devkit-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: HTTP requests, JWT decoding, hashing, UUID generation, regex testing, cron explanation, and text transformations. No overlap or ambiguity exists.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., decode_jwt, hash_text, generate_uuid). The pattern is predictable and easy to understand.
Tool Count5/5
Seven tools is well-scoped for a general developer utility kit. Each tool covers a fundamental, non-overlapping function without being excessive or insufficient.
Completeness4/5
The tool set covers key developer needs: networking, crypto, identifiers, text processing, regex, and cron. Minor gaps like JSONPath or data format conversion are not critical for the stated purpose.
Average 4.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}
```
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds important behavioral details: constant-time comparison for verification, and a caveat that md5/sha1 are for interoperability only. This fully discloses key behaviors beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no fluff. The first sentence covers purpose and options, the second adds the verification feature. It is front-loaded and every sentence adds value.
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?
The description lacks information about the return value or output format, which is critical since no output schema exists. It does not explain what the tool returns (e.g., hex string, boolean for verification). This gap makes it incomplete for an agent to interpret results.
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 coverage is 100%, so the schema already describes each parameter. The description does not add significant new meaning beyond what is in parameter descriptions, such as the HMAC computation when key is provided, which is already stated in the schema.
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 it computes a cryptographic digest or HMAC of a string, specifies supported algorithms and encodings, and distinguishes itself from sibling tools which do not perform hashing. This provides a specific verb+resource combination.
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 does not explicitly state when to use this tool versus alternatives or when not to use it. While it's implied for hashing tasks, no comparisons or exclusions are given. It is adequate but lacks guidance on selection relative to siblings.
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?
Annotations already indicate readOnlyHint=true and idempotentHint=true, so the description adds detail about the return structure (matches with offsets, positional and named capture groups) and optional replacement behavior. This provides useful insight into the tool's output and mutation-free nature.
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 two sentences long, efficient, and front-loaded with the primary action. Every sentence adds value, and there is no redundancy or fluff.
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 no output schema, the description adequately describes return values: matches with offsets and capture groups, plus optional replacement output. Parameter coverage is complete. It could mention error handling or flag validation but is sufficient for an agent to understand the 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 input schema describes all 4 parameters with 100% coverage. The description adds no parameter-specific details beyond what the schema already provides, such as clarifying flag patterns or replacement syntax. The baseline of 3 is appropriate given 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 runs a JavaScript regex against a string and returns matches with character offsets and capture groups. It also mentions an optional replacement feature. This differentiates it from sibling tools like transform_text, which may perform general text transformations.
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 explains the tool's functionality but does not provide explicit guidance on when to use it versus alternatives, nor does it mention any exclusions. The optional replacement feature is shown, but no comparison to sibling tools like transform_text is given.
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?
Adds value beyond annotations (readOnlyHint) by specifying cryptographically secure random source and time-embedding for version 7. No contradictions.
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?
Two sentences, no redundant words. First sentence states action and security, second explains version differences and use case.
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?
Covers input parameters and version implications. Lacks explicit mention of return type (array of strings), but given simplicity and no output schema, it is mostly complete.
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 coverage is 100% with detailed parameter descriptions. The main description adds context about version 7's suitability for DB keys but does not add new parameter-level details beyond schema.
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?
Description clearly states it generates one or more UUIDs with a cryptographically secure random source, and distinguishes between version 4 and version 7, which are unique from sibling tools like http_request or decode_jwt.
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?
Explains when to use each version: version 4 for fully random, version 7 for time-sorted database keys. Does not explicitly state when not to use or list alternatives, but sibling tools are very different so this is sufficient.
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?
Annotations already set readOnlyHint and idempotentHint to true, so the description doesn't need to repeat those. However, it adds valuable behavioral context: decoding validates input and reports errors rather than returning garbage, and that the indent parameter is ignored by non-json operations. This goes beyond what annotations provide.
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?
Two sentences, front-loaded with the main purpose and operation list. Every sentence adds essential information: first covers all operations, second covers validation behavior. No redundancy or fluff.
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 moderate complexity (multiple transformation types), the description is fairly complete. No output schema exists, but for a transformation tool the return value is self-evident. Parameters are fully described in schema. The only minor gap is no mention of size limits or performance, which is acceptable for a simple transformation tool.
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?
Schema coverage is 100% (all three parameters have descriptions). The description adds extra semantics: it clarifies that the indent parameter is ignored by every operation except json-format. This adds value beyond the schema's description.
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 it applies reversible text transformations, listing specific operations (base64, base64url, hex, URL encoding/decoding, JSON formatting/minification). It directly distinguishes itself from sibling tools like hash_text (irreversible) or decode_jwt (specific JWT only).
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 lists what the tool does but does not explicitly say when to use it versus alternative tools (e.g., for encoding/decoding this is appropriate, but no direct comparison with siblings or exclusions). Usage context is implied by the operation list, but no when-not or alternative guidance is provided.
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?
Annotations already mark the tool as read-only (readOnlyHint=true). The description adds value by disclosing that the output includes both a plain English translation and a list of upcoming runs, and that the interval is explicitly reported to highlight potentially problematic schedules. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each serving a distinct purpose: first sentence states the core function, second sentence adds technical scope and a practical benefit. No redundant or filler content.
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?
The description covers the tool's purpose, parameters, and output features (plain English, next runs, interval). Given the absence of an output schema, it adequately communicates what the user will receive. A minor gap is the lack of error handling information (e.g., invalid expression), but for a tool of this simplicity, the description is largely complete.
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 schema covers all three parameters with descriptions (100% coverage). The description adds context beyond the schema by explaining that the expression can be 5 or 6 fields, and that the timezone uses IANA names. It also adds the output feature of reporting the interval, which is not in the schema.
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 that the tool translates a cron expression into plain English and lists next scheduled runs, specifying it handles both 5-field and 6-field syntax. This specific verb+resource description distinguishes it from all sibling tools, which cover different domains like HTTP requests, JWT decoding, hashing, etc.
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 a cron expression needs interpretation, and notes an additional use case: reporting the interval to catch accidentally too-frequent schedules. While no explicit when-not-to-use or alternatives are provided, the sibling tools are sufficiently distinct that no further guidance is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: JSON parsing based on content type, manual redirect following (allowing re-check), 256 KB response cap, and refusal to private/loopback addresses unless env var set. Annotations provide readOnlyHint=false and openWorldHint=true, which are consistent and the description adds significant value beyond them.
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 three sentences, front-loaded with purpose, then technical details. Every sentence adds value with no wasted words.
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 complexity (6 params, no output schema, nested objects), the description covers return format, JSON parsing, redirect behavior, size limit, and host restrictions. It could mention error handling or what happens when response exceeds the cap, but overall quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal extra parameter detail (e.g., mentioning JSON parsing but not linking to a specific param). 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 uses a specific verb ('Sends') and resource ('HTTP request'), and clearly states what is returned (status, headers, timing, body). It implicitly distinguishes from sibling tools which are unrelated (text/crypto utilities).
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 for making HTTP requests via the web, and siblings are clearly different tools, so context is clear. However, it does not explicitly state when to use or not use this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and idempotentHint. The description adds crucial behavioral details: the signature is returned but not verified, and the result always states this. No contradictions.
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?
Two sentences, front-loaded with the core action, followed by important caveats. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description fully covers what the tool does, what it returns, and what it omits. No gaps remain.
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 already describes the token parameter with full coverage. The description does not add new semantic detail beyond mentioning the tool accepts a JWT, which is already implied by the parameter name and schema description.
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 it decodes the header and payload of a JWT, reports specific claims and expiry, and notes it does not verify. This distinguishes it from a validation tool, and siblings like hash_text or generate_uuid are unrelated.
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 explicitly states the tool inspects but does not validate tokens, guiding the agent not to use it for verification. It does not name an alternative tool, but the context is clear enough for proper usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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