mcp-toolkit-server

by enachimuthu

Overview Schema Related Servers Score Discussions

TypeScript

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a clearly distinct purpose: math, text stats, slugification, notes CRUD, encoding, hashing, UUID generation, and datetime. Even notes_list and notes_get are clearly separated by list vs. single-item retrieval. There is no meaningful overlap or confusion between tools.
Naming Consistency3/5
The notes tools follow a consistent notes_<verb> pattern, but the utility tools do not follow a single convention: some are nouns, some are noun_verb, and one is a bare verb. The names are individually readable and the mix is understandable, but the overall naming style is inconsistent.
Tool Count5/5
Eleven tools is well within the ideal range for a general-purpose toolkit. Each utility has a standalone use case, and the notes tools form a small, focused CRUD group without unnecessary bloat.
Completeness4/5
The utility tools cover common string, text, encoding, hash, UUID, and datetime operations well. The notes store supports list, add, get, and delete, but lacks an update operation, which is a minor gap that can be worked around with delete and re-add.
Average 3.9/5 across 11 of 11 tools scored. Lowest: 3.3/5.
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.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the destructive action ('Delete') but does not mention permanence, failure behavior for nonexistent IDs, or any side effects. This is a meaningful gap for a deletion tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence that conveys the essential action and target resource with no filler or redundancy. It is appropriately sized for a one-parameter tool.
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 one-parameter destructive tool, the description is mostly sufficient, but it omits any mention of return behavior, error cases, or the irreversible nature of the deletion. Given the absence of annotations and output schema, slightly more context would make it 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 already fully documents the single 'id' parameter with a clear description, so schema coverage is 100%. The tool description adds no new parameter information, which fits 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 ('Delete') and a precise resource ('a note'), identified by 'its id'. It clearly distinguishes this tool from sibling tools like notes_add, notes_get, and notes_list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus the sibling note tools, nor does it state any exclusions or prerequisites. The intended use is only implied by the verb and resource.
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 lists the counts returned but does not define what constitutes a word, character, line, or sentence, nor does it mention edge cases like empty input or how whitespace and punctuation are handled.
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 filler words. The verb and the object of the operation are front-loaded, and every word contributes to the meaning.
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 pure function with one parameter and no output schema, the description covers the core purpose and the types of results returned. It does not specify the exact return shape or edge-case behavior, but these are minor gaps given the low complexity and the clear list of counts.
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%, and the schema already describes the only parameter, 'text,' as 'The text to analyze.' The description adds no additional parameter-level meaning beyond what the schema provides, so baseline 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 ('Return') and identifies the exact resource: word, character, line, and sentence counts for a block of text. This clearly distinguishes it from siblings like calculator, slugify, and base64, which perform unrelated operations.
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?
There is no explicit statement of when to use this tool over alternatives, but the sibling tools are all clearly distinct utilities, so the intended usage is implied. It does not state exclusions or alternate routes for text analysis, but the context makes the choice nearly unambiguous.
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, the description carries the full burden. It conveys a read operation ('Retrieve') but does not disclose behavior for missing ids, return value format, or whether any permissions are needed. Basic but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded with the verb and the core concept. Every word earns its place; there is no redundant or tangential 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?
Given the tool's simplicity (one parameter, no nested objects, no output schema), the description covers the essential invocation details. A minor gap is the lack of any return-type or error-condition guidance, but overall it is sufficient for selection and basic 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%, so the schema already defines 'id' adequately. The description adds no extra parameter detail beyond what the schema provides, matching the baseline for high 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 uses a specific verb ('Retrieve'), a clear resource ('a single note'), and a clear scope ('by its id'). It unambiguously distinguishes this from sibling tools like notes_list, notes_add, and notes_delete.
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?
Usage context is implied: to get one note you need its id. However, the description does not explicitly state when to use it versus notes_list, nor does it mention any prerequisites or exclusions.
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 of behavioral disclosure. It clearly states the transformation outcome and implies a pure, non-destructive text operation. It does not detail edge-case behavior such as handling of special characters or non-ASCII text, but for a single-input utility this is minimally adequate.
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 concise sentence with no filler. The action and target output are front-loaded, and every word contributes meaning, making it appropriately sized for a one-parameter utility.
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 tool with one required parameter and no output schema, the description is sufficient for an agent to select and invoke it correctly. A brief example or explicit slug-format rule would improve completeness, but nothing essential is missing.
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%, and the input schema already fully describes the only parameter ('text'). The description adds no additional meaning beyond what the schema provides, so the baseline score of 3 applies.
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 ('convert') and a specific resource ('text into a URL-friendly slug'), making the tool's purpose immediately clear. It also distinguishes it from sibling text utilities like base64, hash, and uuid by specifying the unique output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: whenever a URL-friendly slug is needed from arbitrary text. However, it provides no explicit guidance on when not to use it or how it compares to sibling tools, so the usage context must be inferred.
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 were provided, so the description carries the behavioral burden. It discloses the core transformation and the UTF-8 encoding expectation. However, it does not mention error behavior for invalid base64 input, case sensitivity, whitespace handling, or whether output uses standard vs. URL-safe base64.
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 one efficient sentence with no filler. The action is front-loaded, and every word contributes to explaining the tool's behavior.
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-mode transform tool, the description plus the schema covers the required inputs and expected outcomes. Missing edge-case details like invalid-base64 errors would be nice, but the tool is simple enough that the current description 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 description coverage is 100%, so the schema already fully documents both parameters. The description adds the useful detail that the input is UTF-8 for encoding and the decoded result is UTF-8, but it does not add significant meaning 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 states a specific verb and resource: 'Encode a UTF-8 string to base64, or decode a base64 string back to UTF-8.' It clearly covers both modes of the tool. This distinguishes it from sibling tools like hash, slugify, and text_stats, which do different transformations.
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 makes the usage context clear by explicitly defining the two operations the tool supports. While it does not name alternatives or exclusions, none of the sibling tools perform base64 encoding/decoding, so the usage context is unambiguous.
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 adds valuable safety context by stating 'Does not use eval' and 'Safely evaluate', but it doesn't disclose behavior for invalid expressions, division by zero, order of operations, or return type.
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 short sentences with zero filler. The action and scope are front-loaded, and the safety note is tucked in without bloat.
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 one-parameter tool with no output schema, the definition covers what the tool does, supported syntax, and a key safety property. It could mention the return value format (e.g., numeric result) or error handling, but the tool is simple enough that this is a minor gap.
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 alone says the parameter is a string containing an arithmetic expression. The description adds meaning by explicitly listing supported operators and parentheses, which helps the agent form a valid expression. Even with 100% schema coverage, this extra constraint detail elevates it above the baseline.
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?
States a specific verb ('evaluate') with a clear resource (basic arithmetic expression) and enumerates supported operators. This clearly distinguishes it from the unrelated sibling tools like text_stats, hash, or notes_list.
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 when to use the tool by saying 'basic arithmetic expression', but it never explicitly states when to prefer it over alternatives or when not to use it. For example, it doesn't say 'use this for simple math, not for complex calculations or scripting'.
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, the description carries the full burden and does disclose the core behavior: it returns the current date and time adjusted for a given timezone. However, it does not specify the output format (e.g., ISO 8601 string, offset inclusion) and there is no output schema to fill that gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no filler. The action and the key scoping condition are both front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter, read-only utility, this description is nearly complete: an agent knows what input to supply and what kind of result to expect. The only notable gap is the exact return format, which is minor given the simplicity of 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 fully describes the single required parameter, including IANA timezone examples. The description adds no additional parameter semantics, but since schema description coverage is 100%, the baseline of 3 applies.
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 ('Return') and names a clear resource ('current date and time'), then scopes it to 'a given IANA timezone.' This clearly differentiates it from unrelated siblings like calculator, hash, and base64.
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 makes the usage context clear: use this tool whenever you need the current date and time for a specific IANA timezone. It does not explicitly discuss exclusions or alternatives, but none of the sibling tools perform time-related operations, so no further guidance is necessary.
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 core behavior and output format, but does not mention input encoding, digest casing, determinism, or the one-way nature of hashing. These are minor for a simple pure function, but some behavioral context is absent.
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 concise sentence that front-loads the verb and object. It contains no filler and every word contributes meaning.
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, stateless tool with a complete input schema, the description covers purpose, parameters, and output format. It is slightly light on edge-case behavior, but nothing essential is missing for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description reinforces that 'input' is the string and 'algo' is md5 or sha256, but adds little beyond the schema. The phrase 'hex digest' is the only extra semantic hint.
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 states a specific verb ('Compute'), the exact algorithms (md5/sha256), and the output format ('hex digest'). It clearly differentiates this tool from siblings like base64, slugify, and text_stats.
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 makes the tool's scope self-evident: use this when an md5 or sha256 hex digest is needed. It does not explicitly discuss alternatives or exclusions, but for a narrowly scoped utility this is not a significant gap.
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, the description carries the behavioral burden. It does so by disclosing the mutating effect (creating a new note) and the return value (the new id). It does not discuss persistence, error conditions, or ownership, but for a simple create operation the core side effects are clear.
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?
A single sentence that front-loads the action and includes the key return detail. Every word earns its place; no filler or 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 2-parameter CRUD tool with no output schema, the description plus full schema coverage is nearly sufficient: an agent knows what to pass and what to expect back. The only missing contextual element is explicit routing among note-related siblings.
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% and both title and content already have descriptive schema text ('Short title for the note,' 'Body content of the note'). The tool description adds no parameter-level detail, so the baseline score 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 states a specific operation — create a note — and adds the result behavior 'return its id.' This clearly differentiates it from sibling tools like notes_get, notes_list, and notes_delete.
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 intended use is implied by 'Create a new note': an agent would call this when it needs to add a note. However, it does not explicitly name alternatives or conditions such as using notes_list to browse existing notes.
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, the description carries the full burden of behavioral disclosure. It clearly indicates a non-destructive read operation that returns all notes, but it does not describe ordering, pagination, empty results, or the exact return format. This is adequate for a simple list tool but leaves some behavior implicit.
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 four-word sentence that states the action and scope directly. Every word earns its place, and the core purpose is 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, no parameters, and no output schema, the description provides enough information for an agent to understand what the tool does. It does not specify the return shape or ordering, which would be useful but is not strictly required for a simple no-argument list operation.
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, so the baseline is 4. The description correctly implies that no arguments are needed, and there is no parameter information to add.
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 ('List') and resource ('all stored notes'), making the tool's purpose immediately clear. It is naturally distinguished from sibling tools like notes_add, notes_get, and notes_delete, which involve different operations.
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?
'List all stored notes' provides clear context that this tool is for retrieving the full set of notes, not a single note or a filtered view. While it does not explicitly name alternatives or exclusions, the 'all' qualifier and sibling tool names make the appropriate usage unambiguous.
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 behavioral burden. It explicitly discloses the key behavioral trait—randomness—and specifies the UUID version. It implies a simple, non-destructive generation operation with no hidden side effects, which is appropriate for this tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence that states action, result type, randomness, and multiplicity with no filler. Every element in the description earns its place.
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 one-parameter, side-effect-free generator, the description is sufficient for selection and invocation. The only minor gap is that the return shape for multiple UUIDs (e.g., separate strings vs. list) is not explicit, but no output schema exists and the intent is fairly inferable from the count parameter.
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 fully documents the count parameter, including defaults, min, and max, so the description does not need to add parameter syntax. The phrase 'one or more' adds slight semantic reinforcement but not meaningful new information 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 specific action (generate), the exact resource (random v4 UUIDs), and the optional multiplicity. It differentiates the tool from sibling generators like base64, hash, or slugify by naming the UUID format and version.
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 gives clear context: use this when you need random v4 UUIDs. None of the sibling tools offer UUID generation, so explicit when-not-to-use guidance is unnecessary and no exclusion is misleading.
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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/enachimuthu/mcp-toolkit-server'

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