Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TextToolkit convert 'hello world' to camelCase"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Transform, format, and analyze text without leaving your AI assistant workflow.
Tool | Description |
case_to_camel | Convert text to camelCase |
case_to_pascal | Convert text to PascalCase |
case_to_snake | Convert text to snake_case |
case_to_kebab | Convert text to kebab-case |
case_to_constant | Convert text to CONSTANT_CASE |
case_to_dot | Convert text to dot.case |
case_to_no | Convert text to no case |
case_to_pascal_snake | Convert text to Pascal_Snake_Case |
case_to_path | Convert text to path/case |
case_to_sentence | Convert text to Sentence case |
case_to_train | Convert text to Train-Case |
case_to_capital | Convert text to Capital Case |
encode_base64 | Encode text to Base64 |
decode_base64 | Decode Base64 to text |
encode_url | Encode text for URLs |
decode_url | Decode URL-encoded text |
encode_html | Encode HTML entities |
decode_html | Decode HTML entities |
format_json | Format and beautify JSON |
format_xml | Format and beautify XML |
format_sql | Format and beautify SQL |
format_html | Format and beautify HTML |
count_characters | Count characters in text |
count_words | Count words in text |
count_lines | Count lines in text |
analyze_readability | Calculate readability metrics |
string_trim | Trim whitespace from text |
string_substring | Extract a substring |
string_replace | Replace text |
string_split | Split text into an array |
string_join | Join an array into text |
generate_uuid | Generate a UUID |
validate_uuid | Validate a UUID |
generate_md5 | Generate MD5 hash |
generate_sha1 | Generate SHA-1 hash |
generate_sha256 | Generate SHA-256 hash |
generate_sha512 | Generate SHA-512 hash |
generate_hmac | Generate HMAC hash |
generate_lorem_ipsum | Generate lorem ipsum text |
regex_test | Test a regex pattern against text |
regex_replace | Replace text using a regex pattern |
regex_extract | Extract matches using a regex pattern |
regex_split | Split text using a regex pattern |
Related MCP server: agent-utils-mcp
Convert text between camelCase, PascalCase, snake_case, kebab-case
Support for CONSTANT_CASE, dot.case, no case, Pascal_Snake_Case
Additional formats: path/case, Sentence case, Train-Case, Capital Case
Customizable with delimiter and locale options
Base64 encoding and decoding
URL encoding and decoding
HTML entity encoding and decoding
Secure and reliable conversions
JSON formatting with customizable indentation
XML formatting and pretty-printing
SQL query formatting and standardization
HTML code beautification
Character counting (with and without spaces)
Word counting with accurate tokenization
Line counting for multi-line text
Readability scoring (Flesch-Kincaid, etc.)
Trim whitespace (start, end, or both)
Extract substrings with precise control
Replace text with powerful options
Split text into arrays and join arrays into text
Generate UUIDs (v1, v4, v5, or nil)
Validate existing UUIDs
Customizable options for all UUID versions
MD5 hash generation
SHA-1, SHA-256, and SHA-512 hashing
HMAC hash generation with custom keys
Secure cryptographic functions
Generate placeholder text
Customizable length and format
Control paragraph and sentence structure
Test regex patterns against text
Replace text using regex patterns
Extract matches using regex patterns
Split text using regex patterns
Node.js 16.x or higher - The TextToolkit MCP server requires Node.js 16+ to run properly.
To run the TextToolkit MCP server using Node.js npx, use the following command:
npx -y @cicatriz/text-toolkit@latestTo add this server to Cursor IDE:
Go to Cursor Settings > MCP
Click + Add new Global MCP Server
Add the following configuration to your global .cursor/mcp.json file:
{
"mcpServers": {
"text-toolkit": {
"command": "npx",
"args": [
"-y",
"@cicatriz/text-toolkit"
]
}
}
}See the Cursor documentation for more details.
To set up MCP with Cascade, navigate to Windsurf - Settings > Advanced Settings or Command Palette > Open Windsurf Settings Page.
Scroll down to the Cascade section and add the TextToolkit MCP server directly in mcp_config.json:
{
"mcpServers": {
"text-toolkit": {
"command": "npx",
"args": [
"-y",
"@cicatriz/text-toolkit"
]
}
}
}Add the following JSON manually to your cline_mcp_settings.json via Cline MCP Server setting:
{
"mcpServers": {
"text-toolkit": {
"command": "npx",
"args": [
"-y",
"@cicatriz/text-toolkit"
]
}
}
}Access the MCP settings by clicking Edit MCP Settings in Roo Code settings or using the Roo Code: Open MCP Config command in VS Code's command palette:
{
"mcpServers": {
"text-toolkit": {
"command": "npx",
"args": [
"-y",
"@cicatriz/text-toolkit"
]
}
}
}Add the following to your claude_desktop_config.json file:
{
"mcpServers": {
"text-toolkit": {
"command": "npx",
"args": [
"-y",
"@cicatriz/text-toolkit"
]
}
}
}See the Claude Desktop documentation for more details.
You can also run it as CLI by running the following command:
npx -y @cicatriz/text-toolkit@latest# Install globally from npm
npm install -g @cicatriz/text-toolkit
# Run the server
text-toolkit# Clone the repository
git clone https://github.com/Cicatriiz/text-toolkit.git
cd text-toolkit
# Install dependencies
npm install
# Build the project
npm run build
# Run the server
node dist/index.js# Start in stdio mode (default)
text-toolkit
# Start in SSE mode on a specific port
text-toolkit --sse --port=8000
# Display version
text-toolkit --versionYou can test the TextToolkit MCP server using the MCP Inspector:
# Install the MCP Inspector
npm install -g @modelcontextprotocol/inspector
# Run the inspector against your server
mcp-inspector @cicatriz/text-toolkitThe repository includes example scripts demonstrating how to use the TextToolkit MCP server:
# Run the case transformation example
node examples/case-transform-example.jsThis example demonstrates how to:
Connect to the TextToolkit MCP server
List available tools
Transform text to different cases
Use custom options for transformations
Converts text to camelCase.
Parameters:
text: The text to transform
delimiter (optional): The character to use between words
locale (optional): Locale for case conversion
mergeAmbiguousCharacters (optional): Whether to merge ambiguous characters
Example:
{
"text": "hello world test"
}Response:
{
"result": "helloWorldTest"
}Converts text to PascalCase.
Parameters:
text: The text to transform
delimiter (optional): The character to use between words
locale (optional): Locale for case conversion
mergeAmbiguousCharacters (optional): Whether to merge ambiguous characters
Example:
{
"text": "hello world test"
}Response:
{
"result": "HelloWorldTest"
}Encodes text to Base64.
Parameters:
text: The text to encode
Example:
{
"text": "hello world"
}Response:
{
"result": "aGVsbG8gd29ybGQ="
}Decodes Base64 to text.
Parameters:
text: The Base64 string to decode
Example:
{
"text": "aGVsbG8gd29ybGQ="
}Response:
{
"result": "hello world"
}Formats and beautifies JSON.
Parameters:
text: The JSON text to format
indent_size (optional): Number of spaces for indentation (1-8). Defaults to 2.
Example:
{
"text": "{\"name\":\"John\",\"age\":30,\"city\":\"New York\"}",
"indent_size": 4
}Response:
{
"result": "{\n \"name\": \"John\",\n \"age\": 30,\n \"city\": \"New York\"\n}"
}Counts characters in text.
Parameters:
text: The text to analyze
Example:
{
"text": "hello world"
}Response:
{
"total_characters": 11,
"characters_without_spaces": 10
}Replaces text.
Parameters:
text: The text to perform replacements on
search: The string to search for
replace: The string to replace with
replace_all (optional): Whether to replace all occurrences. Defaults to true.
Example:
{
"text": "hello world",
"search": "world",
"replace": "universe"
}Response:
{
"result": "hello universe"
}Generates a UUID.
Parameters:
version (optional): UUID version to generate (v1, v4, v5, nil). Defaults to v4.
namespace (optional): Namespace for v5 UUID (required for v5)
name (optional): Name for v5 UUID (required for v5)
uppercase (optional): Whether to return the UUID in uppercase. Defaults to false.
Example:
{
"version": "v4"
}Response:
{
"uuid": "f47ac10b-58cc-4372-a567-0e02b2c3d479"
}Generates lorem ipsum text.
Parameters:
count (optional): Number of units to generate. Defaults to 5.
units (optional): Type of units to generate (words, sentences, paragraphs). Defaults to sentences.
paragraphLowerBound (optional): Minimum sentences per paragraph. Defaults to 3.
paragraphUpperBound (optional): Maximum sentences per paragraph. Defaults to 7.
sentenceLowerBound (optional): Minimum words per sentence. Defaults to 5.
sentenceUpperBound (optional): Maximum words per sentence. Defaults to 15.
format (optional): Output format (plain, html). Defaults to plain.
Example:
{
"count": 2,
"units": "sentences"
}Response:
{
"text": "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut hendrerit ipsum in nulla euismod, vel ultrices nisi tincidunt."
}Tests a regex pattern against text.
Parameters:
text: The text to test against the pattern
pattern: The regex pattern to test
flags (optional): Regex flags (e.g., 'g', 'i', 'gi'). Defaults to 'g'.
Example:
{
"text": "hello world",
"pattern": "\\w+",
"flags": "g"
}Response:
{
"matches": ["hello", "world"],
"match_count": 2,
"is_match": true
}"Convert 'hello world' to camelCase"
"Encode 'hello world' to Base64"
"Format this JSON: {"name":"John","age":30}"
"Count the characters in 'hello world'"
"Replace 'world' with 'universe' in 'hello world'"
"Generate a UUID"
"Generate 2 sentences of lorem ipsum"
"Test if 'hello world' matches the regex pattern '\w+'"
"Convert 'Hello World' to snake_case"
"Decode this Base64 string: aGVsbG8gd29ybGQ="
TextToolkit is built using the Model Context Protocol (MCP) specification, which allows it to integrate seamlessly with Claude Desktop and other MCP-compatible clients. The server is implemented in TypeScript and uses the following architecture:
Core Services: Implements text transformation, encoding/decoding, and formatting
MCP Server: Handles JSON-RPC requests from clients
Utility Functions: Provides helper functions for text operations
Testing Framework: Includes comprehensive test scripts for verifying functionality
change-case - For case transformation operations
crypto-js - For hash generation
js-beautify - For code formatting and beautification
uuid - For UUID generation and validation
lorem-ipsum - For placeholder text generation
@modelcontextprotocol/sdk - For MCP server implementation
Node.js 16.x or higher
npm 7.x or higher
Contributions are welcome! Please feel free to submit a Pull Request.
Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
TextToolkit includes comprehensive test scripts that can be used to verify the functionality of the MCP server. The test scripts can be used to test both stdio and SSE modes.
# Test stdio mode
node test-comprehensive.js
# Test SSE mode
node test-sse-comprehensive.jsTextToolkit processes all data locally and does not send any information to external servers. Your text data and queries remain private on your device.
MIT
Cicatriz
Calculate readability metrics
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It only mentions 'calculate readability metrics' but does not indicate whether the tool is read-only, has output format constraints, or any side effects. This lack of detail reduces transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short ('Calculate readability metrics'), which is concise and front-loaded. However, it may be overly brief, missing details that could be added without verbosity. Nonetheless, it wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's single parameter and no output schema or annotations, the description is incomplete. It does not explain what readability metrics are returned, the format of the output, or any constraints like text length. A minimally complete description would include examples of metrics computed or output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a single parameter 'text' described as 'The text to analyze'. The description adds no additional semantic meaning beyond the schema, which is sufficient for this simple parameter. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool calculates readability metrics, which is a clear verb+noun. However, it lacks specificity about which metrics are computed (e.g., Flesch-Kincaid, Coleman-Liau). Given the sibling tools are primarily case conversions and string operations, this tool's purpose is distinct but not detailed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, when not to use it, or any prerequisites. The description merely states what it does without any contextual usage information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to camelCase
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to camelCase | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states the conversion operation without disclosing behavioral traits like idempotency, side effects (none expected), or handling of edge cases. The optional parameters (delimiter, locale, mergeAmbiguousCharacters) are not mentioned in the description, leaving the agent to infer behavior solely from the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (three words), which is efficient but lacks necessary detail. It front-loads the purpose but fails to include any context about parameters or usage, making it less helpful than a slightly longer but more informative description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no output schema, and no annotations, the description is woefully incomplete. It does not explain return format, behavior with optional parameters, or edge cases. The agent would have to rely entirely on the schema and trial/error, which is insufficient for safe and correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds no additional meaning beyond what the parameter descriptions already provide. It does not explain how parameters like delimiter or locale affect the conversion, but the schema descriptions are adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Convert text to camelCase' uses a specific verb ('Convert') and identifies the resource ('text' to 'camelCase'). It clearly distinguishes this tool from siblings like case_to_kebab or case_to_snake by 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.
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 (e.g., case_to_pascal, case_to_lower). It lacks any explanation of prerequisites, input constraints, or when to prefer other case converters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to Capital Case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to Capital Case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits beyond the basic purpose. It fails to mention how locale or mergeAmbiguousCharacters affect output, error handling, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It is appropriately sized for a straightforward text transformation tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters, no output schema, and no annotations, the description is too minimal. It does not clarify what 'Capital Case' means (e.g., first letter uppercase per word? all words?), nor does it explain optional parameters like locale or delimiter meaningfully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond what the schema already provides for each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action ('Convert') and the resource ('text') and the result ('Capital Case'). It effectively distinguishes from numerous sibling tools that convert to other cases like camel, snake, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. With many sibling case-conversion tools, the description should indicate scenarios where capital case is appropriate or how it differs from similar tools like case_to_sentence or case_to_title.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to CONSTANT_CASE
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to CONSTANT_CASE | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
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 only states the basic conversion, omitting details such as how it handles edge cases (empty strings, Unicode), whether the conversion is idempotent, or what happens with special characters and whitespace.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence) and front-loaded, which is efficient for a simple tool. However, it could be slightly expanded to include a brief example or note on common usage without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, the description fails to mention what the tool returns (the converted string). It also does not explain the purpose of the three optional parameters or their typical usage, leaving the agent without full context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of parameters, providing baseline meaning. The tool description adds no additional context beyond the schema, such as how the 'delimiter' interacts with word boundaries or the effect of 'locale' on characters with language-specific mappings. Thus, it meets the minimal bar but does not enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Convert text') and the target format ('CONSTANT_CASE'), which is a specific verb+resource. The name itself is self-explanatory, and among sibling case conversion tools (e.g., case_to_camel, case_to_snake), this description uniquely identifies its purpose without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool over alternatives like case_to_snake or case_to_kebab. It does not mention that CONSTANT_CASE is an uppercase variant of snake_case, nor does it specify contexts where this conversion is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to dot.case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to dot.case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It does not disclose any behavioral traits such as handling of special characters, numbers, or whitespace. The behavior is implied but not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, highly concise and front-loaded. It contains no extraneous information, but could slightly elaborate on the nature of dot.case without harming brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of output schema and annotations, the description is brief. It adequately conveys the tool's purpose for simple conversion, but lacks details on return format and edge cases. Adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter descriptions in the schema already cover all four parameters completely (100% coverage). The tool description adds no additional semantic value beyond what is in the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Convert text to dot.case', indicating a specific verb and resource. It distinguishes from siblings by mentioning the specific case style, but does not define dot.case, which may be ambiguous to some agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like case_to_camel or case_to_kebab. The description does not provide context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to kebab-case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to kebab-case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It does not disclose behavior such as lowercase conversion, hyphens as separators, or treatment of special characters. The description is too brief for an agent to understand side effects or edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no waste. It efficiently communicates the core purpose, though it could benefit from slight expansion. Still, conciseness is well-executed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description is incomplete. It does not explain the resulting format or provide examples. For a tool with multiple parameters, it should offer more context to ensure correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers 100% of parameters with descriptions. The description adds no value beyond what the schema already provides, but baseline is 3 when coverage is high. The schema's descriptions are adequate, so no deduction.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Convert text to kebab-case' clearly states the verb and resource. It distinguishes from sibling case converters by specifying the target case, though it could elaborate on what kebab-case entails (lowercase with hyphens).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like case_to_snake or case_to_camel. The description does not provide context for selection, leaving the agent to infer based solely on the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to no case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to no case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only says 'convert' without explaining what 'no case' entails (e.g., preserves spaces? lowercase? removes special characters?). No side effects or traits beyond the vague conversion are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (4 words), which is concise but at the cost of clarity. It is front-loaded but fails to convey essential information, making it inefficient for an agent's decision-making.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, many siblings, and no output schema, the description is incomplete. It does not explain the transformation behavior, expected output format, or how parameters like 'delimiter' affect the result in the 'no case' context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage with descriptions for all 4 parameters. The description adds no additional meaning beyond the schema, meeting the baseline expectation. No extra context is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Convert text to no case' which is a specific verb and resource, but 'no case' is ambiguous and not defined. Given the sibling tools include many case conversion styles, this description does not clearly distinguish what 'no case' means (e.g., lowercase with spaces? no transformation?). It is better than a tautology but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the many alternative case conversion tools. There is no mention of contexts, prerequisites, or comparisons to siblings, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to PascalCase
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to PascalCase | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as handling of special characters, error conditions, or the impact of optional parameters. The description is insufficient for understanding the tool's full behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, consisting of a single sentence that directly conveys the tool's purpose. No extraneous information is included.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having four parameters and no output schema, the description fails to explain the role of optional parameters (delimiter, locale, mergeAmbiguousCharacters) or the expected return format. The description is too minimal for this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters. The description adds no additional meaning beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Convert text to PascalCase', which clearly identifies the tool's function. The name 'case_to_pascal' combined with the description distinguishes it from sibling tools like case_to_camel or case_to_kebab.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks any context about prerequisites, limitations, or comparison to other case conversion tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to Pascal_Snake_Case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to Pascal_Snake_Case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
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 fails to mention any behavioral traits such as handling of special characters, locale sensitivity, or the effect of optional parameters like delimiter and mergeAmbiguousCharacters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence. However, it could include a brief example or hint about the output format without significant bloat, but it is well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is too sparse for a tool with multiple optional parameters and no output schema. It does not explain what Pascal_Snake_Case looks like (e.g., 'HelloWorld' → 'Hello_World'), nor does it clarify parameter usage or return behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond the schema, resulting in a baseline score of 3 with no bonus for extra detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Convert' and the target format 'Pascal_Snake_Case'. The tool name itself distinguishes it from siblings like case_to_pascal and case_to_snake, making its unique purpose evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus other case converters (e.g., case_to_snake, case_to_pascal). There is no mention of use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to path/case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to path/case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description is too short to disclose any behavioral traits, such as how ambiguous characters are handled or locale effects. The tool's behavior is opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is under-specified; it is too short to be helpful. While concise, it omits crucial details, making it ineffective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description should provide more context. It does not explain the resulting path/case format, nor does it address the 4 parameters adequately. Sibling tools suggest a case conversion family, but this one's purpose remains unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description doesn't need to add much parameter meaning. It adds no extra value beyond the schema, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description says 'Convert text to path/case' which is a verb+resource but does not clarify what 'path/case' means, especially given many sibling tools like case_to_kebab, case_to_snake, etc. It fails to differentiate itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like case_to_kebab or case_to_dot. The description provides no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to Sentence case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to Sentence case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present. The description only says 'Convert text to Sentence case' without disclosing behavioral traits such as handling of punctuation, numbers, or special characters. For a transformation tool, more detail is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, very concise and to the point. No wasted words. However, it could be slightly more informative without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description should compensate. The 4 parameters are not explained beyond their schema descriptions. For a conversion tool, this is minimally adequate but not rich in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all parameters have descriptions), so the baseline is 3. The tool description adds no additional meaning beyond what the schema provides, so score remains at 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'convert' and the resource 'text' to 'Sentence case'. It distinguishes itself from sibling case conversion tools like case_to_camel, case_to_kebab, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The sibling tools list many case converters, but there is no indication of which one to choose or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to snake_case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to snake_case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden of behavioral disclosure. It does not mention side effects, error handling, locale behavior, or edge cases. The description adds no behavioral context beyond the basic transformation stated in 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded and contains zero waste. It efficiently communicates the core purpose without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 100% schema coverage, the description lacks details about conversion algorithm, locale sensitivity, delimiter behavior, and output format (no output schema). For a tool among many similar siblings, this brevity reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, with each parameter having a clear description (e.g., 'The text to transform', 'Locale for case conversion'). The tool description does not add extra meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Convert text to snake_case' clearly states the verb and resource, but does not differentiate from sibling tools like case_to_camel or case_to_kebab, all of which have similar phrasing. The name itself is specific, so clarity is high but not maximized.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, no context about prerequisites or exclusions. With many sibling case-conversion tools, explicit usage guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to Train-Case
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to transform to Train-Case | |
| delimiter | No | The character to use between words (optional) | |
| locale | No | Locale for case conversion (optional) | |
| mergeAmbiguousCharacters | No | Whether to merge ambiguous characters (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description does not disclose behavioral aspects like handling of special characters, edge cases, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, concise, and front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks explanation of what Train-Case is and how it differs from other case styles; incomplete for an agent to choose effectively among many siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are documented; description adds no extra meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states converting text to Train-Case, but doesn't differentiate from similar siblings like case_to_kebab or case_to_camel.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over other case converters or text transformations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Count characters in text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is minimal and does not disclose behavioral details beyond the basic operation. Without annotations, the agent doesn't know if it counts all characters (including whitespace, newlines) or only visible characters. Edge cases like empty strings or special characters are not mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with no wasted words. For such a simple tool, this is acceptable, though it could benefit from slightly more detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward counting tool with a single parameter and no output schema, the description is mostly adequate but lacks specifics about what constitutes a character. More detail on counting behavior would improve completeness, especially given similar sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'text', and the description adds the purpose of the parameter indirectly. However, the description does not add any information beyond what the schema already provides ('The text to analyze'). Baseline 3 is appropriate as no extra context is given.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool counts characters in text, which is specific and directly matches the tool name. However, it doesn't differentiate from similar sibling tools like count_words or count_lines, leaving some ambiguity about what qualifies as a character (e.g., spaces, punctuation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus similar counting tools. Given siblings like count_words and count_lines, the description lacks instructions on what makes this tool appropriate for character counting instead of word or line counting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Count lines in text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavior, but it does not specify what constitutes a 'line' (e.g., handling of empty strings or trailing newlines) or the return value format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no unnecessary words, fitting the simplicity of the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description is mostly sufficient but lacks details on edge cases (e.g., empty input) and return value, which reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the schema already describes the 'text' parameter. The description adds minimal extra meaning beyond echoing 'in text', so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Count lines in text' uses a specific verb (count) and resource (lines), clearly distinguishing it from sibling tools like count_characters and count_words.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool over alternatives such as count_characters or count_words, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Count words in text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as what constitutes a word (e.g., whitespace-separated) or the return format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with one sentence, but could benefit from including essential details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and lack of output schema, the description should explain the return value (e.g., a word count number) but does not.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with one parameter described. The description adds no additional meaning beyond the schema's description of 'text'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Count words in text' clearly states the verb and resource, but does not differentiate from sibling tools like count_characters or count_lines.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Sibling tools like count_characters and count_lines exist, but no exclusions or context are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decode Base64 to text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It does not disclose error handling, output format, or behavior on invalid input (e.g., non-Base64 strings).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, front-loading the core purpose. However, it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a straightforward decode tool with one parameter and no output schema, the description is adequate but lacks details on input validation and error handling, which are relevant for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the schema description for 'text' says 'The text to encode or decode', which is misleading for a decode-only tool. The description adds no further clarification, so the parameter semantics are unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Decode Base64 to text' clearly states the action (decode) and the resource (Base64 to text). It effectively distinguishes this tool from its sibling 'encode_base64'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over alternatives like encode_base64, nor any prerequisites or input format requirements. 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.
Decode HTML entities
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only states 'Decode HTML entities'. It does not explain what entities are supported, error handling, or return value. The parameter description in the schema is misleading (suggests encode/decode), and the tool description does not correct it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one sentence) and front-loaded, but it lacks necessary detail. It is not overly verbose, but the brevity sacrifices information that would be helpful for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema and no annotations, the description is incomplete. It does not mention return value, supported entities, or edge cases. The parameter description is confusing. More detail is needed for adequate context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. However, the schema parameter description says 'encode or decode', which is partially inaccurate for a decode tool. The tool description adds no clarification or additional meaning beyond the schema, so the parameter semantics are not improved and are slightly misleading.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool decodes HTML entities, which is a specific verb and resource. However, it does not distinguish itself from sibling tools like encode_html or decode_url, but the purpose is still clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, contexts, or situations where this tool is not appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decode URL-encoded text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry full behavioral transparency. It does not disclose edge cases (e.g., handling of invalid encoding), character set assumptions, or return value format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous words, achieving maximal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description covers the core action but omits expected output and potential error conditions, making it minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'text' has a schema description that says 'encode or decode', which is ambiguous for a decode-only tool. This adds confusion rather than clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool decodes URL-encoded text, using a specific verb and resource. It directly distinguishes from the sibling encode_url.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when needing to decode URL-encoded strings, but provides no explicit guidance on when to avoid or when alternatives (like decode_base64) are better.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Encode text to Base64
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It only states 'encode' but the schema description says 'encode or decode', causing confusion. No details on input encoding, output format, or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the purpose. It is efficient but could be slightly expanded to cover key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of many sibling tools and no output schema, the description is too minimal. It fails to specify that the tool does not decode, or what the output looks like, leaving uncertainty.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'text' parameter, and the description adds no additional meaning beyond the schema's description. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Encode' and the object 'text to Base64', which is specific and distinguishes it from siblings like decode_base64 or encode_html.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as decode_base64 or other encoding tools. The agent is left to 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.
Encode HTML entities
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The main description only says 'Encode HTML entities', but the input schema parameter description says 'The text to encode or decode', creating a contradiction about whether the tool also decodes. With no annotations, this inconsistency undermines behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, front-loading the core purpose. However, it is too brief and could benefit from a sentence clarifying the operation, especially given the parameter confusion.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter tool with no output schema, the description is insufficient. It fails to resolve the encode/decode ambiguity or specify behavior like which entities are encoded, leaving the agent with incomplete information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters, so a baseline of 3 applies. However, the parameter description 'encode or decode' contradicts the tool purpose, adding confusion instead of clarity. The description adds no meaningful semantic value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Encode HTML entities' clearly states the action (encode) and resource (HTML entities), distinguishing it from siblings like decode_html. However, it is slightly generic and could specify the exact transformation (e.g., escaping special characters).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like encode_url or decode_html. The description does not mention prerequisites or exclusions, leaving the agent to 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.
Encode text for URLs
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description minimally conveys URL encoding but lacks specifics on character handling or behavior for invalid input. It is adequate but not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, immediately front-loads the action and target. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple encoding tool with one parameter, the description is mostly complete. It could clarify the encoding scheme (e.g., percent encoding) but is sufficient given sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not add extra meaning beyond the schema, which already has 100% coverage. The schema's parameter description (encode or decode) is broader than the tool's purpose, but this is not addressed in the tool description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Encode text for URLs' clearly states the action (encode) and the target resource (text for URLs), distinguishing it from siblings like decode_url and other encoding tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as decode_url, encode_base64, or other string transformation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Format and beautify HTML
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to format | |
| indent_size | No | Number of spaces for indentation (1-8) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only says 'Format and beautify HTML'. It does not disclose that the operation is likely read-only, whether malformed HTML is handled, or any side effects. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at 4 words, with no wasted text. While short, it effectively conveys the core purpose, though a bit more detail could improve clarity without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool, the description lacks critical details such as what 'beautify' means (e.g., indentation, line breaks), whether the operation is idempotent, or prerequisites like valid HTML. This leaves gaps for an agent assessing tool suitability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and both parameters have clear descriptions in the schema. The tool description adds no additional meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Format' and the resource 'HTML', distinguishing it from siblings like format_json, format_sql, and format_xml. However, it could be more specific about what beautification entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For instance, it does not mention when to prefer format_html over encode_html or decode_html, leaving the agent without decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Format and beautify JSON
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to format | |
| indent_size | No | Number of spaces for indentation (1-8) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lacks any behavioral details. It does not disclose what happens with invalid input, whether it preserves comments or key order, or any side effects. With no annotations, the description should cover these aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Although very short, the description is under-specified. It sacrifices necessary information for brevity, leading to incompleteness. A good description would be more informative while still concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no output schema, the description is incomplete. It fails to address important aspects like error handling, validation behavior, or what 'beautify' entails. More context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of parameters with descriptions. The tool description adds no extra meaning beyond what the schema already provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: formatting and beautifying JSON. It uses a specific verb and resource and is distinguishable from sibling tools like format_html or format_sql.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. There is no mention of contexts where it is appropriate or inappropriate, nor any reference to other tools for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Format and beautify SQL
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to format | |
| indent_size | No | Number of spaces for indentation (1-8) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only states the action without disclosing behavioral traits such as error handling, safety, or reversibility. For a formatting tool, it should clarify if invalid SQL is accepted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise phrase, front-loaded with the key action. However, it could be slightly more descriptive without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and no output schema, the description is mostly adequate but lacks details on invalid input handling and does not mention the default indentation behavior beyond schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both parameters described. The description adds no extra meaning beyond the schema, so baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool formats and beautifies SQL, distinguishing it from siblings like format_html and format_json.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like other formatters or text tools. It does not mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Format and beautify XML
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to format | |
| indent_size | No | Number of spaces for indentation (1-8) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description should compensate by disclosing behavioral traits. It does not mention error handling for malformed XML, whether comments are preserved, or if the tool is idempotent. The description is too minimal for a tool with no structured metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no unnecessary words. It is appropriately concise for a simple tool, though it could benefit from slightly more structure (e.g., mentioning expected output).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no output schema), the description is too brief. It does not explain what the output is (formatted XML string), how errors in input XML are handled, or any side effects. A more complete description would include these details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description does not need to add much. The schema already documents both parameters (text, indent_size) with types, defaults, and constraints. The description adds no additional meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Format and beautify XML' clearly states the action (format/beautify) and the resource (XML). It is specific and distinguishes the tool from siblings like format_json or format_html by naming XML explicitly. However, it does not elaborate on scope or disambiguation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., format_html for HTML). The description lacks context about prerequisites, limitations, 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.
Generate HMAC hash
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to hash | |
| key | Yes | The secret key for HMAC | |
| algorithm | No | The hashing algorithm to use | SHA256 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. 'Generate HMAC hash' accurately describes the core behavior but omits details like algorithm default, return format, or any side effects. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded, no wasted words. Extremely concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple hash tool with no output schema, the description covers the essential purpose. Could be slightly more complete by hinting at key usage, but it's functional.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds no extra meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate HMAC hash' clearly states the tool's action and resource. It distinguishes from sibling hash tools (e.g., generate_md5, generate_sha256) by specifying HMAC (keyed hashing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. Does not mention that HMAC requires a secret key, unlike other hash tools. No when-not-to-use or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate lorem ipsum text
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Number of units to generate | |
| units | No | Type of units to generate | sentences |
| paragraphLowerBound | No | Minimum sentences per paragraph | |
| paragraphUpperBound | No | Maximum sentences per paragraph | |
| sentenceLowerBound | No | Minimum words per sentence | |
| sentenceUpperBound | No | Maximum words per sentence | |
| format | No | Output format | plain |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description does not disclose behavioral traits such as idempotency, output format, or side effects. It merely restates the tool's basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise but lacks any structure or additional context. Every sentence should earn its place, and this single sentence does not provide sufficient information for a tool with 7 parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no output schema, no annotations), the description is incomplete. It does not explain what lorem ipsum is used for or what the return value looks like, leaving the agent to rely solely on the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond the basic function, earning a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate lorem ipsum text' clearly states the action (generate) and the resource (lorem ipsum text), and it distinguishes this tool from siblings, as no other sibling generates lorem ipsum.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs. alternatives, nor does it specify any prerequisites or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate MD5 hash
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to hash |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It fails to disclose output characteristics (e.g., 32-character hex string) or that the operation is deterministic and non-destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no redundancy. It earns its place, though additional details would improve completeness without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, no output schema), the description is minimally adequate but omits output format and behavior details, reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'text', and the description adds no new meaning beyond what the schema already provides. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate MD5 hash' clearly states the tool's action and resource. It distinguishes the tool from siblings like generate_sha256 by name, but does not explicitly differentiate usage scenarios.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use MD5 versus alternative hash tools (e.g., SHA256, HMAC). The description lacks any context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate SHA-1 hash
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to hash |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a terse description, the agent gets no insight into behavioral traits such as output format (hex, base64), determinism, or any side effects, leaving the tool's behavior partially unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no wasted words. However, it is too brief to add valuable context, slightly lowering the score from a perfect 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of many sibling hash tools, the lack of output format details (e.g., hex string length) and usage context makes the description incomplete for an agent to correctly interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully describes the single parameter (text) with a clear description. The tool description adds no additional meaning, meeting the baseline for 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate SHA-1 hash' clearly states the action (generate) and the specific resource (SHA-1 hash), distinguishing it from sibling tools like generate_md5 or generate_sha256.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use SHA-1 versus other hash algorithms, nor any context about trade-offs like security or performance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate SHA-256 hash
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to hash |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only states the basic function. There is no disclosure of output format (e.g., hex string), side effects, or performance characteristics. With no annotations, the description carries the full burden but fails to elaborate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately conveys the tool's purpose. No extraneous words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema), the description is minimal but omits what the output is (e.g., a hex digest) and any constraints on input length. This missing context could lead to confusion.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'text' is fully described in the input schema. The description adds no further semantic value beyond what the schema already provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Generate' and the specific resource 'SHA-256 hash'. It is distinct from sibling tools which cover other hash algorithms or text transformations, leaving no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like generate_md5 or generate_sha1. There is no mention of context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate SHA-512 hash
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to hash |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states 'Generate SHA-512 hash' with no information about return format, idempotency, edge cases, or side effects. This is insufficient for an agent to understand 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (5 words) but lacks essential information such as output format or usage notes. Conciseness without completeness is under-specification, not effective efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter hash generation tool with no output schema, the description should mention the output format (e.g., hex-encoded) or any constraints. Without this, the agent has incomplete understanding of the tool's contract.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with a description for the single 'text' parameter. The tool description adds no additional meaning beyond the schema; baseline score of 3 is appropriate as the schema itself is clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the specific operation 'generate SHA-512 hash', which is clear but does not differentiate from sibling hash tools like generate_sha1 or generate_sha256. The name itself provides the distinction, but the description adds no additional context to help choose between them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, no prerequisites, no context for choosing SHA-512 over other hash algorithms. The description is silent on usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a UUID
| Name | Required | Description | Default |
|---|---|---|---|
| version | No | UUID version to generate | v4 |
| namespace | No | Namespace for v5 UUID (required for v5) | |
| name | No | Name for v5 UUID (required for v5) | |
| uppercase | No | Whether to return the UUID in uppercase |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It only says 'Generate a UUID' without disclosing randomness, determinism, side effects, or any important constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no waste. It could be slightly expanded for context, but it is efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters and no output schema, the description is too minimal. It provides no overview of version differences, required parameters for v5, or return format, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the tool description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate a UUID' clearly states the action and output. However, it does not differentiate from sibling tools like validate_uuid or other generation tools, but the name itself is specific enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as validate_uuid. No information on version selection or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract matches using a regex pattern
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to extract from | |
| pattern | Yes | The regex pattern with capture groups | |
| flags | No | Regex flags (e.g., 'g', 'i', 'gi') | g |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not explain whether all matches or only first match is returned, how capture groups are handled, or the effect of flags. The minimal description leaves significant ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but lacks structure. It could include more details without being verbose, such as specifying return format or flag defaults.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a regex extraction tool without an output schema, the description does not explain the return format (array of strings, groups, etc.). It also does not account for edge cases or behavior with no matches.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all three parameters with descriptions, achieving 100% coverage. The description adds minor context ('with capture groups') but does not significantly enhance understanding beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Extract matches using a regex pattern' clearly states the action (extract) and resource (matches via regex), and it distinguishes this tool from siblings like regex_replace or regex_split which perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as regex_test or regex_split. It lacks context on 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.
Replace text using a regex pattern
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to perform replacements on | |
| pattern | Yes | The regex pattern to match | |
| replacement | Yes | The replacement string | |
| flags | No | Regex flags (e.g., 'g', 'i', 'gi') | g |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits like global vs first-match replacement (though default flag 'g' is in schema), behavior on no match, return value format, and potential performance considerations. It does none of these.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. It could be slightly expanded to include guidance or transparency without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and no annotations, the description is too brief. It lacks information about return values, edge cases (e.g., no match), and usage context, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions. The description adds no extra meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (replace), the resource (text), and the method (regex pattern). It distinguishes from sibling tools like string_replace (non-regex) and regex_extract (extract matches), making it easy to select the correct tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. For example, it does not mention that for simple string replacements without regex, string_replace might be more appropriate, or that regex_test could be used for validation first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Split text using a regex pattern
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to split | |
| pattern | Yes | The regex pattern to split by | |
| flags | No | Regex flags (e.g., 'i') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only states 'Split text using a regex pattern', omitting details like return type (array? strings?), error handling, empty match handling, or potential performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no unnecessary words. Front-loaded with the action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of a split operation and the rich set of sibling tools, the description is minimally complete. It could be improved by clarifying the return format (e.g., 'Returns an array of strings') and edge cases like empty matches or invalid patterns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The description adds no additional meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Split text using a regex pattern' clearly identifies a specific verb (split) and resource (text with regex). It distinguishes regex_split from sibling tools like string_split (which splits by a literal delimiter) and regex_extract/regex_replace.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over siblings such as string_split, regex_extract, or regex_test. No when-not-to-use or alternative suggestions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test a regex pattern against text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to test against the pattern | |
| pattern | Yes | The regex pattern to test | |
| flags | No | Regex flags (e.g., 'g', 'i', 'gi') | g |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose what the tool returns (e.g., boolean, matched text, capture groups). It lacks behavioral details beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single concise sentence, efficient and to the point. Could benefit from additional structure (e.g., return type), but remains clear and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description should explain the return value (e.g., boolean match). It also fails to differentiate from sibling regex tools in terms of output, leaving agents uncertain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds no additional meaning beyond the schema. Baseline 3 is appropriate as the schema already documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Test a regex pattern against text' clearly states the verb 'Test' and the resource, distinguishing it from sibling tools like regex_extract and regex_replace which perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., regex_extract, regex_replace). The description does not mention 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.
Join an array into text
| Name | Required | Description | Default |
|---|---|---|---|
| parts | Yes | The array of strings to join | |
| delimiter | No | The delimiter to join with |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It adds no behavioral details beyond the schema, such as how empty arrays or null values are handled, or whether the delimiter is optional.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise and gets to the point. However, it is slightly under-specified; a bit more detail could be added without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain the return value (a joined string), edge cases, or nuances like default delimiter behavior, which are only implied by the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema's parameter descriptions, which are already clear ('The array of strings to join', 'The delimiter to join with').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Join an array into text' clearly states the action (join) and the resource (array into text), which is sufficient to understand the tool's purpose. It distinguishes from siblings like 'string_split' by being its inverse, though this is implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor are any prerequisites or exclusions mentioned. The description simply states what the tool does without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Replace text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to perform replacements on | |
| search | Yes | The string to search for | |
| replace | Yes | The string to replace with | |
| replace_all | No | Whether to replace all occurrences |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It fails to disclose key behaviors: it does not mention that replacement is case-sensitive, that the original string is not modified (returns new string), or the effect of the replace_all parameter. Minimal beyond schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At only two words, the description is under-specified rather than concise. It does not earn its brevity, as it omits essential context. A well-structured description would front-load the purpose and key differentiators.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and many sibling tools (e.g., regex_replace), the description is entirely insufficient. It does not explain the output, dge cases, or limitations. The agent has no guidance on invocation beyond the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond the schema; it merely restates the tool's existence. Parameters like replace_all are documented in schema, but the description does not clarify their semantics further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Replace text' is a verb+resource but very generic. Among siblings like regex_replace, it does not specify that this tool performs simple substring replacement (non-regex). It distinguishes itself only by exclusion of 'regex', which is not explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines provided. There is no indication when to use this tool over alternatives like regex_replace or string_join. The description offers no context for decision-making, leaving the agent to infer intent 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.
Split text into an array
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to split | |
| delimiter | No | The delimiter to split by | |
| limit | No | Maximum number of splits (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behavior. It only states the action, omitting details like delimiter-not-found behavior, limit handling, or empty input handling. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with no wasted words. However, it could be slightly expanded for clarity without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no output schema and sibling tools in the same domain, the description lacks crucial details like return format, edge cases, and behavior with optional parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are described in the input schema (100% coverage). The description adds no additional meaning beyond what the schema provides, resulting in baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Split text into an array' clearly states the verb (split), resource (text), and outcome (array). It effectively distinguishes from siblings like string_join or regex_split.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., regex_split, string_substring). The description lacks any context about preferred use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract a substring
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to extract a substring from | |
| start | No | Starting index (inclusive) | |
| end | No | Ending index (exclusive, optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose behavioral traits beyond the basic function. It does not mention behavior for out-of-bounds indices, default handling of the end parameter, or any side effects. With no annotations, the description fails to provide necessary context for safe usage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (two words), but it is under-specified rather than concise. It lacks critical details that could be included without verbosity, such as the use of numeric indices.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a substring operation with optional parameters and no output schema, the description fails to explain return values or edge cases. It is not complete enough for an agent to reliably use without additional knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Since the input schema provides complete descriptions for all three parameters, the description adds no additional meaning. The baseline of 3 is appropriate because the schema already covers the semantic information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Extract a substring' states the action and resource clearly, but it is too generic given the many sibling tools like regex_extract. It does not specify that it uses numeric indices, which could lead to confusion with pattern-based extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like regex_extract, string_slice, or other extraction methods. There are no prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Trim whitespace from text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to trim | |
| trim_type | No | Type of trimming to perform | both |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of behavioral disclosure. It correctly identifies the operation as trimming whitespace but does not specify which characters are considered whitespace (e.g., spaces, tabs, newlines) or that the tool returns a new string without modifying the original. For a simple tool, this is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—only four words—and every word is necessary. It is front-loaded with the key information, making it easy for the agent to quickly grasp the function. There is no wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (two parameters, no nested objects, no output schema), the description is mostly sufficient. It clearly states the input and action. However, adding a note that the trimmed string is returned would enhance completeness, as there is no output schema to document return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'text' and 'trim_type' having clear descriptions in the schema. The tool's description adds no extra meaning beyond the schema, so a baseline score of 3 is appropriate. No additional semantic context is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Trim whitespace from text' clearly specifies the action (trim) and the resource/object (whitespace from text), making the purpose immediately understandable. While it doesn't explicitly differentiate from siblings like 'string_replace' or 'regex_extract', the context of string manipulation tools makes its distinct role clear enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'string_replace' (which could also remove whitespace) or 'regex_extract'. The description lacks context about suitable scenarios or prerequisites, leaving the agent to infer usage from the name and schema alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate a UUID
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | The UUID to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description fails to disclose what validation entails (e.g., format check, version check) and does not indicate the return value (e.g., boolean, error). This leaves the agent uncertain about 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, front-loading the core action and resource. No unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks essential context about the output or validation behavior, despite no output schema existing. For a simple validation tool, it should at least state the result type to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a clear description for the uuid parameter. The tool description adds no additional meaning beyond the schema, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates a UUID, using a specific verb and resource. It is unambiguous and distinguishes from sibling tools like generate_uuid.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While there is no explicit when-to-use guidance, the description implies usage for checking UUID correctness. Given the tool's simplicity, this is sufficient, though explicit mention of return type would improve clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0analyze_readabilitycase_to_camelcase_to_capitalcase_to_constantcase_to_dotcase_to_kebabcase_to_nocase_to_pascalcase_to_pascal_snakecase_to_pathcase_to_sentencecase_to_snakecase_to_traincount_characterscount_linescount_wordsdecode_base64decode_htmldecode_urlencode_base64encode_htmlencode_urlformat_htmlformat_jsonformat_sqlformat_xmlgenerate_hmacgenerate_lorem_ipsumgenerate_md5generate_sha1generate_sha256generate_sha512generate_uuidregex_extractregex_replaceregex_splitregex_teststring_joinstring_replacestring_splitstring_substringstring_trimvalidate_uuidEach tool has a clearly distinct purpose: case conversions target different formats, encoding/decoding are separate, regex tools cover distinct operations, etc. There is no ambiguity between them.
Names follow predictable patterns like case_to_* for case conversions, verb_noun for most others (count_, encode_, generate_), but there is a mix of verb-first (analyze_readability) and noun-first (regex_extract, string_join) patterns. Overall consistent but not perfectly uniform.
With 43 tools, the server is overloaded for a text utility toolkit. While each tool is justified, the sheer number exceeds typical MCP server scope, making navigation and selection harder than necessary.
The toolset covers a wide range of text operations: case conversion, encoding, formatting, regex, string manipulation, hashing, and validation. Minor gaps exist (e.g., no text diff, line sorting), but core workflows are well-covered.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Exact hashing, base64/hex/URL encoding, JWT decoding and UUIDs for AI agents. No auth required.
500+ deterministic tools for AI agents: math, conversion, validation, hashing, encoding, date/time.
61 text, security, converter, calculator, and PDF tools -- callable via MCP on one host.
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