Text-Toolkit

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. The case conversion tools target specific case formats, encoding/decoding tools handle different formats, and string operations are well-separated. Even similar-sounding tools like string_replace and regex_replace are clearly differentiated by their descriptions.
Naming Consistency5/5
All tools follow a consistent verb_noun or noun_verb pattern with snake_case throughout. The naming is highly predictable: case_to_*, count_*, decode_*, encode_*, format_*, generate_*, regex_*, string_*, and validate_* patterns create excellent consistency.
Tool Count2/5
43 tools is excessive for a text processing server. While the domain is broad, many tools could be consolidated (e.g., 13 case conversion tools could be a single parameterized tool). The count feels bloated and will overwhelm agents with too many similar options.
Completeness5/5
The tool surface is remarkably complete for text processing. It covers case conversion, counting, encoding/decoding, formatting, hashing, generation, regex operations, string manipulation, and validation. No obvious gaps exist for common text manipulation needs.
Average 2.7/5 across 43 of 43 tools scored. Lowest: 1.7/5.
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 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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior1/5
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. 'Generate HMAC hash' reveals nothing about whether this is a read-only operation, whether it has side effects, what permissions might be required, rate limits, error conditions, or output format. For a cryptographic tool with zero annotation coverage, this represents a complete failure to describe behavioral characteristics beyond the basic function name.
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 maximally concise at just three words. While this represents severe under-specification rather than ideal conciseness, from a pure structural perspective, there is zero wasted language or unnecessary elaboration. Every word directly contributes to stating the tool's basic function, though this comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a cryptographic tool with 3 parameters, 0% schema coverage, no annotations, no output schema, and multiple similar sibling tools, the description is completely inadequate. It doesn't explain what HMAC is, how it differs from other hash functions, what the parameters mean, what the output looks like, or any behavioral characteristics. For a tool of this complexity and context, the description provides virtually no useful information beyond the tool name itself.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 3 parameters (text, key, algorithm), the description provides absolutely no information about what these parameters mean, their expected formats, or valid values. 'Generate HMAC hash' doesn't mention any parameters at all, leaving the agent completely in the dark about what inputs are required and what they represent. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate HMAC hash' is a tautology that essentially restates the tool name 'generate_hmac'. While it correctly identifies the verb ('Generate') and resource ('HMAC hash'), it provides no additional specificity about what HMAC means, what it's used for, or how it differs from sibling hash generation tools like generate_md5, generate_sha1, etc. The purpose is technically correct but lacks differentiation and meaningful context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance on when to use this tool versus alternatives. There are multiple hash generation siblings (generate_md5, generate_sha1, generate_sha256, generate_sha512, generate_hmac), but the description doesn't explain what makes HMAC different, when it's appropriate versus other hash functions, or any prerequisites for use. This leaves the agent with no contextual decision-making framework.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. 'Generate a UUID' gives no information about side effects, performance, rate limits, or output format. It doesn't specify whether this is a deterministic or random generation, or what happens when parameters are omitted. This is inadequate for a tool with parameters.
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 two words, with no wasted language. It's front-loaded with the core action, though this brevity comes at the cost of completeness. For conciseness alone, it's optimal.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description is completely inadequate. It doesn't explain what the tool returns, how parameters interact, or behavioral traits. For a UUID generator with multiple configuration options, this leaves the agent guessing about functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 4 parameters (version, namespace, name, uppercase) with 0% description coverage, and the tool description provides no information about any parameters. It doesn't explain what 'version' controls (e.g., UUID v4 vs v5), what 'namespace' and 'name' are for in namespaced UUIDs, or how 'uppercase' affects output. With low schema coverage, the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate a UUID' is a tautology that restates the tool name without adding specificity. It doesn't explain what kind of UUID is generated (e.g., v4 random, v5 namespaced) or how it differs from sibling tools like validate_uuid. The purpose is minimally stated but lacks distinguishing details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention sibling tools like generate_hmac or validate_uuid, nor does it specify contexts where UUID generation is appropriate (e.g., for unique identifiers in databases). There's no indication of prerequisites or constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. 'Replace text' gives no information about whether this is a read-only or destructive operation, what permissions might be required, error handling, or any behavioral traits. This leaves the agent with insufficient context to understand how the tool behaves.
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 two words, 'Replace text', which is front-loaded and wastes no space. However, this conciseness comes at the cost of being under-specified rather than efficiently informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a 4-parameter tool with no annotations, 0% schema description coverage, and no output schema, the description is completely inadequate. It fails to provide necessary context about purpose, usage, behavior, or parameters, making it insufficient for an agent to effectively use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 4 parameters, the description 'Replace text' adds no meaningful information about parameter semantics. It doesn't explain what 'text', 'search', 'replace', or 'replace_all' mean, their expected formats, or how they interact, leaving parameters completely undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Replace text' is a tautology that essentially restates the tool name 'string_replace' without adding meaningful specificity. While it does indicate a text replacement function, it lacks the detail needed to distinguish this tool from sibling tools like 'regex_replace' or other string manipulation tools in the server.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'regex_replace' available, there is no indication of whether this tool performs simple string replacement versus regex-based replacement, or any other contextual usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions conversion but does not explain what 'path/case' entails (e.g., how delimiters are used, what locale affects, or how ambiguous characters are handled). This leaves key behavioral traits undefined, making it inadequate for a tool with multiple parameters.
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 very concise ('Convert text to path/case'), which is efficient and front-loaded. However, it is arguably too brief, bordering on under-specified rather than optimally concise, as it omits necessary details for clarity.
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 (4 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It does not explain the conversion behavior, parameter usage, or output format, leaving significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It does not mention any parameters (text, delimiter, locale, mergeAmbiguousCharacters) or their roles, failing to add meaning beyond the bare schema. This is insufficient given the four parameters involved.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Convert text to path/case' states a basic purpose (converting text to a specific case format) but is vague about what 'path/case' means. It distinguishes from siblings like 'case_to_camel' or 'case_to_snake' by implying a different output format, but lacks specificity about what makes 'path/case' unique compared to other case conversion tools 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?
No guidance is provided on when to use this tool versus alternatives like 'case_to_kebab' or 'case_to_snake', which appear to serve similar case conversion purposes. The description does not mention any specific contexts, prerequisites, or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. 'Generate lorem ipsum text' does not reveal any behavioral traits, such as whether the operation is read-only, destructive, has rate limits, or requires authentication. It lacks details on output format, error handling, or side effects, making it insufficient for a tool with 7 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'Generate lorem ipsum text', which is front-loaded and wastes no words. It efficiently conveys the core purpose without unnecessary elaboration, 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.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It does not address parameter usage, behavioral aspects, or output expectations, leaving significant gaps for an AI agent to invoke the tool correctly. The lack of context makes it inadequate for a tool with multiple inputs and unknown return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no meaning beyond the input schema, which has 7 parameters with 0% schema description coverage (no descriptions in schema properties). Parameters like 'count', 'units', and 'format' are undocumented in both schema and description. With low coverage, the description fails to compensate by explaining what parameters do or how they affect the output, leaving semantics unclear.
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 lorem ipsum text' clearly states the verb ('Generate') and resource ('lorem ipsum text'), making the purpose evident. However, it does not differentiate from sibling tools like 'generate_uuid' or 'generate_hmac' beyond the resource type, as all involve generation but of different outputs. It avoids tautology by not merely restating the name, but lacks specificity on scope or format.
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 does not mention any context, prerequisites, or exclusions, such as when to choose this over other text-generation tools or how it relates to siblings like 'count_words' or 'format_html'. Usage is implied only by the name and purpose, with no explicit instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the conversion action without disclosing behavioral traits like error handling, performance, side effects, or output format details. For a tool with 4 parameters, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded and appropriately sized for a straightforward tool, though it may be too brief given the parameter complexity.
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 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the conversion logic, parameter interactions, or expected results, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'text' but doesn't explain the other parameters (delimiter, locale, mergeAmbiguousCharacters) or their roles in the conversion process. The description adds minimal value beyond the 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 verb 'Convert' and the resource 'text', specifying the output format 'dot.case'. It distinguishes from siblings like 'case_to_camel' or 'case_to_snake' by naming the specific case conversion, but doesn't explicitly differentiate beyond the case type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for case conversion (e.g., case_to_camel, case_to_snake), the description lacks context about scenarios where dot.case is preferred, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 what 'Pascal_Snake_Case' means (e.g., capital letters with underscores), how errors are handled, or any performance considerations. This is inadequate for a tool with 4 parameters.
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 sentence that directly states the tool's function. There's no wasted verbiage, and it's front-loaded with the core purpose.
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 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is severely incomplete. It doesn't explain what the tool returns, how parameters interact, or provide any context for usage. This leaves significant gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but fails to do so. It mentions no parameters at all, leaving 'text', 'delimiter', 'locale', and 'mergeAmbiguousCharacters' completely unexplained. The description adds zero value beyond the bare 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 verb 'Convert' and the target format 'Pascal_Snake_Case', which is specific and distinguishes it from sibling tools like case_to_pascal or case_to_snake. However, it doesn't fully differentiate from all case conversion siblings beyond naming the output format.
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 on when to use this tool versus alternatives like case_to_pascal or case_to_snake is provided. The description only states what it does, not when it's appropriate compared to other case conversion tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 only states what the tool does without any information on performance, error handling, input constraints, or output format. This is inadequate for a tool with undocumented parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just three words, making it front-loaded and efficient. There's no wasted language, though this brevity contributes to gaps in other dimensions like parameter semantics and behavioral transparency.
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, 0% schema description coverage, and no output schema, the description is insufficient. It doesn't compensate for these gaps by explaining input expectations, output format, or behavioral traits, making it incomplete for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, and the description doesn't mention the 'text' parameter at all. It fails to explain what the input should contain (e.g., HTML-encoded strings) or provide examples, leaving the parameter's meaning unclear beyond the schema's basic type definition.
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 HTML entities' clearly states the tool's function with a specific verb ('decode') and resource ('HTML entities'), making the purpose immediately understandable. However, it doesn't explicitly distinguish this tool from its sibling 'encode_html', which performs the inverse operation, though the distinction is logically implied.
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 like processing web content or cleaning text, nor does it reference the sibling 'encode_html' for reverse operations. Usage is implied from the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 ('Calculate') but doesn't describe what the tool returns (e.g., scores, formats), whether it has side effects, performance characteristics, or error handling. For a tool with no annotation coverage, this is a significant gap in transparency.
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 three words, front-loading the core purpose without any fluff. Every word earns its place by directly stating the tool's function, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a calculation tool with no annotations, no output schema, and low schema coverage), the description is incomplete. It doesn't explain what readability metrics are, how results are returned, or usage context, leaving the agent with insufficient information to effectively 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?
The description adds no parameter semantics beyond the input schema. With 0% schema description coverage and 1 parameter ('text'), the schema alone defines it as a required string. The description doesn't explain what 'text' should contain (e.g., plain text, length limits, language), so it doesn't compensate for the low coverage, but the single parameter keeps it from being worse.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Calculate readability metrics' states a clear verb ('Calculate') and resource ('readability metrics'), but it's somewhat vague about what specific metrics are calculated. It distinguishes from siblings like count_words or count_characters by focusing on readability rather than basic counts, but lacks specificity about what constitutes 'readability metrics' (e.g., Flesch-Kincaid, Gunning Fog).
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 use, or differentiate from sibling tools like count_words or string_analysis tools that might overlap in text processing. This leaves the agent without explicit direction on 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 full burden. 'Convert text to camelCase' implies a pure transformation with no side effects, but it doesn't disclose behavioral traits like: whether it preserves non-alphanumeric characters, how it handles locale-specific rules (given the locale parameter), what happens with empty input, or error conditions. For a 4-parameter 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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a straightforward transformation tool. Every word earns its place.
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 4 parameters with 0% schema coverage, no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't explain parameter roles, output format, error handling, or differentiation from alternatives. For a text transformation tool in a crowded namespace, more context is needed to guide proper usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 4 parameters have descriptions in the schema. The tool description mentions only 'text' implicitly but provides no information about the delimiter, locale, or mergeAmbiguousCharacters parameters. It doesn't explain what these parameters do, their default values, or how they affect the conversion. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the transformation target 'to camelCase', which is specific and unambiguous. It distinguishes from siblings like case_to_snake or case_to_kebab by naming the exact output format. However, it doesn't specify what type of input text it accepts (e.g., any case format) or mention the resource being transformed.
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 the many sibling case conversion tools (case_to_snake, case_to_kebab, etc.). It doesn't indicate what input formats are appropriate, when camelCase output is preferred, or any prerequisites. The agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the conversion action but doesn't explain what Train-Case entails (e.g., capitalization rules, handling of spaces/special characters), potential side effects, or output format. This leaves significant gaps for a tool with multiple parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters with 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain the transformation behavior, parameter roles, or output format, which are essential for proper tool invocation. This is inadequate for a parameter-rich text processing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 4 parameters (text, delimiter, locale, mergeAmbiguousCharacters) are documented in the schema. The description adds no information about these parameters, failing to compensate for the coverage gap. For example, it doesn't explain what 'delimiter' or 'locale' do in the context of Train-Case conversion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the resource 'text to Train-Case', making the purpose immediately understandable. It distinguishes from siblings by specifying the exact case transformation (Train-Case), though it doesn't explicitly contrast with other case conversion tools like case_to_camel or case_to_snake.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for case conversion (e.g., case_to_camel, case_to_snake), the description lacks context on Train-Case's specific use cases or differences from other formats, leaving the agent to infer based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but lacks behavioral details. It doesn't disclose that MD5 is a one-way hash function, potential performance aspects, or output format (e.g., hexadecimal string). It implies generation but doesn't clarify if it's deterministic or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with no wasted words—just three words. It's front-loaded and efficiently states the core function, though this brevity contributes to gaps in other dimensions.
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, no output schema, and 0% schema coverage, the description is incomplete. It doesn't explain the tool's behavior, output, or context within sibling tools, leaving significant gaps for an AI agent to understand and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description doesn't add parameter meaning. It doesn't explain what 'text' represents (e.g., input string to hash) or any constraints (e.g., encoding, length). With 1 parameter and low coverage, baseline is 3 as it minimally compensates by implying text is hashed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate MD5 hash' states the action and output but is vague about what it generates the hash from. It doesn't specify that it hashes the input text parameter, nor does it differentiate from sibling hash tools like generate_sha1 or generate_sha256, which perform similar functions with different algorithms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention MD5's specific use cases (e.g., checksums, legacy systems) or warn against its limitations (e.g., cryptographic insecurity), nor does it reference sibling tools for other hash algorithms or text processing tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. 'Generate SHA-1 hash' implies a deterministic computation but reveals nothing about performance, error handling, output format, or security implications (e.g., SHA-1 is cryptographically broken). This is inadequate for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase with zero wasted words. It's front-loaded with the core purpose and appropriately sized for a simple hashing tool. Every word earns its place by specifying both the action and algorithm.
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, 0% schema coverage, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., hex string), doesn't document the single parameter, and provides no behavioral context. For a cryptographic tool, this leaves significant gaps in understanding how to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It doesn't mention the 'text' parameter at all, leaving the input completely undocumented. However, since there's only one parameter and SHA-1 is a standard algorithm, the agent might infer the parameter's purpose. This meets the minimum baseline but doesn't add meaningful semantics beyond the 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 'Generate SHA-1 hash' clearly states the verb ('Generate') and resource ('SHA-1 hash'), making the purpose immediately understandable. It distinguishes from siblings like generate_md5, generate_sha256, and generate_sha512 by specifying the exact hash algorithm. However, it doesn't explicitly mention what resource is being hashed (text input), 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 when SHA-1 is appropriate compared to other hash functions (e.g., MD5 for speed, SHA-256 for security), nor does it reference sibling tools like generate_md5 or generate_sha256. The agent must infer usage 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. It states the action ('validate') but doesn't explain what validation entails (e.g., format checks, version support), what happens on success/failure (e.g., returns boolean, error message), or any constraints (e.g., rate limits). 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 with a single, front-loaded sentence ('Validate a UUID') that directly states the purpose without any wasted words. It's appropriately sized for a simple tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema, no annotations), the description is incomplete. It lacks details on validation criteria, return values, or error handling, which are essential for an agent to use the tool correctly. While concise, it doesn't provide enough context for reliable 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 input schema has 1 parameter with 0% description coverage, so the schema provides no semantic details. The description doesn't add any parameter-specific information beyond implying a 'uuid' input, which is already obvious from the schema's property name. This meets the baseline for minimal value when schema coverage is low but doesn't compensate meaningfully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Validate a UUID' clearly states the tool's function with a specific verb ('validate') and resource ('UUID'), making the purpose understandable. However, it doesn't distinguish this tool from potential siblings that might also validate UUIDs in different ways or contexts, which is a minor gap in specificity.
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, such as needing a UUID string to validate, or compare it to sibling tools like 'generate_uuid' for creating UUIDs, leaving the agent to infer usage from context 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 only states the basic transformation. It doesn't disclose behavioral traits like what 'Capital Case' means precisely, whether it handles special characters, what happens with empty input, error conditions, or performance characteristics. The description is minimal and lacks important operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 4 words. It's front-loaded with the core purpose and wastes no words. Every word earns its place by communicating the essential transformation.
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 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain the transformation details, parameter usage, return values, or edge cases. For a text transformation tool with multiple parameters, this minimal description leaves too much unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but fails to do so. It mentions no parameters at all, leaving all 4 parameters (text, delimiter, locale, mergeAmbiguousCharacters) completely undocumented. The description doesn't explain what these parameters do or how they affect the transformation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Convert' and the resource 'text' with the specific transformation 'to Capital Case'. It distinguishes from some siblings like 'case_to_camel' by specifying the case type, but doesn't differentiate from all text transformation tools like 'string_replace' or 'regex_replace'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention when to choose this over other case conversion tools (like case_to_camel, case_to_snake) or general string manipulation tools. There's no context about typical use cases or limitations.
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 the basic function. It doesn't disclose behavioral traits like whether the conversion is locale-sensitive (hinted by the locale parameter), what happens with special characters, or if the operation is idempotent. The description adds minimal context beyond the name.
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?
Extremely concise with a single sentence that directly states the tool's function. There is no wasted verbiage, and the information is front-loaded appropriately for such a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with 0% schema coverage, no annotations, no output schema, and many sibling tools, the description is incomplete. It doesn't help an agent understand parameter meanings, output format, or differentiation from similar tools, leaving significant gaps for proper tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but provides no parameter information. It doesn't explain what 'text' should contain, what 'delimiter' overrides (default hyphen), what 'locale' affects, or what 'mergeAmbiguousCharacters' does. With 4 parameters and 0% schema coverage, this is inadequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the resource 'text', specifying the target format 'kebab-case'. It distinguishes from siblings like case_to_camel or case_to_snake by naming the specific case type, but doesn't explain what kebab-case entails beyond the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like case_to_snake or case_to_camel. The description only states what it does, not in what contexts or for what purposes it should be selected among the many sibling text transformation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action 'convert' but gives no behavioral details such as what 'no case' entails (e.g., lowercasing, delimiter handling), error conditions, or output format. This is inadequate for a tool with 4 parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'Convert text to no case', which is front-loaded and wastes no words. It efficiently states the core purpose 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?
Given the complexity (4 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It lacks details on behavior, parameter usage, and output, making it insufficient for an agent to reliably invoke this tool without additional context or experimentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'text' implicitly but adds no meaning for parameters like delimiter, locale, or mergeAmbiguousCharacters. The description fails to explain what these parameters do or how they affect the conversion, leaving them undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the resource 'text', specifying the transformation to 'no case'. It distinguishes from siblings like case_to_camel or case_to_snake by naming the specific case format, though it doesn't explicitly contrast with them in the description 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. With many sibling tools for case conversion (e.g., case_to_camel, case_to_snake), there is no indication of what 'no case' means or when it's preferred, leaving the agent to guess based on the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic transformation without disclosing behavioral traits like error handling, performance characteristics, or what happens with invalid inputs. It mentions conversion but lacks details on edge cases or output format specifics.
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 and front-loaded, directly stating the tool's purpose 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?
Given 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameter semantics, behavioral details, or return values, making it inadequate for a tool with multiple configuration options.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but only mentions 'text' implicitly. It doesn't explain the purpose of parameters like delimiter, locale, or mergeAmbiguousCharacters, leaving 3 of 4 parameters undocumented. The description adds minimal value beyond the 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 'Convert text to PascalCase' clearly states the verb (convert) and resource (text) with the specific output format (PascalCase). It distinguishes from siblings like case_to_camel or case_to_snake by specifying PascalCase, but doesn't explicitly differentiate beyond naming the target case format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like case_to_camel or case_to_snake, despite having many sibling case conversion tools. The description implies usage for PascalCase conversion but offers no context about 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 only states the conversion action without details on output format, error handling, or side effects. For a tool with 4 parameters and no annotations, this is insufficient, as it doesn't explain how parameters like 'delimiter' or 'locale' affect 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—just four words—and front-loaded with the core action. There is no wasted language, making it easy to parse quickly. However, this brevity comes at the cost of completeness, as noted in other dimensions.
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 (4 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior, parameter roles, or output format, leaving significant gaps for an agent to understand and invoke it correctly. The conciseness doesn't compensate for these missing details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, meaning parameters are undocumented in the schema. The description adds no information about parameters beyond the required 'text' field. It doesn't explain what 'delimiter,' 'locale,' or 'mergeAmbiguousCharacters' do, failing to compensate for the low schema coverage and leaving key semantics unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Convert text to Sentence case.' It specifies the verb ('Convert') and resource ('text'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'case_to_capital' or 'case_to_pascal,' which are similar case-conversion tools, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools for case conversion (e.g., 'case_to_camel,' 'case_to_snake'), there is no indication of what 'Sentence case' means or when it's preferred over other formats. This lack of context makes it hard for an agent to choose appropriately.
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. 'Convert text to snake_case' implies a transformation operation but reveals nothing about error handling, performance characteristics, locale sensitivity (despite a locale parameter), or what happens with special characters. The description doesn't address the behavioral implications of the four parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at just 4 words. It's front-loaded with the core action and delivers maximum information density with zero wasted words. Every word earns its place in this minimal but complete statement of purpose.
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 4 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what snake_case transformation entails, how parameters affect the conversion, what the output looks like, or edge cases. The context demands more explanation than provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 4 parameters, the description must compensate but fails to do so. It mentions 'text' implicitly but doesn't explain the purpose of 'delimiter', 'locale', or 'mergeAmbiguousCharacters' parameters. The description adds no meaningful parameter semantics beyond what's obvious from the tool name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the resource 'text to snake_case', making the purpose immediately understandable. It distinguishes from some siblings like 'case_to_camel' by specifying the target case format, though it doesn't explicitly differentiate from all case conversion tools 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for case conversion (camel, kebab, pascal, etc.), there's no indication of when snake_case is appropriate or when to choose this over other formatting tools like encode/decode or regex operations.
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. 'Format and beautify HTML' implies a read-only transformation, but it doesn't specify whether it modifies the input, handles errors, or has any side effects like rate limits or authentication needs. This leaves significant gaps for a tool with parameters.
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 three words, front-loading the core purpose without any wasted text. It efficiently communicates the tool's function in a minimal format.
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 has 2 parameters with 0% schema coverage and no output schema, the description is incomplete. It lacks details on parameter usage, return values, and behavioral traits, making it inadequate for an agent to reliably invoke this tool without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'HTML' which relates to the 'text' parameter, but doesn't explain what 'text' should contain or the purpose of 'indent_size'. This adds minimal value beyond the schema's basic 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 'Format and beautify HTML' clearly states the tool's purpose with a specific verb ('format and beautify') and resource ('HTML'). It distinguishes from most siblings that handle text transformation, encoding, or generation, though it doesn't explicitly differentiate from 'format_json' or 'format_xml' which are similar formatting tools.
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 prefer it over other formatting tools like 'format_json' or 'format_xml', nor does it specify prerequisites such as input requirements or typical 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 the full burden of behavioral disclosure. While 'format and beautify' implies a read-only transformation, the description doesn't specify what 'beautify' entails (e.g., indentation, line breaks), whether it validates JSON, handles errors, or returns formatted output. This leaves significant behavioral aspects unclear.
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 three words, with no wasted language. It's front-loaded with the core purpose and contains no unnecessary information, 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?
For a tool with 2 parameters (one required), 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how it handles invalid JSON, what 'beautify' means operationally, or provide any parameter guidance. The context demands more complete documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for both parameters, the description provides no information about the 'text' parameter (presumably the JSON to format) or the 'indent_size' parameter (presumably controlling indentation). The description doesn't compensate for this complete lack of parameter documentation in the 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 'Format and beautify JSON' clearly states the tool's purpose with a specific verb ('format and beautify') and resource ('JSON'), making it immediately understandable. However, it doesn't distinguish this tool from its sibling 'format_html', 'format_sql', and 'format_xml', which perform similar formatting operations on different data types.
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 are multiple sibling tools for formatting different data types (HTML, SQL, XML) and for case conversion, but the description doesn't indicate this is specifically for JSON formatting or when to choose it over other formatting tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'format and beautify' implies a read-only transformation, it doesn't specify whether the tool validates SQL syntax, what formatting rules it applies, whether it preserves comments, or what happens with malformed input. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
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 three words, with zero wasted language. It's front-loaded with the core functionality and uses efficient phrasing. Every word earns its place in conveying the essential purpose.
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 transformation tool with 2 parameters (one required), 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, what formatting standards it follows, or provide any context about the parameters. Given the complexity and lack of structured documentation, the description should do much more to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for both parameters, the description provides no information about what 'text' should contain or what 'indent_size' controls. The description doesn't mention that 'text' is the SQL to format or explain what 'indent_size' means in the context of SQL beautification. This fails to compensate for the complete lack of schema documentation.
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 ('format and beautify') and resource ('SQL'), making it immediately understandable. However, it doesn't distinguish this tool from its sibling 'format_html', 'format_json', and 'format_xml' tools, which all perform similar formatting functions on different data types.
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 SQL formatting is needed, what types of SQL it handles, or how it differs from other formatting tools like 'format_json' or 'format_html' in the sibling list.
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. 'Format and beautify XML' implies a transformation operation but doesn't specify whether this is read-only, what happens with malformed XML, whether the operation is reversible, or any performance characteristics. The description lacks crucial behavioral context for a transformation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 3 words. Every word earns its place: 'Format' and 'beautify' describe the action, 'XML' specifies the target. 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?
For a transformation tool with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'beautified' means, what the output looks like, how errors are handled, or provide any parameter guidance. The description leaves too many open questions for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 2 parameters (1 required), the description provides no information about parameters. It doesn't mention the 'text' parameter (required XML input) or 'indent_size' parameter (optional formatting control). The description fails to compensate for the complete lack of schema documentation.
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 and beautify XML' specifies the verb (format/beautify) and resource (XML). It distinguishes from siblings like format_html and format_json by specifying XML, but doesn't explain how it differs from other formatting tools beyond the target format.
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 XML formatting is needed, what constitutes 'beautified' XML, or how this differs from similar tools like format_html or format_json. No context about appropriate use cases is provided.
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 basic operation but doesn't describe what happens with multiple matches (e.g., returns all matches, first match only), error handling for invalid patterns, performance implications, or output format. For a regex tool with 3 parameters and 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 at just 5 words ('Extract matches using a regex pattern'), with zero wasted words. It's front-loaded with the core purpose. Every word earns its place, making it efficient despite its simplicity.
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 regex operations, 3 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., array of matches, match objects), how it handles edge cases, or provide examples. For a tool with this level of potential complexity, the description is inadequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'regex pattern' which relates to the 'pattern' parameter, but doesn't explain the 'text' parameter (input string to search) or 'flags' parameter (regex flags like 'i' for case-insensitive). The description adds minimal value beyond the schema, failing to adequately cover the 3 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 'Extract matches using a regex pattern' clearly states the verb ('extract') and resource ('matches'), specifying it's a regex-based extraction operation. It distinguishes from siblings like regex_replace, regex_split, and regex_test by focusing on extraction rather than replacement, splitting, or testing. However, it doesn't explicitly differentiate from non-regex siblings that might also extract 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 regex extraction is preferable over other extraction methods (like string_substring), nor does it clarify when to choose between regex_extract, regex_replace, regex_split, or regex_test. No context about use cases or exclusions is provided.
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 mentions the operation ('Replace text') but doesn't disclose critical traits like whether it's case-sensitive, how flags affect behavior, what happens on pattern mismatch, or error handling. For a regex 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—a single sentence with zero waste. It's front-loaded with the core action and method. Every word earns its place, making it easy to scan and understand at a glance 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?
Given the complexity of regex operations, 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is insufficient. It doesn't cover parameter meanings, behavioral nuances, error cases, or output format. For a tool that performs pattern-based text transformation, more context is needed for reliable agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but fails to do so. It doesn't explain what the 'flags' parameter does, how 'replacement' interacts with regex groups, or the expected format for 'pattern'. With 4 parameters (3 required) completely undocumented in both schema and description, this is inadequate for effective tool use.
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 ('Replace') and resource ('text'), specifying the method ('using a regex pattern'). It distinguishes from sibling tools like 'string_replace' by indicating regex-based replacement. However, it doesn't explicitly contrast with 'regex_extract' or 'regex_split', which would make it a 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?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention when regex replacement is preferred over simple string replacement ('string_replace'), or how it differs from other regex tools like 'regex_extract' or 'regex_split' in the sibling list. The description offers no context for tool selection.
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. While 'split text' implies a read-only transformation, it doesn't address critical behavioral aspects: whether the operation is case-sensitive, how empty matches are handled, what happens with invalid regex patterns, or the format of the output (e.g., array of strings). For a regex tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's appropriately sized for a straightforward tool and front-loads the core functionality. Every word earns its place, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of regex operations, 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain error handling, output format, or practical examples. For a tool with three parameters and no structured documentation, this leaves the agent with insufficient context to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for all three parameters. It mentions 'text' and 'pattern' implicitly but doesn't explain the 'flags' parameter at all. No details are provided about regex syntax, flag options (like 'i' for case-insensitive), or expected input formats. The description adds minimal value beyond what the parameter names suggest.
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: 'Split text using a regex pattern'. It specifies the verb ('split') and resource ('text'), and the regex pattern usage distinguishes it from the simpler 'string_split' sibling tool. However, it doesn't explicitly differentiate from 'regex_extract' or 'regex_replace', which keeps 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'string_split' (for simple delimiter-based splitting) and 'regex_extract' (for extracting matches rather than splitting), the agent receives no help in choosing between them. There's no mention of prerequisites, typical use cases, 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. While 'Test' implies a read-only validation operation, the description doesn't specify what the test returns (e.g., boolean match result, match positions, groups), whether it's case-sensitive, how flags affect matching, or any performance considerations. This leaves significant behavioral aspects undocumented.
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 6 words, front-loading the core purpose with zero wasted words. Every word earns its place in communicating the basic function, though this brevity comes at the cost of completeness.
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 (0% documented in schema), no annotations, no output schema, and multiple similar sibling tools, the description is inadequate. It doesn't explain what the tool returns, how to interpret results, when to use it versus other regex tools, or provide any parameter guidance. The description leaves too many contextual gaps for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 3 parameters, the description provides no additional semantic information about what 'text', 'pattern', or 'flags' represent. It doesn't explain that 'flags' is optional, what valid flag values are, or provide examples of pattern syntax. The description fails to compensate for the complete lack of schema documentation.
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 action ('Test') and the resources involved ('a regex pattern against text'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'regex_extract', 'regex_replace', or 'regex_split', which all involve regex operations but 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. With multiple sibling regex tools available (regex_extract, regex_replace, regex_split), there's no indication that this tool is specifically for testing/validating patterns rather than extracting, replacing, or splitting text based on patterns.
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 implies a transformation operation but doesn't disclose what happens with empty arrays, null values, error conditions, or the format of returned text. For a tool with zero annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just four words, with zero wasted language. It's front-loaded with the core purpose, though this brevity comes at the cost of completeness.
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 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how errors are handled, or provide enough context for reliable use despite the simple operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but doesn't. It mentions 'array' which maps to the 'parts' parameter, but provides no details about array contents, delimiter usage, or default behaviors. The 'delimiter' parameter is completely undocumented in both schema and 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 'Join an array into text' clearly states the verb ('join') and resource ('array'), specifying what the tool does. It distinguishes from siblings like string_split (which does the opposite) and string_replace (which modifies text), but doesn't explicitly mention these distinctions.
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 string_split (for splitting text) or case conversion tools, nor does it specify prerequisites or appropriate contexts for joining arrays.
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 basic action ('split text into an array') but omits critical details: it does not mention that splitting is delimiter-based, how empty strings or edge cases are handled, whether the operation is read-only or has side effects, or any performance considerations. This leaves significant gaps for a tool with parameters.
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 ('Split text into an array') with no wasted words, making it easy to parse. It is front-loaded with the core action, though this brevity comes at the cost of completeness. Every sentence (here, a single sentence) earns its place by stating the essential function without redundancy.
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 (3 parameters, no annotations, no output schema), the description is incomplete. It does not explain the splitting mechanism, parameter usage, return format (e.g., array structure), or behavioral traits. For a basic string operation tool, more context is needed to guide effective use, especially with undocumented parameters and no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description adds no information about the three parameters ('text', 'delimiter', 'limit'), such as their roles, default values (e.g., delimiter might default to whitespace), or semantics (e.g., 'limit' controls the number of splits). It fails to compensate for the schema's lack of descriptions, leaving parameters ambiguous.
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 'Split text into an array' clearly states the verb ('split') and resource ('text'), with the outcome ('into an array') specified. It distinguishes from siblings like 'string_join' (which combines) and 'regex_split' (which uses regex), though not explicitly. However, it lacks specificity about the splitting mechanism (delimiter-based), making it slightly less precise than 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 does not mention sibling tools like 'regex_split' (for regex-based splitting) or 'string_substring' (for extracting parts), nor does it specify use cases (e.g., for delimiter-based text processing). Without such context, the agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Extract a substring' implies a read-only operation that returns a portion of text, but it doesn't specify how indices work (e.g., zero-based vs. one-based, negative indices for counting from the end), error handling for out-of-bounds indices, or what happens if 'start' or 'end' are omitted. This leaves significant behavioral gaps for a tool with 3 parameters.
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 two words, front-loading the core action ('extract') and target ('substring') with zero waste. Every word earns its place by directly conveying the tool's function without unnecessary elaboration, 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.
Completeness2/5
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 annotations, no output schema), the description is incomplete. It doesn't explain how substring extraction works with the parameters, what the output looks like, or error conditions. For a tool that manipulates text based on indices, more context is needed to use it effectively without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining parameters. 'Extract a substring' implies parameters for the text and extraction bounds, but it doesn't clarify the roles of 'start' and 'end' (e.g., whether they are indices, inclusive/exclusive). Without this, users might misinterpret the schema's integer parameters. The description adds minimal value beyond the schema's basic 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 'Extract a substring' clearly states the verb ('extract') and resource ('substring'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'string_join' or 'string_replace' by focusing on extraction rather than combination or replacement. However, it doesn't specify what kind of substring extraction (e.g., by indices) beyond the basic concept.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'regex_extract' and 'string_split' that also extract portions of text, there's no indication of when substring extraction by indices is preferable over pattern-based or delimiter-based extraction. No context or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 'trim whitespace' implies a read-only transformation, the description doesn't specify whether this modifies the original text or returns a new string, what happens with null/empty input, or any performance considerations. For a tool with zero annotation coverage, this represents a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at just four words, front-loading the core functionality with zero wasted language. Every word earns its place by communicating the essential action and target. The structure is optimal for quick comprehension 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?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is insufficiently complete. It doesn't explain the optional 'trim_type' parameter, provide examples of input/output behavior, or clarify edge cases. For a text manipulation tool among many alternatives, more context about trimming behavior and parameter usage would help the agent use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for both parameters, the description 'Trim whitespace from text' provides minimal parameter guidance. It hints at the 'text' parameter but doesn't explain the 'trim_type' parameter at all. The description doesn't clarify what trimming options exist (e.g., leading, trailing, or both), what the default behavior is, or what values 'trim_type' accepts. This fails to compensate for the complete lack of schema documentation.
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 'Trim whitespace from text' clearly states the verb ('trim') and resource ('whitespace from text'), making the purpose immediately understandable. It distinguishes this tool from siblings like case conversion or encoding tools by focusing on whitespace removal rather than text transformation or analysis. However, it doesn't explicitly differentiate from potential similar tools (though none exist 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 Guidelines2/5
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 what types of whitespace are trimmed, whether it's for cleaning user input, preparing data for processing, or other use cases. With many sibling text manipulation tools available, the lack of contextual guidance leaves the agent to infer usage scenarios independently.
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 ('Count characters') but lacks details on behavior: it doesn't specify if whitespace or punctuation is included, whether it returns an integer or structured data, or if there are any limitations (e.g., text length constraints). This leaves significant 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise ('Count characters in text')—just three words—with zero wasted language. It's front-loaded and gets straight to the point, making it easy for an agent to parse quickly. Every word earns its place by specifying the action and target.
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 simplicity (1 parameter, no annotations, no output schema), the description is incomplete. It doesn't explain what 'characters' means (e.g., Unicode code points vs. bytes), doesn't mention the return type or format, and provides no behavioral context. For a tool with zero structured coverage, more detail is needed to ensure correct 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 input schema has 1 parameter ('text') with 0% description coverage, so the schema provides no semantic information. The description adds minimal value by implying the parameter is text to count characters from, but it doesn't elaborate on format (e.g., plain text, encoded strings) or constraints. Since there's only one parameter, the baseline is 4, but the lack of detail beyond the obvious reduces the score to 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Count characters in text' clearly states the verb ('Count') and resource ('characters in text'), making the purpose immediately understandable. It distinguishes from sibling tools like 'count_lines' and 'count_words' by specifying characters rather than lines or words. However, it doesn't explicitly mention what constitutes a 'character' (e.g., Unicode vs. ASCII, whitespace handling), 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 sibling tools like 'count_lines' or 'count_words' for different counting needs, nor does it specify contexts where character counting is appropriate (e.g., text analysis, input validation). The agent must infer usage 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Count lines in text' implies a read-only operation, but it doesn't specify details like how it handles different newline formats (e.g., \n vs. \r\n), whether it counts empty lines, or what the output format is. For a tool with zero annotation coverage, this leaves significant behavioral gaps, though it's not misleading.
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 three words, front-loaded with the core action, and has zero wasted words. Every part ('Count', 'lines', 'in text') directly contributes to understanding the tool's function, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no annotations, no output schema), the description is minimal but insufficient. It doesn't cover behavioral aspects like newline handling or output format, and with no annotations to fill gaps, it leaves the agent with incomplete context for reliable use. A more complete description would add details on edge cases or 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?
The input schema has 1 parameter with 0% description coverage, and the description doesn't add any parameter-specific information beyond the tool name. It doesn't explain what 'text' should contain or any constraints. However, with only one parameter and a straightforward purpose, the baseline is adequate, but it doesn't compensate for the lack of schema details.
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 'Count lines in text' clearly states the verb ('Count') and resource ('lines in text'), making the purpose immediately understandable. It distinguishes from siblings like count_characters and count_words by specifying lines rather than characters or words. However, it doesn't explicitly differentiate from all siblings (e.g., it could mention it's for line counting vs. other text operations), so it's not a perfect 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. It doesn't mention when to prefer count_lines over count_characters or count_words, nor does it specify context like handling empty lines or newline characters. There's no explicit when/when-not or alternative tool references, leaving usage decisions to the agent's inference.
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. 'Count words in text' implies a read-only operation that returns a numeric result, but it doesn't specify output format (e.g., integer count), handling of edge cases (e.g., empty strings, punctuation), or performance characteristics. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 three words, with zero wasted language. It is front-loaded with the core action ('Count words'), making it easy to parse. Every word earns its place by directly contributing to understanding the tool's function.
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 simplicity (1 parameter, no annotations, no output schema), the description is incomplete. It doesn't explain what constitutes a 'word', how the count is returned, or handle edge cases. While minimalism can be appropriate, this lacks basic context needed for reliable use, especially with siblings that perform similar operations.
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 1 parameter with 0% description coverage, so the schema provides no semantic information. The description adds minimal value by implying the 'text' parameter is the input to count words from, but it doesn't elaborate on constraints (e.g., max length, encoding) or examples. This meets the baseline for low schema coverage but doesn't fully compensate.
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 'Count words in text' clearly states the verb ('Count') and resource ('words in text'), making the purpose immediately understandable. It distinguishes from siblings like count_characters and count_lines by specifying 'words' rather than characters or lines. However, it doesn't explicitly mention what constitutes a 'word' (e.g., whitespace-separated tokens), leaving some ambiguity.
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 count_characters or count_lines, nor does it specify use cases (e.g., for text analysis vs. formatting). Without any context on when this tool is appropriate, the agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the transformation (Base64 to text) but doesn't describe error handling (e.g., for invalid input), performance characteristics, or output specifics (e.g., text encoding). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise ('Decode Base64 to text') with zero wasted words. It front-loads the core purpose efficiently. Every word earns its place, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no annotations, no output schema), the description is incomplete. It doesn't cover error cases, input validation, or output details. For a decoding operation, more context on behavior and limitations would be helpful, especially with no structured fields to 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?
The description adds no parameter semantics beyond what the input schema provides. With 0% schema description coverage, the single parameter 'text' is undocumented in both schema and description. The description implies the parameter should be Base64-encoded text but doesn't specify format or constraints. Baseline 3 is appropriate as the schema defines the parameter minimally.
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 to text' clearly states the verb (decode) and resource (Base64), with the output format (text) specified. It distinguishes from sibling tools like 'encode_base64' by indicating the opposite operation. However, it doesn't fully differentiate from other decoding tools like 'decode_html' or 'decode_url' beyond the input format.
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 (e.g., that input must be valid Base64), exclusions (e.g., not for URL-encoded data), or comparisons to siblings like 'decode_html' or 'decode_url'. Usage is implied by the name and purpose 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions encoding but doesn't specify whether this is a read-only operation, if it has side effects, error handling, or performance characteristics. For a tool with zero 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 with a single, clear sentence that directly states the tool's function. There is no wasted language or unnecessary elaboration, making it highly efficient and front-loaded.
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, output schema, and low schema description coverage, the description is insufficiently complete. It doesn't address potential complexities like input validation, error cases, or output format, leaving the agent with incomplete context for reliable tool 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 0%, but the description adds some semantic context by specifying that the input is 'text' to be encoded. However, it doesn't elaborate on encoding specifics (e.g., character encoding, handling of binary data) beyond what the schema's 'text' parameter implies, leaving room for ambiguity.
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 the resource 'text to Base64', making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'decode_base64' 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 Guidelines2/5
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_html' or 'encode_url', 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 full burden for behavioral disclosure. 'Encode HTML entities' implies a transformation operation but doesn't specify what gets encoded (e.g., <, >, &, quotes), whether it's idempotent, or if there are any side effects. For a tool with zero annotation coverage, this minimal description leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise at three words, with zero wasted language. It's front-loaded with the core action ('encode') and resource ('HTML entities'), making it immediately scannable. Every word earns its place by directly contributing to understanding the tool's function.
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 moderate complexity (transformation operation), no annotations, no output schema, and 0% schema description coverage, the description is insufficiently complete. It doesn't explain what 'encode' means operationally, what format the output takes, or provide any examples. For a tool that modifies data, more context about behavior and results is needed for safe and effective 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 schema has 0% description coverage, so the description must compensate. It mentions 'HTML entities' which relates to the 'text' parameter, implying the tool takes text input and outputs encoded HTML. However, it doesn't explain parameter specifics like encoding standards (e.g., HTML5 entities), handling of invalid input, or examples. This provides basic context but falls short of fully compensating for the schema gap.
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 'Encode HTML entities' clearly states the verb ('encode') and resource ('HTML entities'), making the purpose immediately understandable. It distinguishes from sibling tools like 'decode_html' by specifying encoding rather than decoding. However, it doesn't explicitly differentiate from other encoding tools like 'encode_base64' or 'encode_url', 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 scenarios like sanitizing HTML input, preparing text for web display, or avoiding XSS attacks. With siblings like 'decode_html', 'format_html', and various string manipulation tools, the lack of usage context leaves the agent guessing about appropriate applications.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions encoding but doesn't specify whether this is a read-only operation, what encoding standard is used (e.g., percent-encoding), potential side effects, or error handling. This is inadequate for a tool that modifies data.
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 three words, front-loaded and without any wasted language. Every word ('Encode text for URLs') directly contributes to understanding the tool's purpose efficiently.
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 simplicity (1 parameter, no output schema, no annotations), the description is incomplete. It doesn't explain the encoding method, output format, or common use cases, which are essential for an agent to use this tool correctly alongside its many siblings.
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 description implies the 'text' parameter is the input to encode, which aligns with the single required parameter in the schema. Since there are 0 parameters with schema description coverage, the description adds meaningful context by clarifying what the parameter represents, though it doesn't detail encoding specifics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Encode text for URLs' clearly states the tool's function with a specific verb ('encode') and resource ('text for URLs'), but it doesn't distinguish this from its sibling decode_url or explain what URL encoding entails. It's not tautological but remains somewhat vague about the specific encoding method.
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_html, encode_base64, or decode_url. It lacks context about typical use cases, prerequisites, or exclusions, leaving the agent to infer usage 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Generate SHA-256 hash' but doesn't explain what this entails—e.g., whether it's a one-way cryptographic hash, if it's deterministic, what the output format is (hexadecimal string?), or any performance considerations. For a cryptographic tool with zero annotation coverage, this is a significant gap in transparency.
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 three words, front-loading the core functionality with zero wasted text. Every word ('Generate', 'SHA-256', 'hash') earns its place by directly contributing to understanding the tool's purpose, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a cryptographic hash function, no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain the output (e.g., a hex string), behavioral traits (e.g., deterministic, one-way), or usage context. For a tool that could have security implications, this minimal description is inadequate and leaves too much undefined.
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 1 parameter ('text') with 0% description coverage, so the schema provides no semantic information. The description doesn't mention parameters at all, failing to compensate for the low schema coverage. However, since there's only one parameter and the tool name implies it hashes text, the baseline is 3—adequate but with clear gaps, as it doesn't specify input constraints (e.g., text length, encoding).
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 SHA-256 hash' clearly states the verb 'Generate' and the resource 'SHA-256 hash', making the purpose immediately understandable. It distinguishes from siblings like generate_md5 or generate_sha1 by specifying the exact hash algorithm. However, it doesn't explicitly mention that it operates on text input, which would make it fully specific.
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 SHA-256 is preferred over other hash functions (e.g., MD5 for speed, SHA-512 for security) or how it differs from sibling tools like generate_hmac. There's no context about use cases such as data integrity checks, password hashing, or cryptographic applications.
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 generates a hash but doesn't mention whether this is a read-only operation, if it has side effects, performance characteristics, or what the output format looks like. For a cryptographic tool with zero 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 at just three words, with zero wasted language. It's front-loaded with the essential information and doesn't contain any unnecessary elaboration or repetition.
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 cryptographic hashing tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the hash output takes (hexadecimal, base64, etc.), whether the operation is deterministic, or any security considerations. The description should provide more complete context for proper tool usage.
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 description mentions 'SHA-512 hash' which implies text input, but doesn't explicitly describe the 'text' parameter beyond what the schema provides. With 0% schema description coverage and only one parameter, the description adds minimal semantic value. The baseline is appropriate given the simple parameter structure.
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 SHA-512 hash' clearly states the verb ('Generate') and resource ('SHA-512 hash'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like generate_md5, generate_sha1, or generate_sha256, which perform similar hashing operations with different algorithms.
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_md5 or generate_sha256, nor does it mention any prerequisites, constraints, or typical use cases for SHA-512 hashing. It simply states what the tool does without contextual usage information.
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 provides no information about error handling, performance characteristics, or what happens with malformed input. The description doesn't mention whether it handles percent-encoding, plus signs for spaces, or other URL encoding nuances.
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 maximally concise at just three words, with zero wasted language. It's front-loaded with the essential information and every word earns its place in communicating the core function.
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 decoding tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It doesn't explain what the tool returns (decoded string, error object, etc.), doesn't mention edge cases like malformed input, and provides no examples of proper usage.
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?
With 0% schema description coverage for the single 'text' parameter, the description adds meaningful context by specifying that the input should be 'URL-encoded text'. This clarifies the expected format beyond the basic string type indicated in the schema, though it could be more specific about encoding standards.
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 ('decode') and resource ('URL-encoded text'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'decode_base64' or 'decode_html', 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'decode_base64', 'decode_html', and 'encode_url', there's no indication of when URL decoding is appropriate versus other decoding operations or when to use the complementary encoding tool.
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 transformation behavior but lacks details on edge cases (e.g., handling of special characters, whitespace, or locale effects), error conditions, or output format specifics. The description is minimal and doesn't compensate for the absence of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple transformation tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers the basic purpose but fails to address parameter meanings, behavioral nuances, or output details, leaving significant gaps for a tool with multiple optional inputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but doesn't mention any parameters. It ignores 3 optional parameters (delimiter, locale, mergeAmbiguousCharacters) and provides no context beyond the required 'text' parameter. This leaves most parameters undocumented and unclear in purpose.
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 'Convert text to CONSTANT_CASE' clearly states the verb ('Convert') and resource ('text') with specific output format ('CONSTANT_CASE'). It effectively distinguishes from siblings like case_to_camel, case_to_snake, etc., by specifying the exact case transformation target.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for converting text to constant case, but provides no explicit guidance on when to use this tool versus alternatives (e.g., case_to_snake for snake_case) or any prerequisites. Context is implied by the tool name and sibling list, but no explicit when/when-not statements are included.
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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