Utilities: JSON, regex, cron, hash, base64, URL, color, diff, CSV, JWT, UUID, Markdown, semver, time
Encode or decode Base64 data. Useful for binary data, token inspection, and data serialization.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Whether to encode (normal -> base64) or decode (base64 -> normal) | encode |
| input | Yes | The data to encode or decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It accurately describes the core transform as a pure encode/decode operation, but it does not disclose behavior for malformed Base64 input, default mode effects, or output format details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with n filler. The first sentence front-loads the core operation and resource; the second earns its place by adding useful application contexts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity two-parameter utility with a fully documented schema, the description is sufficient for basic invocation. The lack of an output schema is offset by the fact that the encode/decode result is implied by the operation, though edge cases like invalid input are not covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'mode' and 'input' documented including the enum and default. The description adds use-case context but no additional parameter-level meaning, so 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear operation ('Encode or decode') on a specific resource ('Base64 data') and adds concrete use cases. It does not explicitly name sibling tools, but Base64 encoding is clearly distinct from siblings like hash_compute and jwt_decode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Useful for binary data, token inspection, and data serialization' gives practical context for when an agent should choose this tool. It stops short of naming alternatives or stating when not to use it, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert a color between hex, RGB, HSL, and named formats. Returns the color in all supported representations.
| Name | Required | Description | Default |
|---|---|---|---|
| color | Yes | The color to convert. Formats: #RRGGBB, #RGB, rgb(r,g,b), hsl(h,s%,l%), or a named color like "red" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses the core transformation behavior and explicitly states the output behavior: 'Returns the color in all supported representations.' This gives useful insight beyond the schema, though it does not address invalid-input handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences with no filler. The core action is front-loaded, and every word contributes meaning. The output behavior is stated in the second sentence, making the whole definition easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple single-parameter conversion tool with no output schema, annotations, or nested objects. The description covers what it does, what formats it accepts, and what it returns, which is sufficient for an agent to invoke and interpret the result correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already thoroughly documents the single `color` parameter and its accepted formats. The description renames the same formats but does not add meaningful semantics beyond what the schema provides, matching the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Convert') and resource ('a color'), and precisely names the supported representations: hex, RGB, HSL, and named formats. This makes it clearly distinct from all sibling tools, which handle different data types like base64, cron, CSV, or JWT.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use it: whenever a color needs to be converted between common formats. No alternative color-conversion tool exists among the siblings, so explicit exclusions are unnecessary. The context is clear enough for correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse a cron expression (5 fields) and explain the schedule. Computes and shows the next 5 run times in ISO format.
| Name | Required | Description | Default |
|---|---|---|---|
| expression | Yes | Cron expression with 5 fields: minute hour day-of-month month day-of-week (e.g. "*/15 * * * *") |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the full burden of behavioral disclosure. It does state the core behavior: parse the expression, explain the schedule, and compute the next 5 run times in ISO format. However, it omits details such as timezone handling, behavior on invalid expressions, or whether standard extensions like @daily are supported, which would make behavior more transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences convey the input, the operation, and the output format with no filler. The main purpose is front-loaded, and every clause adds necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one required parameter, no nested objects, no output schema), the description is nearly complete. It tells the agent what input to provide and what output to expect (schedule explanation and next 5 ISO run times). It could be slightly richer by describing the shape of the returned schedule explanation, but it is adequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides a fully descriptive parameter definition with a concrete example, so schema description coverage is 100%. The tool description adds little beyond reaffirming that the expression has 5 fields, which does not meaningfully extend the schema's parameter semantics. Baseline 3 applies because the schema handles the documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Parse') and a clear resource ('a cron expression (5 fields)'), then states exactly what it produces: a schedule explanation plus the next 5 run times in ISO format. This clearly distinguishes it from sibling tools, all of which handle unrelated formats or operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the tool's purpose clear enough that an agent knows to use it whenever a cron expression needs parsing and next-run calculation. No sibling tool overlaps with this functionality, so explicit alternative routing is unnecessary. It lacks explicit 'when not to use' guidance, but the context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse CSV/TSV/semicolon/pipe-delimited data into rows. Returns column headers, row/column counts, delimiter used, column consistency check, and a JSON preview of the first rows. Handles quoted fields and escaped quotes.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | Yes | The delimited data to parse | |
| delimiter | No | Delimiter: auto, comma, tab, semicolon, or pipe | auto |
| has_header | No | Whether the first row is a header row |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden and does so well: it explains what the tool returns and that it handles quoted fields and escaped quotes. It does not mention potential limitations such as malformed input handling, size limits, or exact preview size, so it is strong but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences, each adding distinct value: main purpose, concrete outputs, and edge-case handling. It is front-loaded with the tool's core action and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple parsing tool with no output schema, the description usefully lists the return components and special-case behavior. It is complete enough for an agent to select and call the tool, though 'column consistency check' could be more specific and no error behavior is mentioned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all three parameters. The description reinforces which delimiters are supported and the fact that the first row may be a header, but it does not add meaningful parameter-level detail beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Parse') and the exact resource ('CSV/TSV/semicolon/pipe-delimited data'), and clearly distinguishes the tool from siblings like json_inspect or text_diff by describing what it returns: headers, counts, delimiter, consistency check, and preview. The purpose is immediately understandable and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies the tool is for converting delimited text into structured rows, which is distinct from all listed siblings. It does not explicitly name an alternative or state when not to use it, so the guidance is clear but lacks explicit exclusions or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compute a cryptographic hash of input data. Supports MD5, SHA1, SHA256, and SHA512. Returns the hex digest.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The data to hash | |
| algorithm | No | Hash algorithm to use | sha256 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses that the operation is a pure computation, returns a hex digest, and supports several algorithms. It could mention input encoding, but for a simple utility this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences convey purpose, supported algorithm choices, and output format without redundancy. The most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is sufficient for a simple two-parameter tool: it states the input, algorithm choices, and return format. Minor specifics such as input encoding and the default algorithm are absent, but the schema covers the default and the rest is reasonably inferable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both 'input' and 'algorithm'. The description adds no extra meaning about parameter formats or constraints beyond confirming the algorithm options.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and resource: 'Compute a cryptographic hash of input data.' It then lists supported algorithms and the output format, making the tool's identity unmistakable and clearly distinct from sibling utilities like base64_encode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: this tool is for computing cryptographic hashes. It does not explicitly discuss when-not-to-use or compare to alternatives, but with no hash-related sibling in the list, the intended usage is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse, validate, and pretty-print JSON strings. Returns whether the input is valid JSON, the parsed data type, keys, and formatted output.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The JSON string to inspect |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral burden. It discloses what the tool returns: validity, parsed data type, keys, and formatted output. It does not detail edge-case handling for invalid JSON, but the explicit mention of returning validity mitigates 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence that leads with core actions and then states return contents. There is no redundant wording or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description gives a useful high-level list of output categories but does not specify exact output field names or behavior on invalid JSON. This leaves a moderate gap for an agent that must programmatically consume the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the single parameter already has a clear description: 'The JSON string to inspect'. The tool description adds overall behavior but no new parameter-level detail, so 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs — parse, validate, and pretty-print — applied to JSON strings, and lists concrete return information such as validity, data type, keys, and formatted output. This clearly identifies the tool's function and distinguishes it from unrelated sibling utilities like base64_encode or text_diff.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly establishes when to use this tool: when a JSON string needs validation, inspection, or formatting. It does not name exclusions or alternative tools, but no sibling tool offers the same JSON-inspection capability, so the usage context is reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Decode and validate a JWT. Returns header, payload, algorithm, signature presence, and — when a secret is provided — whether an HMAC-SHA256/384/512 signature verifies. Also checks exp/nbf/iat timestamps.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | The JWT (three dot-separated base64url parts) | |
| secret | No | Shared secret to verify HS256/HS384/HS512 signatures |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It does well by listing returned fields, the conditional HMAC verification behavior, and the exp/nbf/iat timestamp checks. It does not cover invalid-token behavior or non-HMAC algorithms, but the major behavioral surface is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The primary purpose is front-loaded, and each clause adds meaningful behavioral detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description appropriately explains what the tool returns. It covers the core outputs and timestamp checks. It could be more complete by addressing malformed tokens or failure behavior, but it is sufficient for correct invocation in most cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description reinforces the token and secret meanings but does not add substantial parameter semantics beyond what the schema already provides. It clarifies that secret is optional and only relevant to HMAC verification, which is mild added value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Decode and validate a JWT,' and enumerates what the tool returns. This clearly distinguishes it from siblings like base64_encode and hash_compute, whose purposes are different.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when the tool is useful — for JWT decoding and validation — and explains that providing a secret enables HMAC signature verification. However, it does not explicitly state when to use this tool instead of alternatives or mention any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert Markdown to HTML. Supports ATX headings, fenced code blocks, ordered/unordered lists, blockquotes, horizontal rules, paragraphs, inline code, bold/italic/strikethrough, links, images, and auto-linked URLs. Raw HTML in the source is escaped.
| Name | Required | Description | Default |
|---|---|---|---|
| markdown | Yes | The Markdown source to convert to HTML |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It clearly states the supported Markdown elements and adds an important safety-relevant behavior: raw HTML in the source is escaped. It could mention the return format explicitly, but for a simple conversion tool this is a well-transparent description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with the primary action front-loaded. Every sentence earns its place: the first states the core function, the second lists supported features and the escaping behavior. No redundant or filler content is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, no-output-schema, no-annotation tool, the description covers what input is expected, what conversion features are supported, and a key behavioral detail (HTML escaping). It does not explicitly spell out the response structure, but the tool name and opening sentence make the HTML output clear enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes the single `markdown` parameter, so the baseline is 3. The description adds value by enumerating the Markdown constructs accepted as input, which tells the agent what syntax the parameter may contain beyond the schema's generic wording.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Convert Markdown to HTML.' It then enumerates the supported Markdown features, making the tool's scope unambiguous. None of the sibling tools handle Markdown conversion, so the purpose is fully distinct without needing to name an alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: whenever Markdown needs to be converted to HTML. It does not explicitly state when not to use it or name alternatives, but the sibling tools are all unrelated, so no real ambiguity exists.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test a regular expression against an input string. Returns all matches with their positions and capturing groups.
| Name | Required | Description | Default |
|---|---|---|---|
| flags | No | Regex flags (g, i, m, s, u, etc.) | g |
| input | Yes | The string to test the pattern against | |
| pattern | Yes | The regex pattern to test |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description takes on the transparency burden. It discloses key observable behavior: the tool returns matches, their positions, and capturing groups. It does not mention invalid-pattern or no-match behavior, but for a pure test utility the core behavior is clearly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler: the core action is front-loaded and the return behavior is stated immediately after. Every sentence contributes useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple utility with complete schema coverage, the description adequately supplies the missing output semantics. It would be slightly stronger with edge-case notes like invalid patterns or empty match results, but those are not essential for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with pattern, input, and flags already explained in the schema itself. The description adds no additional parameter-level meaning, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a precise action ('Test a regular expression against an input string') and specifies the output ('all matches with their positions and capturing groups'). This clearly distinguishes it from the unrelated sibling utilities such as hash_compute and csv_parse.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly establishes the tool's context: use it when you need to test a regular expression against a string. There are no competing regex-focused sibling tools, so explicit alternatives are unnecessary, though the description does not state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Increment a semantic version to the next release level: major, minor, patch, premajor, preminor, prepatch, or prerelease. An optional identifier (e.g. beta) seeds/steps the prerelease tag. Returns the resulting version string.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | Which segment to increment | patch |
| version | Yes | Starting version, e.g. 1.2.3 | |
| identifier | No | Prerelease identifier, e.g. beta, rc (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full behavioral disclosure burden. It states that the tool returns the resulting version string and that an identifier seeds/steps the prerelease tag, but it does not mention error behavior for invalid versions, whether the function is pure, or how the identifier behaves with non-prerelease levels. This is adequate but not deeply transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with the action, and free of filler. The enumeration of all seven level values partially duplicates the schema enum, but it is brief and reinforces purpose clarity without becoming bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a relatively simple pure-function tool with no output schema and no annotations, the description covers the core operation, available levels, optional identifier behavior, and return value. It lacks explicit handling of malformed versions or edge cases, but it provides enough for an agent to invoke the tool correctly in the common case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the identifier parameter ('seeds/steps the prerelease tag') with an example ('beta'), and by clarifying that 'version' is the starting point for the increment. This goes slightly beyond the schema's terse parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and object ('Increment a semantic version') and enumerates the exact release levels, so an agent knows precisely what the tool does. This clearly differentiates it from sibling tools like semver_compare, semver_max, and semver_satisfies, which are non-transformational.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied by the operation name and description, but there is no explicit statement of when to use this tool versus alternatives such as semver_compare or semver_satisfies. No exclusions, prerequisites, or routing guidance is provided, leaving the agent to infer the use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two semantic versions with correct SemVer 2.0.0 precedence (e.g. 1.10.0 > 1.9.0 — plain string sort is wrong). Returns -1/0/1, the human relation, and the difference level (major, minor, patch, prerelease).
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | First version, e.g. 1.10.0 | |
| b | Yes | Second version, e.g. 1.9.9 |
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 transparently states the return shape: -1/0/1, the human relation, and the difference level, and it highlights the precedence behavior with a concrete example. It does not cover invalid-version handling, but otherwise it reveals the key behavior an agent needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tightly packed sentences. It front-loads the core purpose, includes a concrete example that demonstrates the critical pitfall, and enumerates the return values without any filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Since there is no output schema, the description sufficiently details the return values and the ordering semantics for a straightforward comparison utility. It omits edge cases like invalid versions or build metadata handling, but for this tool's simplicity and sibling context the description is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters are already documented with examples in the schema itself. The description adds no new parameter-level details beyond 'two semantic versions,' so it neither improves nor detracts from the schema baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Compare') with a specific resource ('two semantic versions') and explicitly names the governing standard (SemVer 2.0.0 precedence). This clearly differentiates it from siblings like semver_bump, semver_max, and semver_satisfies, 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when correct SemVer precedence matters, and it warns that plain string sort is incorrect. It does not explicitly list when-not-to-use or name alternatives, but the context and example sufficiently guide selection for a comparison task.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pick the BEST version from a comma-separated list that satisfies a range — the highest satisfying version (upgrade target). Correctly excludes inapplicable prereleases (npm rules) and rejects invalid list entries. Returns the max satisfying version or "none".
| Name | Required | Description | Default |
|---|---|---|---|
| range | Yes | npm-style range, e.g. "^1.0.0" | |
| versions | Yes | Comma-separated version list, e.g. "1.4.1, 1.5.0-beta.1, 1.6.0" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden and does so well. It discloses npm prerelease exclusion rules, rejection of invalid list entries, and the exact return value ('none' when no match). These are meaningful behavioral traits beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loaded with the core purpose, and every sentence adds value. It avoids restating the tool name and packs key behaviors and return semantics into three tight sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter pure function with no output schema, the description fully covers inputs, behavior, validation rules, and return value. The tool is complete enough for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with both parameters documented by example. The description reinforces that versions are comma-separated and range is npm-style, but adds little beyond the schema. Baseline 3 is appropriate because the schema already handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the specific operation: pick the highest version satisfying a range from a comma-separated list. It distinguishes itself from the sibling semver tools by emphasizing 'max satisfying version' as an upgrade target.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: when you need the best/highest satisfying version, such as an upgrade target. It does not explicitly name alternatives or state when not to use it, but the purpose is distinct enough from semver_satisfies, semver_compare, and semver_bump that the usage context is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test whether a version satisfies an npm-style version range (caret ^, tilde ~, >=, <=, <, >, =, hyphen ranges, x-wildcards like 1.2.x, &&/space AND and || OR). Returns whether it satisfies plus the expanded operator bounds so the caller can see why. Prerelease versions only match a range that names their major.minor.patch with an explicit prerelease (same as npm).
| Name | Required | Description | Default |
|---|---|---|---|
| range | Yes | The range to test against, e.g. "^1.2.3 || ~2.0.0" | |
| version | Yes | The version to test, e.g. 1.9.9 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full disclosure burden and does so well. It explains the return value ('whether it satisfies plus the expanded operator bounds'), and it documents the nuanced prerelease matching rule, which matches npm behavior. It does not describe error handling or exact return format, but for a two-string utility the key behavioral traits are covered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core verb and resource. The second sentence states the return value, and the third covers an important edge case. The operator list is dense but directly useful; no sentence is wasted, though it could be slightly more scannable with line breaks or bullet points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only two string parameters, no output schema, and no annotations, the description covers the essential ground: input syntax, return value, and a notable edge case. The only remaining gaps are exact output shape and error behavior, which are minor for a pure version-checking utility. Sibling tools like semver_compare and semver_bump are clearly different in purpose from what is described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already describes both parameters with examples. The description adds meaningful detail beyond the schema by listing exactly which operator forms are accepted (caret, tilde, hyphen ranges, x-wildcards, AND/OR) and by explaining how prerelease versions are matched. This genuinely helps an agent construct correct inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Test whether a version satisfies an npm-style version range.' It also enumerates the accepted range syntax, which clearly separates this from sibling tools like semver_compare, semver_bump, and semver_max. An agent can immediately understand what operation is performed and what input shapes are expected.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the purpose clear enough that an agent can infer when to choose it — whenever a version needs to be checked against a range. However, it does not explicitly distinguish this from semver_compare or semver_max, nor does it state when not to use it. The guidance is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two texts and show the differences line by line. Returns added/removed/unchanged counts and the diff output.
| Name | Required | Description | Default |
|---|---|---|---|
| text1 | Yes | The original text | |
| text2 | Yes | The modified text to compare against the original |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It discloses that comparison is line-by-line and that the return includes added/removed/unchanged counts and diff output. This gives an agent a reasonable expectation of the tool's behavior, though it does not cover edge cases like whitespace handling or exact diff formatting.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The primary action is front-loaded, and the return-value summary follows immediately. Every sentence adds useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-string, read-only diff tool with no output schema, the description adequately explains both the input relationship and the return values. It could be slightly more explicit about the exact structure of the diff output, but it is complete enough for correct tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with text1 described as 'The original text' and text2 as 'The modified text to compare against the original.' The tool description adds no extra parameter meaning beyond the schema, so 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Compare'), a resource ('two texts'), and the mode of comparison ('line by line'). It also differentiates itself from sibling tools, which are all conversion, parsing, or generation utilities rather than text comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the use case obvious: use this tool when you need to compare two texts and see differences alongside added/removed/unchanged counts. It does not explicitly name alternatives or exclusions, but the context among sibling tools is clear enough that no alternative would be considered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert and explain a date/time: epoch ms/s, ISO 8601, or relative ("2h ago"). Cross-convert timezones (IANA names like America/New_York). Naive inputs treated as UTC.
| Name | Required | Description | Default |
|---|---|---|---|
| to_tz | No | IANA timezone to convert to (comma-separated for several), e.g. Europe/Berlin. Default: UTC only | |
| value | Yes | Time input: ISO 8601, epoch seconds or milliseconds, or relative like "2h ago" / "in 3 days" / "now" | |
| from_tz | No | IANA timezone the input is in, e.g. America/New_York. Use when the input is a wall-clock time without an offset |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral disclosure burden. It discloses accepted input representations, timezone conversion behavior, and the important default that naive inputs are treated as UTC. It does not describe output shape, but the parsing and timezone defaults are substantial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise, information-dense sentences front-load the core function and formats. The timezone default is included without filler or unnecessary repetition of the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a three-parameter conversion tool with no output schema, the description plus schema covers the required value, optional timezone parameters, accepted input types, and the UTC default. It does not describe the exact return shape, but it gives an agent enough to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3, but the description adds a meaning not present in the schema: naive inputs are treated as UTC. It also reinforces relative-time parsing and timezone cross-conversion, going slightly beyond the structured parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb and resource ('Convert and explain a date/time') and enumerates accepted input forms (epoch ms/s, ISO 8601, relative) plus timezone cross-conversion. This clearly distinguishes it from the sibling converter utilities, which are all named for different formats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context: use this when converting or explaining date/time values, including timezone conversion with IANA names. It does not explicitly name alternatives or exclusions, but the listed formats and timezone behavior make the intended use evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Parse and analyze a URL. Returns protocol, hostname, port, path, query parameters, fragment, and validation status.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to analyze (e.g. https://example.com/path?query=1) |
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 does state that the tool returns URL components and a validation status, making it clear this is a parse-and-report operation. However, it does not disclose how invalid URLs are handled (return status vs. error), whether any network access occurs, or the exact nature of the validation status, leaving some behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single well-structured sentence that front-loads the action and then lists concrete outputs. There is no filler, no repetition, and no unnecessary verbiage; every element contributes to understanding what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter, no annotations, and no output schema, the description is largely complete: it identifies the resource and enumerates all output categories an agent would need to know about. The main gap is that it does not specify the data type or format of the validation status, which could matter when interpreting results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage for the single url parameter, including type, required flag, and a concrete example. The description restates that a URL is parsed but adds no parameter-specific detail beyond what the schema supplies, which meets the baseline of 3 for full schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with a specific verb phrase, 'Parse and analyze a URL,' and explicitly names the resource and the returned components: protocol, hostname, port, path, query parameters, fragment, and validation status. This clearly differentiates it from sibling tools, which all operate on different data types such as base64, cron, CSV, JSON, JWT, Markdown, regex, semver, and UUID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the use case—when you need to break a URL into its components or validate it—but provides no explicit when-to-use or when-not-to-use guidance, nor does it reference any alternatives. The sibling tools are distinct enough that the right choice is fairly obvious, so the absence is not severe, but the guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate RFC 4122 version 4 UUIDs and/or cryptographically random URL-safe tokens. Batch count is clamped to 1-10.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | uuid = v4 UUIDs, token = random base64url tokens, both = pairs | uuid |
| count | No | How many to generate (clamped 1-10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With zero annotations provided, the description carries the full burden of behavioral disclosure. It discloses the exact UUID standard (RFC 4122 v4), the randomness source (cryptographically secure), the token encoding (URL-safe base64url), and the effective batch cap (clamped to 1-10) — the clamping note being genuinely valuable since the schema's min/max of ±9e15 would otherwise mislead. It stops short of stating the output shape (single value vs array, pair structure for 'both') or error behavior for out-of-range counts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero filler, front-loaded with the primary action and format details in the first sentence. The clamping disclosure in the second sentence earns its place by flagging a runtime constraint an agent must respect when choosing batch sizes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool (2 optional params, no output schema), the description covers purpose, format, and clamping, but omits the return shape — a real gap since an agent cannot know whether count=1 yields a bare string or an array, or what 'pairs' looks like for kind='both'. It also leaves out-of-range count behavior ambiguous: the schema permits negatives and enormous values, and the description does not clarify whether those error out or are silently clamped.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. Both parameters are already documented in the schema — 'kind' has an enum with per-value descriptions and 'count' carries its own '(clamped 1-10)' note — so the free-text description adds no meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Generate'), resource ('RFC 4122 version 4 UUIDs and/or cryptographically random URL-safe tokens'), and a clear scope. This distinguishes it from all 15 siblings — none generate random identifiers, and the closest sibling hash_compute clearly computes hashes from user input rather than minting values. An agent can select this tool without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is given — no alternatives are named and no use cases are described. Usage is implied rather than stated: the purpose is unambiguous and no sibling functionally overlaps, so misrouting is unlikely. However, the description itself earns no credit for stating any selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
time_convertbase64_encodecolor_convertcron_parsecsv_parsehash_computejson_inspectjwt_decodemarkdown_to_htmlregex_testsemver_bumpsemver_comparesemver_maxsemver_satisfiestext_diffurl_analyzeuuid_mintClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Developer utilities: color conversion, WCAG contrast, timestamps, UUIDs, and hashing.
238+ dev tools via MCP: JSON, QR, PDF, DNS, hash, UUID, code review, JWT, SSL, WHOIS, and more
Developer toolkit: UUID, timestamp, unit conversion, JSON tools, and QR code (SVG). Free.
Remote MCP server: 10 developer utilities (base64, JWT, DNS, UUID, URL, JSON, UA, IP lookup).
Add one secure layer between your agents and this server.
Each tool targets a distinct operation—encoding, color conversion, parsing, hashing, JWT validation, Markdown rendering, regex testing, SemVer operations, diffing, URL analysis, and UUID generation. The four SemVer tools are related but cleanly separated by action (bump vs compare vs max vs satisfies), and descriptions clarify their boundaries.
Tools overwhelmingly follow an object_verb snake_case convention (base64_encode, csv_parse, regex_test, semver_bump). Semver_max and semver_satisfies deviate slightly from the imperative verb pattern, but the overall naming is predictable and searchable.
At 16 tools, the server is slightly above the ideal 3–15 tool range but each utility earns its place for a general-purpose developer toolbox. No tools feel redundant, and the count remains manageable because the names and domains are highly scannable.
The toolkit covers a solid breadth of common developer utilities: encodings, common formats (JSON, CSV, Markdown), hashing/JWT, regex, SemVer, cron, URL, UUIDs, and diffing. It lacks some fringe converters such as YAML/XML parsing or HTML escape/unescape, but these are minor gaps that agents can work around rather than dead ends.
