MCP Utility Tools

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: unit conversion, password generation, date math, hashing, base64, UUIDs, case conversion, and text statistics. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow the verb_noun snake_case pattern (convert_units, generate_password, date_calculator, hash_text, etc.). The naming is consistent and predictable.
Tool Count5/5
Eight tools is well-scoped for a general utility server. Each tool serves a distinct common utility need, and the count is within the ideal 3-15 range.
Completeness4/5
The tool surface covers a broad set of common utilities (conversion, generation, encoding, text analysis). Minor gaps exist (e.g., no random number generator or URL encoder/decoder) but the set is reasonably complete for its stated purpose.
Average 3.9/5 across 8 of 8 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 status not available
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

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 disclosing behavior. It only says 'generate a hash digest' and does not mention output format, determinism, encoding, or potential edge cases like invalid algorithm values. This is minimal for a tool with no annotation support.
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 sentence with no redundant words, front-loading the core purpose. Every word contributes to understanding the tool's function.
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, complete parameter schema, and presence of an output schema, the description is largely sufficient. It lacks usage context, but the core function is clearly stated and well-supported by structured metadata.
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 both parameters fully described in the schema (including default and allowed algorithm values). The description adds no semantically meaningful information beyond referring to 'text', so the baseline 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 ('Generate') and resource ('hash digest of the given text'), clearly stating what the tool does. It is easily distinguished from sibling utility tools like convert_units or generate_password, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. It only states the action without contextualizing appropriate use cases, such as checksums or password hashing.
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 burden of behavioral disclosure. It adds that UUIDs are random and version 4, which is useful. But it does not mention output format, side effects, or constraints beyond what the schema already covers.
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?
Single sentence, front-loaded, and free of wasted words. Effectively communicates the core purpose without redundancy.
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 simple utility tool with well-parameterized schema and an output schema, the description is adequate. It omits return details but the output schema covers that. The lack of usage guidelines is a minor gap.
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 count and uppercase. The description adds 'one or more' which is already in the schema. No additional parameter semantics are provided beyond 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 the tool's function: generating random UUIDs (version 4). This verb-resource-version structure distinguishes it from sibling tools like generate_password or hash_text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use UUID generation versus alternatives (e.g., when to use generate_password for tokens or hash_text for digests). No context or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only states the basic conversion operation without addressing edge cases, input validation, output format, or error handling. For a stateless conversion tool this is a notable gap, though not severely misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the action and lists the supported cases. No extraneous information is present, earning a perfect score.
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 simple two-parameter converter with a clear schema and an output schema present, the description adequately captures the tool's purpose. It could mention behavior for unexpected input or reversibility, but the core use case is fully covered, making it nearly 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?
The input schema fully describes both parameters with allowed values and examples. The description adds no additional parameter semantics beyond restating the target case options, which are already in the schema. With 100% schema coverage, the baseline 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 identifies the tool as a text converter between specific naming conventions, enumerating all supported styles with concrete examples (snake_case, camelCase, etc.). This differentiates it from sibling tools like convert_units or hash_text, making its function 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 use whenever text needs to be transformed into a different casing style, and the sibling list provides context for alternatives. However, it does not explicitly state exclusions or when not to use this tool, such as for non-case text transformations, so it falls short of a 5.
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 burden of behavioral disclosure. It states the high-level operations but lacks details on edge cases, error handling, or return format. The existence of an output schema helps, but the description adds minimal behavioral context beyond the basic operations.
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, front-loaded sentence that communicates both primary operations without any filler. It is maximally concise while remaining informative, earning full marks for efficiency.
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 simple calculation utility, the description plus schema and output schema cover the essential information needed to select and invoke the tool. It could slightly improve by explicitly noting parameter dependencies (e.g., days required for add/subtract), but the schema already communicates these, so the context is nearly 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 each parameter already well-described (e.g., date1 as 'starting date', date2 as 'second date', operation listing allowed values). The description essentially paraphrases the schema without adding new semantic details, so it stays at the baseline of 3.
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 identifies the tool's purpose: calculating date differences or adding/subtracting days. It uses a specific verb and resource, and is well-distinguished from sibling tools like convert_units and hash_text, which serve entirely 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states the main use cases ('difference between two dates, or add/subtract days') but does not explicitly mention when not to use the tool or compare it to alternatives. However, since all sibling tools are unrelated utilities, the context is clear enough, earning a 4 rather than a 5.
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 burden of disclosing behavioral traits. It mentions 'cryptographically secure random passwords,' which is a key behavioral trait. However, it does not disclose details like what happens with invalid input or whether output is a single string vs an array, though output schema may cover that.
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 with no unnecessary words. It communicates the core function efficiently.
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 low complexity, full schema coverage, and presence of an output schema, the description is nearly complete. It lacks explicit usage scenarios, but that is more relevant to the usage guidelines dimension. The tool is simple enough that the description suffices.
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 (count, length, use_symbols). The description adds no additional parameter semantics beyond the schema, so a baseline 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 tool's function: generating one or more cryptographically secure random passwords. The verb 'generate' and resource 'password' are specific, and the description distinguishes it from sibling tools like generate_uuid or hash_text.
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: use when you need random passwords. It does not explicitly name alternatives or exclusions, but the context is clear given the sibling tools are unrelated. No misleading guidance is present.
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 burden of disclosing behavior. It correctly implies a pure conversion operation, but does not explicitly state side effects (e.g., no data is modified) or return format. For a straightforward conversion tool, the description is adequate but lacks added context beyond the examples.
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: two sentences with no wasted words. The first sentence states the purpose, and the second provides illustrative examples. It is front-loaded and easily parsed.
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 simple conversion tool with full schema coverage and an output schema, the description is complete. It names the unit categories and examples, which is exactly what an agent needs. The output schema handles return values, so the description need not repeat that.
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%, and each parameter already has an example in the schema. The description's examples overlap with the schema's parameter examples, adding minimal new semantic meaning. Baseline of 3 is appropriate since the schema already carries the parameter documentation burden.
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 converts numeric values between common units of length, weight, temperature, or volume. The verb 'convert' is specific, and the resource is well-defined. It clearly distinguishes from sibling tools like generate_password or date_calculator by focusing on unit conversion.
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 concrete examples (km<->mi, kg<->lb, celsius<->fahrenheit, liter<->gallon) that imply when to use this tool. However, it does not explicitly state exclusions or compare to alternatives, but the context is clear and sufficient for a simple conversion tool.
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 full burden. It clearly states the tool computes read-only statistics and lists the output categories. However, it does not disclose assumptions (e.g., reading time formula, Unicode handling) or explicitly affirm no side effects, leaving some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous detail. It lists the key outputs without repetition and efficiently communicates the tool's behavior.
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?
Given the simple one-parameter tool and the presence of an output schema, the description adequately covers the tool's purpose for selection and invocation. It does not need to document return values since the output schema exists.
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 provides 100% coverage for the sole parameter ('text' with description 'The text to analyze.'). The description adds no extra meaning beyond the schema, so it meets the baseline of 3.
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 ('Get') and identifies the resource ('statistics about a piece of text') while enumerating the exact outputs (character, word, sentence, paragraph counts, reading time). This clearly distinguishes it from sibling tools that perform conversions or hashing.
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 that this tool is for analyzing text statistics and is unambiguously distinct from sibling utilities. It does not explicitly name alternatives or state when not to use it, but the intended use case is self-evident from the description.
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?
No annotations are provided, so the description carries the full burden of explaining behavior. It accurately discloses that the tool performs either encoding or decoding based on the operation parameter, which is a transparent account of its behavior. It does not mention error handling for invalid Base64, but for a simple pure-conversion tool this is sufficient.
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 sentence that front-loads the verb and resource, with zero wasted words. It is concise and immediately informative.
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?
The tool has only two parameters with a 100% schema-described coverage and an output schema, so the return format is already implied. The description fully covers the tool's purpose and behavior; there are no prerequisites, side effects, or edge cases that need explaining for this simple conversion 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 descriptions cover 100% of the parameters, so the baseline is 3. The tool description adds little beyond the schema—it restates that text is encoded/decoded, which the schema already says. No additional semantics are provided that aren't already 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 uses specific verbs 'Encode' and 'decode' to identify the resource (text) and clearly states the tool's two functions. It distinguishes from siblings like hash_text and convert_case by naming the exact conversion (Base64), making it 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 explicitly states the operation ('Encode text to Base64, or decode...'), giving clear context for when to use the tool. It doesn't mention alternative tools, but the sibling tools are functionally distinct (e.g., hash_text for hashing), so no exclusion is necessary.
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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