mcp-server-devutils

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.1
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. For example, base64_encode and base64_decode are complementary but distinct operations, while cron_explain, cron_next, and cron_validate each handle different aspects of cron expressions. The descriptions make it easy to differentiate between tools like json_format, json_minify, and json_validate.
Naming Consistency5/5
Tool names follow a highly consistent snake_case pattern with clear verb_noun or noun_verb structures throughout. Examples include base64_decode, cron_explain, json_format, and timestamp_to_iso. There are no deviations in naming conventions, making the set predictable and easy to navigate.
Tool Count4/5
With 17 tools, the count is slightly high but reasonable for a developer utilities server covering diverse domains like encoding, time, JSON, and identifiers. Each tool serves a specific function, though some related tools (e.g., hash and hash_all) could potentially be consolidated without loss of functionality.
Completeness5/5
The toolset provides comprehensive coverage for common developer utility tasks, including encoding/decoding, cron handling, hashing, JSON manipulation, time conversions, and ID generation. There are no obvious gaps; for instance, cron operations include explain, next, and validate, while time tools cover ISO to timestamp, timestamp to ISO, and current time.
Average 3.4/5 across 17 of 17 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('decode') but doesn't describe error handling (e.g., for invalid Base64), output format (e.g., string vs. binary), or performance characteristics. This leaves significant gaps for a tool that transforms data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded with the core action and resource, making it easy to parse. Every word earns its place, and no unnecessary details are included.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., decoded text, potential errors), which is critical for a decoding operation. For a tool with 100% schema coverage but no behavioral context, this leaves the agent guessing about outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'input' clearly documented as 'The Base64 string to decode'. The description adds no additional meaning beyond this, such as format requirements or examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('decode') and resource ('a Base64 string'), making the purpose immediately understandable. It distinguishes from its sibling 'base64_encode' by specifying the opposite operation. However, it doesn't explicitly mention what the output is (decoded text/binary), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where Base64 decoding is appropriate, prerequisites for the input string, or comparisons with other tools like 'jwt_decode' that might also handle encoded data. The agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the action ('Encode a string to Base64') but doesn't disclose behavioral traits such as error handling (e.g., for non-string input), encoding standards (e.g., UTF-8), or output format details. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded with the core action and resource, making it easy to parse. Every word earns its place, and there is no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It doesn't explain the return value (e.g., the Base64-encoded string), potential errors, or encoding specifics. For a tool with no structured output or annotations, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'input' documented as 'The string to encode'. The description adds no additional meaning beyond this, as it doesn't elaborate on constraints or examples. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Encode') and resource ('a string'), specifying the transformation to Base64. It distinguishes from its sibling 'base64_decode' by indicating the opposite operation, though it doesn't explicitly name the sibling. The purpose is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. While the name implies it's for Base64 encoding, it doesn't mention use cases, prerequisites, or compare it to other encoding/hashing tools like 'hash' or 'hash_all' in the sibling list. The description lacks context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose output format (e.g., returns a dict of algorithm:hash pairs), performance implications of hashing with 'all' algorithms, or error handling. 'At once' hints at batch processing but is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core purpose ('Hash a string') and adds specificity ('with all supported algorithms at once') without redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a tool that performs a potentially complex operation. It doesn't explain what 'all supported algorithms' entails or what the return value looks like, leaving significant gaps for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'input' documented as 'The string to hash'. The description adds no additional meaning beyond this, such as encoding requirements or length limits. Baseline 3 is appropriate since the schema adequately covers the single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('hash') and resource ('a string'), specifying it uses 'all supported algorithms at once'. This distinguishes it from the sibling 'hash' tool, which likely uses a single algorithm. However, it doesn't specify what those algorithms are, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like the 'hash' sibling. It doesn't mention use cases (e.g., comparing multiple hashes, security testing) 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It doesn't mention error handling (e.g., what happens with invalid JSON), performance characteristics, or output format details. The description is minimal and lacks behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words, front-loaded with the core purpose. Every word earns its place with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the formatted output looks like, error conditions, or typical use cases. Given the simplicity of the tool, more context would be helpful for an AI agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description doesn't add any parameter semantics beyond what's in the schema (e.g., it doesn't explain what 'pretty-print' means in terms of the indent parameter). Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Pretty-print / format a JSON string' which specifies both the verb (format) and resource (JSON string). It distinguishes from siblings like json_minify (which does the opposite) and json_validate (which validates rather than formats), though it doesn't explicitly name these alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like json_minify or json_validate. It doesn't mention prerequisites (e.g., needing valid JSON input) or typical use cases (e.g., debugging, human-readable output).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions validation and type reporting but doesn't specify error handling, output format, performance characteristics, or any constraints. For a tool with no annotations, this leaves significant gaps in understanding its behavior beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of a single, clear sentence: 'Validate a JSON string and report its type.' There is no wasted language, and it efficiently communicates the core purpose without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It covers basic purpose but fails to address behavioral aspects like error responses, type reporting details, or usage context. For a validation tool with no structured support, more information is needed to ensure the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'input' documented as 'JSON string to validate.' The description adds no additional meaning beyond this, as it doesn't elaborate on syntax, examples, or validation rules. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Validate a JSON string and report its type.' It specifies the verb (validate), resource (JSON string), and outcome (report type). However, it doesn't explicitly differentiate from sibling tools like json_format or json_minify, which handle JSON differently but are related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description lacks context about use cases, prerequisites, or comparisons to sibling tools such as json_format (for formatting) or json_minify (for compression). It merely states what the tool does without indicating appropriate scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic action ('test a regular expression against a string') without detailing traits like error handling, output format, performance implications, or rate limits. This is inadequate for a tool with no annotation coverage, leaving significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence: 'Test a regular expression against a string.' It is front-loaded, efficient, and wastes no words, making it easy to understand quickly. Every part of the sentence contributes directly to the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (regex testing with parameters) and the lack of annotations and output schema, the description is incomplete. It does not explain what the test returns (e.g., matches, groups, boolean result), error cases, or behavioral details. For a tool with no structured output or annotations, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters (pattern, flags, testString) well-documented in the schema. The description does not add any meaning beyond what the schema provides, such as examples or usage notes. According to the rules, with high schema coverage, the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Test a regular expression against a string.' It specifies the verb ('test') and the resource ('regular expression'), making the function unambiguous. However, it does not differentiate from sibling tools, which are unrelated (e.g., base64_encode, json_format), so it lacks explicit sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, and there is no reference to sibling tools or other methods for regex testing. Usage is implied by the name and description alone, with no explicit instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't explain what validation entails (e.g., syntax checking, semantic validation, error messages), whether it's read-only or has side effects, or any performance or security considerations. This leaves significant gaps 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just three words, front-loading the core purpose with zero wasted text. Every word earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It states the purpose but lacks behavioral details and usage context, which are important for a validation tool that likely returns success/failure or error information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'expression' fully documented in the schema. The description doesn't add any additional meaning beyond what the schema provides (e.g., format examples, validation rules), so it meets the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Validate a cron expression' clearly states the verb ('validate') and resource ('cron expression'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'cron_explain' or 'cron_next', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are sibling tools like 'cron_explain' and 'cron_next' that work with cron expressions, but the description doesn't mention any context, prerequisites, or exclusions for choosing this validation tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose behavioral traits like whether generation is deterministic, performance characteristics, error conditions, or what the output format looks like (e.g., array of strings).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple generation tool with one well-documented parameter and no output schema, the description is minimally adequate. However, it lacks details on output format and behavioral context, which would help an agent use it correctly without trial and error.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'one or more UUIDs', which hints at the 'count' parameter, but the schema already has 100% coverage with a clear description. No additional parameter semantics are provided beyond what's in the schema, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Generate' and the resource 'UUIDs (v4)', making the purpose specific and understandable. It distinguishes from siblings like 'ulid_generate' by specifying UUID v4, but doesn't explicitly contrast with all possible alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where UUID generation is needed, prerequisites, or comparisons with sibling tools like 'ulid_generate' for different ID formats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] the current time' but doesn't specify if this is based on system time, server time, or UTC, nor does it mention potential latency, rate limits, or error conditions. For a tool with no annotation coverage, this leaves key behavioral traits unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Get the current time in multiple formats.' It is front-loaded with the core purpose and adds value by specifying 'multiple formats.' There is no wasted wording, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on output formats, timezone handling, or behavioral traits. Without an output schema, the description should ideally hint at return values, but it only mentions 'multiple formats' vaguely, leaving gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the absence of inputs. The description doesn't need to add parameter details, and it correctly implies no parameters are required by not mentioning any. This meets the baseline for tools with no parameters, as it doesn't mislead about inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get the current time in multiple formats' clearly states the tool's function with a specific verb ('Get') and resource ('current time'), and specifies the output characteristic ('multiple formats'). It distinguishes itself from siblings like 'timestamp_to_iso' or 'iso_to_timestamp' by focusing on the current time rather than conversion. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like needing real-time timestamps for logging or comparisons, or when to prefer this over tools like 'timestamp_to_iso' for specific formats. With siblings available for time-related operations, the lack of usage context is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose output format (e.g., timestamps, timezone handling), error behavior for invalid expressions, or performance considerations like rate limits, leaving gaps for a tool that generates data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality without unnecessary words, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 2 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. However, it lacks details on output format (e.g., list of timestamps) and error handling, which would enhance completeness for an agent invoking this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters (expression and count). The description adds no additional meaning beyond what's in the schema, such as cron syntax examples or count usage context, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Show') and resource ('next N run times for a cron expression'), distinguishing it from sibling tools like cron_explain and cron_validate that serve different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for generating future schedules from cron expressions, but it doesn't explicitly state when to use this tool versus alternatives like cron_explain (for explanation) or cron_validate (for validation), nor does it 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. While 'Minify' implies a transformation operation, it doesn't disclose important behavioral traits: whether malformed JSON will cause errors, if the output is deterministic, performance characteristics, or what happens with special JSON values. The description only states what the tool does, not how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence with parenthetical clarification. Every word earns its place - 'Minify' (verb), 'a JSON string' (resource), '(remove whitespace)' (operational detail). No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple transformation tool with 100% schema coverage but no annotations and no output schema, the description is minimally adequate. It explains what the tool does but lacks information about error conditions, output format, or behavioral guarantees that would be helpful for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'input' clearly documented as 'JSON string to minify'. The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Minify') and resource ('a JSON string'), with the parenthetical '(remove whitespace)' providing precise operational detail. It distinguishes from sibling json_format (which presumably formats/beautifies JSON) by focusing on minimization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (when you need to remove whitespace from JSON), but doesn't explicitly state when to use this tool versus alternatives like json_format or json_validate. No explicit exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it states the conversion function, it doesn't mention error handling (e.g., invalid ISO strings), timezone considerations, precision (seconds/milliseconds), or what happens with edge cases like leap seconds. For a data transformation tool, these behavioral aspects are important but undocumented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized for a simple conversion tool and front-loads the essential information immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter conversion tool with no annotations and no output schema, the description adequately covers the basic purpose. However, it lacks details about return format, error conditions, and behavioral nuances that would be helpful given the absence of structured metadata. The description is minimally complete but could provide more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'date' fully documented in the schema as 'ISO 8601 date string'. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline score of 3 for high schema coverage without adding extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Convert') and the resource transformation ('ISO 8601 date string to Unix timestamp'). It precisely distinguishes this tool from sibling tools like 'timestamp_to_iso' (which performs the inverse operation) and 'timestamp_now' (which generates current timestamp).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly provides usage context by specifying the input format ('ISO 8601 date string') and output type ('Unix timestamp'), which helps determine when to use this tool. However, it doesn't explicitly state when to use this versus alternatives like 'timestamp_to_iso' or mention any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the core behavior (conversion), but does not mention potential edge cases (e.g., handling of invalid timestamps, timezone considerations, or output format variations). It adds basic context but lacks details on error handling 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and every part of the sentence contributes essential information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no output schema, no annotations), the description is minimally complete. It covers the basic transformation but lacks details on output format (e.g., timezone inclusion) or error behavior. For a simple conversion tool, this is adequate but has clear gaps in behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'timestamp' fully documented in the schema. The description does not add any additional meaning beyond what the schema provides (e.g., it doesn't clarify 'seconds or milliseconds' further). Baseline score of 3 is appropriate as the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Convert') and the transformation ('Unix timestamp to ISO 8601 date string'), distinguishing it from sibling tools like 'iso_to_timestamp' (reverse operation) and 'timestamp_now' (generates current timestamp). It precisely defines what the tool does without being vague or tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing to convert a Unix timestamp to a human-readable ISO format, but it does not explicitly state when to use this tool versus alternatives like 'iso_to_timestamp' or 'timestamp_now'. The context is clear, but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the transformation behavior (cron expression to plain English) but doesn't disclose error handling, format limitations, or performance characteristics. The description is accurate but lacks depth about behavioral traits beyond the core function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and uses minimal structure, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no annotations, no output schema), the description is reasonably complete for its purpose. It clearly defines what the tool does, though it could benefit from mentioning output format details or error scenarios to enhance completeness for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the parameter 'expression' fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as examples of valid formats or edge cases. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('explain') and the resource ('a cron expression'), specifying the output format ('in plain English'). It distinguishes from sibling tools like cron_next (which predicts next run times) and cron_validate (which checks syntax), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'in plain English', suggesting this tool is for human-readable interpretation rather than validation or scheduling. However, it doesn't explicitly state when to use this versus alternatives like cron_validate or cron_next, nor does it provide exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It specifies the operation (hashing) and supported algorithms, but doesn't mention output format (e.g., hex string), performance characteristics, or security implications. It adequately describes the core behavior but lacks richer context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences: the first states the purpose, and the second lists supported algorithms. Every word earns its place, with no redundancy or unnecessary elaboration, making it highly efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (2 parameters, no output schema, no annotations), the description is reasonably complete. It covers the purpose and algorithm support, but could improve by mentioning the output format or typical use cases. For a simple hashing tool, it's mostly adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters. The description adds value by listing the specific algorithm options, which reinforces the enum in the schema, but doesn't provide additional semantic context beyond what's already in the structured data.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('hash') and resource ('a string'), specifying the action and target. It distinguishes from sibling tools like 'hash_all' by focusing on single-string hashing rather than batch processing, and from encoding/decoding tools by its cryptographic purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by listing supported algorithms, which helps select this tool for string hashing needs. However, it doesn't explicitly state when to use alternatives like 'hash_all' for batch processing or other sibling tools for different operations, leaving some guidance gaps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it decodes without verification (indicating it's a read-only, non-destructive operation) and specifies what information is shown (header, payload, expiry). However, it doesn't mention error handling for invalid tokens or output format details, leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that is front-loaded with the core purpose and includes essential details. Every word earns its place, with no wasted text, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no annotations, no output schema), the description is mostly complete. It covers the purpose, behavior, and output components. However, it lacks details on error cases or exact return structure, which could be helpful for an agent, though not critical for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'token' fully documented in the schema. The description adds no additional meaning beyond what the schema provides (e.g., no syntax or format details for the token). Baseline 3 is appropriate as the schema handles the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('decode a JWT token') and resource ('JWT token'), and distinguishes it from siblings by specifying 'without verification' and listing the output components ('header, payload, expiry'). This is precise and differentiates it from potential verification tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'without verification', suggesting it's for inspection rather than security validation, but it doesn't explicitly state when to use this tool versus alternatives (e.g., when verification is needed) or provide exclusions. It offers some context but lacks explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a generation/creation operation, but doesn't specify whether this is deterministic, what format the output takes, or any rate limits or constraints. The description is accurate but lacks operational details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise - a single sentence that communicates the complete purpose with the parenthetical adding essential clarification. Every word earns its place, and the structure is front-loaded with the core action immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter generation tool with no output schema, the description provides sufficient context about what's being created. The ULID explanation is crucial for understanding the output. However, without an output schema, the description could benefit from specifying the exact format of the generated ULID.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't waste space discussing non-existent parameters, and the ULID explanation provides all necessary context about what's being generated.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate') and resource ('ULID'), with the parenthetical explanation providing essential context about what a ULID is. It distinguishes this tool from sibling tools like uuid_generate by specifying the exact type of identifier being created.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the ULID explanation, suggesting this is for creating sortable unique identifiers. However, it doesn't explicitly state when to use this versus alternatives like uuid_generate or timestamp_now, nor does it provide any exclusion criteria or prerequisites for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcp-server-devutils MCP server](https://glama.ai/mcp/servers/ofershap/mcp-server-devutils/badges/card.svg)](https://glama.ai/mcp/servers/ofershap/mcp-server-devutils)

Score Badge

Copy to your README.md:

[![mcp-server-devutils MCP server](https://glama.ai/mcp/servers/ofershap/mcp-server-devutils/badges/score.svg)](https://glama.ai/mcp/servers/ofershap/mcp-server-devutils)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ofershap/mcp-server-devutils'

If you have feedback or need assistance with the MCP directory API, please join our Discord server