binary or decimal' which is already in the format parameter's enum description. No additional parameter semantics, examples, or constraints are provided beyond what's in the structured schema.
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: 'Format bytes to human-readable format' with the specific verb 'format' and resource 'bytes'. It distinguishes between 'binary or decimal' formats, though it doesn't explicitly differentiate from sibling tools like format_number or format_json. The purpose is specific but lacks sibling comparison context.
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. There's no mention of when this specific byte formatting is appropriate compared to other formatting tools (format_number, format_json) or conversion tools. The agent must infer usage from the tool name alone without contextual 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 full burden. It states what the tool does but doesn't disclose behavioral traits like whether it's read-only, what happens with invalid inputs, performance characteristics, or what the output looks like. For a formatting tool with no annotation coverage, 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 extremely concise and front-loaded with all necessary information in a single parenthetical phrase. Every word earns its place, with no wasted text or unnecessary elaboration. The structure efficiently communicates the core functionality.
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 moderate complexity (6 parameters, no output schema, no annotations), the description is minimally complete. It covers what the tool does but lacks information about output format, error handling, or behavioral constraints. For a formatting utility that transforms data, more context about the result would be helpful, though the 100% schema coverage helps compensate.
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 6 parameters thoroughly. The description adds minimal value beyond the schema - it lists the format types (which are already in the enum) but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when 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: 'Format numbers' with specific format types listed (currency, percentage, thousands separator, decimal). It uses a specific verb ('format') and resource ('numbers'), though it doesn't explicitly distinguish from sibling tools like 'format_bytes' or 'format_json' beyond the number focus.
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. There's no mention of when to choose this over other formatting tools (like format_bytes for byte formatting) or when not to use it. Usage is implied by the format types listed 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.
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. While it mentions the conversion action, it doesn't describe error handling, performance characteristics, or what happens with invalid inputs. The description is functional but lacks operational context needed 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: the first clause states the core purpose, followed by a comprehensive list of supported formats. Every word serves a purpose with zero redundancy. The structure efficiently communicates both function and capabilities.
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 moderate complexity (text transformation with multiple formats) and 100% schema coverage but no annotations or output schema, the description is minimally adequate. It explains what the tool does but lacks information about return values, error conditions, or behavioral constraints that would be helpful for 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?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 reflects adequate coverage through the schema alone.
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: 'Convert text to different case formats' followed by a specific list of formats. It uses a specific verb ('Convert') and resource ('text'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'format_bytes' or 'format_json', 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. It doesn't mention any prerequisites, limitations, or comparison to sibling tools like 'format_bytes' or 'format_json'. The agent must infer usage from the tool name and description alone without 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 full burden for behavioral disclosure. It mentions 'secure passwords' which implies security considerations, but doesn't specify what makes them secure (e.g., cryptographically random generation, entropy levels). It also doesn't mention output format, whether passwords are stored or ephemeral, or any rate limits. The description adds minimal behavioral context 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 immediately communicates the core function. Every word earns its place: 'generate' (action), 'secure passwords' (resource), 'with customizable options' (key feature). There's no redundancy or unnecessary elaboration, making it optimally concise for this tool type.
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?
For a password generation tool with 6 well-documented parameters but no output schema and no annotations, the description is minimally adequate. It covers the what but not the how or why - missing details about security guarantees, output format, or integration considerations. The high schema coverage compensates for some gaps, but behavioral aspects remain underspecified.
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 6 parameters thoroughly with descriptions, defaults, and constraints. The description adds no additional parameter semantics beyond 'customizable options' - it doesn't explain parameter interactions, trade-offs, or provide examples. This meets the baseline 3 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 verb ('generate') and resource ('secure passwords'), specifying the tool's function. It distinguishes from siblings like 'generate_random' by focusing specifically on password generation with customizable options. However, it doesn't explicitly contrast with 'generate_slug' or other generation tools, keeping it at a 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 Guidelines2/5
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 'generate_random' or 'hash_string'. There's no mention of prerequisites, typical use cases, or scenarios where other tools might be more appropriate. The phrase 'with customizable options' hints at flexibility but doesn't offer concrete usage 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?
No annotations are provided, so the description carries full burden. It states the basic function but doesn't disclose behavioral traits like what transformations are applied (e.g., removing special characters, handling spaces), whether the operation is idempotent, error conditions, or output format. For a tool with no annotations, 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.
Conciseness5/5
Is 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 a simple transformation tool. 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (simple text transformation), 100% schema coverage, and no output schema, the description is minimally adequate. However, it lacks details on behavioral aspects like transformation rules or output format, which would be helpful for an agent to use it correctly without trial and error.
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 three parameters thoroughly. The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, edge cases, or provide 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 'Generate URL-friendly slug from text' clearly states the verb ('generate'), resource ('slug'), and purpose ('URL-friendly'). It distinguishes from siblings like 'format_text_case' or 'generate_password' by specifying the specific transformation type. However, it doesn't explicitly differentiate from all possible text transformation tools beyond the sibling list provided.
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 when slug generation is appropriate compared to other text formatting tools, nor does it specify any prerequisites or exclusions. The agent must infer usage from the purpose 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 full burden but offers minimal behavioral insight. It doesn't disclose whether validation is strict or lenient, what happens on failure (e.g., returns boolean vs error), performance characteristics, or rate limits. 'Validate' implies a read-only check, but details are lacking.
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 purpose and enumerates all supported formats. Every word earns its place with zero redundancy, making it easy to scan and understand quickly.
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?
For a validation tool with 2 parameters, 100% schema coverage, and no output schema, the description is adequate but incomplete. It covers what formats are supported but lacks details on return values, error cases, or validation specifics. Given the simplicity, it meets minimum viability but leaves gaps an agent would need to infer.
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 parameters 'input' and 'type' are well-documented in the schema. The description adds value by listing all possible validation types, but doesn't explain parameter interactions or validation rules beyond what the enum provides. Baseline 3 is appropriate given 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: 'Validate data against various formats' with a comprehensive list of supported formats. It specifies the verb (validate) and resource (data), though it doesn't explicitly differentiate from siblings like 'analyze_language' or 'format_json' which 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 Guidelines2/5
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, error handling, or compare it to similar tools like 'format_json' or 'decode_base64' that might overlap in functionality. Usage context is implied but not explicit.
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. While 'Get' implies a read-only operation, it doesn't explicitly state whether this requires permissions, whether it's safe to call frequently, what format the information returns in, or potential limitations. The description mentions what information is retrieved but not how it behaves 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states what the tool does. Every word earns its place - 'Get' (action), 'operating system version, platform information, and current user information' (resources retrieved). No wasted words or redundant information.
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?
For a simple, parameterless information retrieval tool with no output schema, the description is adequate but has gaps. It specifies what information is retrieved but doesn't describe the return format, structure, or any behavioral aspects. Given the simplicity of the tool (0 params, no annotations), the description meets minimum requirements but could be more complete by addressing output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage (empty schema). The description appropriately doesn't discuss parameters since none exist. It focuses instead on what information the tool retrieves, which is the correct emphasis for a parameterless tool.
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 specific verbs ('Get') and resources ('operating system version, platform information, and current user information'). It distinguishes itself from siblings by focusing on system information retrieval rather than data transformation or analysis. However, it doesn't explicitly differentiate from potential similar tools like 'get_current_datetime' beyond the different data domains.
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, context for usage, or comparison with sibling tools. While the tool's purpose is clear, there's no explicit 'when' or 'when not' guidance for an AI agent.
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 mentions the tool 'generates a hash' but doesn't specify whether this is deterministic, what algorithm is used, if it's cryptographically secure, or what the output format looks like (e.g., hex, base64). For a hashing 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: the core purpose is stated in the first phrase, and the additional context is brief and relevant. Every sentence earns its place without redundancy, making it efficient and easy to parse.
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 moderate complexity (hashing function), no annotations, no output schema, and 1 parameter with full schema coverage, the description is minimally adequate. It states what the tool does and hints at usage but lacks details on behavior (e.g., algorithm, output format) and doesn't fully compensate for the missing annotations, leaving room for improvement.
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 parameter 'input' documented as 'String to hash'. The description adds no additional parameter semantics beyond this, as it doesn't explain constraints like string length or character set. 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate hash for a string' specifies both the action (generate hash) and the resource (string). It distinguishes from siblings like encode_base64 or generate_password by focusing specifically on hashing. However, it doesn't specify the hashing algorithm or output format, keeping it from 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage context with '(useful for .env file keys)', suggesting a specific application scenario. However, it doesn't explicitly state when to use this versus alternatives like generate_password (for security) or encode_base64 (for encoding), nor does it mention any prerequisites or exclusions. The guidance is helpful but incomplete.
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 but only states what the tool returns, not behavioral traits like performance, rate limits, or error handling. It mentions output formats but doesn't disclose if it's real-time, cached, or has any side effects, leaving gaps for a tool with potential time-sensitive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. It could be slightly more structured by explicitly noting the lack of parameters, but it avoids redundancy and wastes no words, earning a high score for brevity and clarity.
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 low complexity (0 params, no annotations, no output schema), the description is minimally adequate but incomplete. It covers the purpose and output formats but lacks behavioral context (e.g., real-time vs. cached, error cases). Without annotations or output schema, more detail on return values or usage constraints would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is appropriate here, but doesn't explicitly state 'no parameters required,' so it slightly underperforms. Baseline for 0 params is 4, as the description doesn't need to compensate for schema gaps.
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 verb ('Get') and resource ('current date and time'), with explicit scope ('all available formats'). It distinguishes from siblings by focusing on datetime retrieval rather than analysis, conversion, generation, or validation operations.
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 through 'all available formats,' suggesting this tool is for comprehensive datetime retrieval. However, it lacks explicit guidance on when to use this vs. alternatives like convert_timezone or generate_random for timestamps, and provides no exclusions or prerequisites.
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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