Machine-readable utilities and datasets for AI agents. MCP + HTTP + x402.
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Average 4.4/5 across 18 of 18 tools scored.
Each tool targets a distinct data format and operation, with explicit 'Do not use when' cross-references to prevent confusion. For example, base64_encode/decode, url_encode/decode, and timestamp_convert/timezone_convert are clearly separated, and the various validators (ISBN, Luhn, UUID, JSON) apply to different identifiers.
Most tool names follow an object_operation pattern (e.g., base64_decode, country_lookup, timestamp_convert), using lowercase with underscores. The main deviation is countries_bulk, which uses a noun+adjective form without an explicit operation, making it inconsistent with the verb-like operations used elsewhere.
With 18 tools, the server falls into the 16-25 range which feels heavy for a utility collection. While each tool is individually useful and the scope is broad, the count is higher than typical for a well-focused server and may overwhelm agents scanning the available options.
The set provides solid coverage of encoding/decoding, validation, lookups, and conversions, with paired encode/decode and convert functions. However, some common utilities such as hashing, HTML entity encoding, UUID generation, or email validation are absent, leaving minor gaps for agents that need those operations.
base64_decodeAInspectDecode a Base64 string into UTF-8 text and report invalid payloads as errors when recovering embedded credentials, tokens, or transport-encoded content.
Use when:
Decode this Base64 string to UTF-8 text
Recover plain text from a Base64-encoded payload
Check whether a Base64 string is valid and decode it
Do not use when:
Encode plain text to Base64 (use base64_encode)
Decode percent-encoded URL components (use url_decode)
Decrypt ciphertext—Base64 decoding is not decryption
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Base64 text to decode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses error behavior (invalid payloads reported as errors) and clarifies the output encoding (UTF-8). While it doesn't mention padding or URL-safe variants, for a simple decode tool the provided context is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a short headline, bulleted use-cases, and clear exclusions. It is concise, every sentence contributes meaning, and there is no fluff 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?
For a single-parameter utility tool with no output schema and no annotations, the description is remarkably complete. It covers purpose, usage, alternatives, limitations (not decryption), and error behavior, leaving no significant gaps for an agent to interpret.
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 parameter 'value' is described as 'Base64 text to decode'. The description's use-case examples add contextual meaning (e.g., recovering credentials), but it doesn't introduce new syntactic or format details beyond the schema. Baseline 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 opens with a specific verb ('Decode'), a clear resource ('Base64 string'), and a defined outcome ('into UTF-8 text'), plus error behavior ('report invalid payloads as errors'). This immediately distinguishes it from siblings like base64_encode and url_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 'Use when' list provides concrete scenarios (recovering plain text, checking validity), and the 'Do not use when' section explicitly names alternative tools (base64_encode, url_decode) and clarifies it's not decryption. This gives clear guidance on when to select this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
base64_encodeAInspectConvert a UTF-8 string into standard Base64 when you need a portable text encoding for credentials, binary-safe transport, or embedding data in JSON/HTTP fields.
Use when:
Encode this UTF-8 string as Base64
Convert plain text into standard Base64 for transport
Produce a Base64 representation of a credential or payload string
Do not use when:
Decode Base64 back to text (use base64_decode)
Percent-encode URL components (use url_encode)
Hash or encrypt data for security—Base64 is encoding only
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Text to encode |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses that Base64 is encoding-only, not for hashing/encryption, and explains use cases for credentials and binary-safe transport. It doesn't cover edge cases, but for a pure encode tool, 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?
The description is well-structured but the 'Use when' list contains three near-identical bullets, making it slightly redundant. Still, it's compact and front-loaded with the main purpose.
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 utility, the description covers purpose, usage scenarios, and explicitly excludes related tools. No output schema is needed for this simple tool.
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 covers the only parameter 'value' with 'Text to encode'. The description adds the UTF-8 encoding context, clarifying the expected input format. This adds semantic value beyond the schema.
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 'Convert a UTF-8 string into standard Base64', which is a specific verb+resource. It distinguishes itself from siblings like base64_decode and url_encode by explicitly naming them in the 'Do not use when' section.
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?
Provides a 'Use when' and 'Do not use when' list, explicitly naming base64_decode and url_encode as alternatives. This directly answers when to use vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
countries_bulkAInspectDownload the complete versioned countries dataset as JSON for caching, offline use, or multi-country analysis when a single country_lookup call is not enough.
Use when:
Download the full countries dataset as JSON
Cache the complete versioned countries snapshot for offline use
Analyze or filter many countries without repeated single-code lookups
Do not use when:
Look up metadata for a single country by ISO code (use country_lookup instead)
Look up one currency or language code (use currency_lookup / language_lookup)
Fetch live geopolitical or exchange-rate feeds
Payment: 0.001 USDC via x402 on eip155:8453. No account. No API key. No signup.
| Name | Required | Description | Default |
|---|---|---|---|
| version | No | Dataset version to retrieve. Defaults to 1. | 1 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses key behaviors: full-dataset download, versioning, caching/offline suitability, and payment requirement (0.001 USDC via x402 with no auth). It also mentions it's not a live feed, which sets expectations.
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 well-structured with a lead sentence, bulleted usage sections, and payment note. It is slightly longer than minimal but every section adds value, and the most important info 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?
Given the simplicity of the tool (one optional parameter, no output schema), the description fully covers what, when, cost, and authentication expectations. No significant gaps remain.
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 only parameter 'version' is already fully described in the schema (default '1', description). The tool description adds no additional parameter context, so it stays at the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Download' with a clear resource ('complete versioned countries dataset') and format ('as JSON'). It also differentiates from sibling tools by stating it's for when a single country_lookup call is insufficient.
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?
Explicit 'Use when' and 'Do not use when' sections list concrete scenarios and name alternative tools (country_lookup, currency_lookup, language_lookup). This provides clear decision guidance that exceeds most tool descriptions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
country_lookupAInspectResolve an ISO 3166-1 alpha-2 country code (for example CA, US, JP) to official name, region, currency, calling code, languages, and related country metadata when you already have the two-letter code.
Use when:
What currency and calling code does country code JP use?
Get official name, region, and languages for ISO country CA
Look up standardized metadata for a two-letter ISO 3166-1 country code
Do not use when:
Download the full countries dataset for offline or multi-country use (use countries_bulk)
Look up currency symbol or decimals by ISO 4217 code alone (use currency_lookup)
Search for a country when you only know its English name and not the ISO code
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ISO 3166-1 alpha-2 country code, for example CA or JP |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses the output metadata categories and the input requirement. It does not cover edge cases like case-insensitivity or errors, but for a simple read-only lookup the behavior is sufficiently 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 concise, front-loaded with the primary purpose, and uses clear sections for use/non-use. Every sentence earns its place.
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 lookup tool with full schema coverage and no output schema, the description adequately explains what it does, when to use it, and what it returns. No gaps remain.
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 documents the parameter with pattern and examples. The description adds the same examples (CA, US, JP) but no additional meaning beyond the schema's 100% coverage. Baseline 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 clearly states the tool resolves ISO 3166-1 alpha-2 codes to official name, region, currency, calling code, languages, and related metadata. It uses a specific verb (resolve) and resource (country code) and is differentiated from sibling tools via the 'Do not use when' section.
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?
Provides explicit 'Use when' scenarios with example queries, and 'Do not use when' conditions that name alternative tools (countries_bulk, currency_lookup). This fully guides selection vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
currency_lookupAInspectResolve an ISO 4217 currency code such as USD, EUR, or JPY to its name, symbol, decimal places, and which countries use it when you need currency metadata rather than a live exchange rate.
Use when:
What is the symbol and number of decimal places for EUR?
Which countries use currency code JPY?
Get ISO 4217 name and metadata for USD
Do not use when:
Convert amounts between currencies or fetch live FX rates
Find which currency a country uses when you only have a country code (use country_lookup)
Validate payment-card-like numbers (use luhn_validate)
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description does the heavy lifting. It clearly discloses that the tool returns metadata and is not for live rates. It does not mention error handling or case sensitivity, but for a simple lookup this is adequate. It could go further, but it does not contradict anything.
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 well-structured with a clear lead sentence and organized use/do-not-use lists. Every sentence adds value, and it remains succinct despite covering alternatives.
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 parameter, no output schema), the description fully covers purpose, usage, exclusions, and return content. It is complete for an agent to select and invoke 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?
The schema provides only min/max length for 'code' with 0% description coverage. The description compensates by giving examples (USD, EUR, JPY) and explaining the code's role as an ISO 4217 currency code, adding meaning beyond the schema.
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 the tool resolves ISO 4217 currency codes to metadata like name, symbol, decimal places, and countries, distinguishing it from exchange rate tools and sibling lookups. The verb 'resolve' is specific and the resource is well-defined.
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 explicit 'Use when' and 'Do not use when' sections with concrete example queries and states alternatives like country_lookup and luhn_validate. This fully guides the agent on when to select this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
http_status_lookupAInspectExplain what a numeric HTTP status code such as 404 or 429 means by returning its standard reason phrase and a short description for debugging or API responses.
Use when:
What does HTTP status 404 mean?
Get the standard name and description for status code 429
Explain an HTTP response code returned by an API
Do not use when:
Decide whether a request succeeded without needing the status name
Look up MIME types or content-type headers (use mime_lookup)
Diagnose TLS, DNS, or network failures that are not HTTP status codes
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It explains the tool returns a reason phrase and short description, which is useful behavioral context. While it doesn't detail error handling or edge cases, for a simple lookup tool the description is sufficiently 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 well-structured with a clear main sentence followed by concise bullet lists for use and non-use cases. It is appropriately sized, front-loads the purpose, and every sentence adds value.
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 lookup tool with no output schema and no annotations, the description fully covers what the tool does, when to use it, when not to, and what it returns. The guidance is complete and self-contained.
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 only defines 'code' as an integer with range, and schema description coverage is 0%. The description compensates by explaining that the parameter is a numeric HTTP status code and providing concrete examples (404, 429), which adds meaning beyond the raw schema.
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 the tool explains numeric HTTP status codes by returning the standard reason phrase and a short description. It explicitly mentions examples (404, 429) and distinguishes itself from sibling tools by specifying it is for HTTP status codes, not MIME types or network diagnostics.
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?
Provides explicit 'Use when' and 'Do not use when' sections with concrete examples. It names an alternative tool (mime_lookup) for MIME type lookups and clearly excludes unrelated uses, giving the agent strong guidance on when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
isbn_validateAInspectVerify whether an ISBN-10 or ISBN-13 passes checksum rules and return normalized digits when checking book identifiers before catalog or purchase flows.
Use when:
Is this ISBN valid?
Check the checksum for an ISBN-13 book identifier
Normalize and validate an ISBN-10 or ISBN-13 string
Do not use when:
Look up book title, author, or cover metadata from an ISBN
Validate UUIDs (use uuid_validate) or payment-card Luhn numbers (use luhn_validate)
Validate arbitrary barcode formats that are not ISBN-10/13
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ISBN-10 or ISBN-13 candidate |
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 describes the core verification and normalization behavior, but it does not specify what happens for invalid ISBNs, the exact return format (e.g., boolean vs. normalized string), or whether input formatting like hyphens is handled. This is adequate but leaves key behavioral details unspecified.
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 well-structured with a concise opening sentence and clear bullet lists for usage. Every sentence serves a distinct purpose, with no fluff or repetition. It is appropriately sized for the tool's simple one-parameter design.
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 validation tool with one parameter, the description covers purpose, usage scenarios, and exclusions well. The only gap is the lack of detail about return behavior or edge-case handling, but given the tool's low complexity and the schema's full parameter coverage, it is mostly 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?
The schema already provides 100% coverage for the single parameter 'value' with description 'ISBN-10 or ISBN-13 candidate'. The description's mention of verifying and normalizing an ISBN-10/13 string adds only marginal context 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 explicitly states the tool 'Verify whether an ISBN-10 or ISBN-13 passes checksum rules and return normalized digits', using a specific verb and resource. It also distinguishes itself from sibling tools by naming alternatives like uuid_validate and luhn_validate in the exclusions, making its purpose unmistakable.
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 'Use when' and 'Do not use when' sections. It gives concrete scenarios ('Check the checksum for an ISBN-13 book identifier') and explicitly names alternative tools for other validation tasks, offering strong guidance on when to use this tool versus others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
json_validateAInspectParse a string as JSON to confirm it is well-formed and return a normalized representation when possible before further processing or storage.
Use when:
Is this string valid JSON?
Parse and normalize a JSON payload before processing
Check whether agent or user-supplied text is well-formed JSON
Do not use when:
Validate against a specific JSON Schema or OpenAPI model beyond well-formedness
Encode or decode Base64 (use base64_encode / base64_decode)
Validate UUID or ISBN identifier formats
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
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 reveals that the tool normalizes output 'when possible' and positions it for pre-processing/storage, but it does not specify error handling or return format on invalid JSON. Adds useful context beyond a bare one-liner.
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 well-structured with a clear opening sentence followed by bullet-style 'Use when' and 'Do not use when' lists. Every sentence adds value, and the format aids scannability. No filler or redundant content.
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 single-param tool with no output schema, the description covers purpose, usage contexts, exclusions, and normalization behavior. It lacks explicit return/error details, but the use-case framing and exclusions make it sufficiently complete for an agent to select and invoke 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 0%, so the description must compensate. It implies 'value' is the string to parse via 'Parse a string as JSON', but does not explicitly describe the parameter's meaning, edge cases, or accepted encodings. The schema only provides the type, so the description adds minimal semantic value beyond the tool name.
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-resource pair: 'Parse a string as JSON to confirm it is well-formed and return a normalized representation when possible.' It clearly states what the tool does and distinguishes itself from sibling tools by explicitly excluding schema validation, Base64, and identifier format checks.
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 explicit 'Use when' and 'Do not use when' sections, listing concrete scenarios and naming alternative tools (e.g., base64_encode, base64_decode). This is the gold standard for usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
language_lookupAInspectResolve an ISO 639-1 language code such as en, fr, or ja to its English name and native name when you need language metadata from a two-letter code.
Use when:
What language does ISO 639-1 code ja refer to?
Get the native name for language code fr
Resolve a two-letter language code to its English and native names
Do not use when:
Translate text between languages
Detect the language of arbitrary free-form text
Look up country languages from a country code (use country_lookup)
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly states the tool resolves codes to English and native names, and explicitly lists non-goals (translation, detection). It does not mention behavior for invalid codes or case sensitivity, but for a simple read-only lookup, the description provides sufficient transparency about what the tool does and does not do.
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 highly concise and well-structured: an opening sentence states the core purpose, followed by bulleted 'Use when' and 'Do not use when' sections. Every sentence adds value, and the structure makes it easy to scan quickly.
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 lookup tool with one parameter and no output schema, the description is complete. It explains the input parameter, the output (English name and native name), provides usage examples, and clarifies exclusions. No additional documentation is needed for an agent to correctly invoke it.
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 0%, so the description must compensate. It does so by explaining the parameter 'code' is an ISO 639-1 two-letter code with examples (en, fr, ja), which adds meaning beyond the raw schema constraints (minLength 2, maxLength 3). However, it does not discuss case sensitivity or the possibility of three-letter codes, which would be useful given the schema allows up to 3 characters.
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 'Resolve' with a clear resource 'ISO 639-1 language code' and specifies the output: English name and native name. It distinguishes itself from siblings by explicitly stating what it does not do (translate, detect) and pointing to country_lookup for country-language lookups, which is strong differentiation.
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 includes explicit 'Use when' and 'Do not use when' sections with concrete examples (e.g., 'What language does ISO 639-1 code ja refer to?') and identifies an alternative tool (country_lookup) for country-language queries, providing clear guidance on when to select this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
luhn_validateAInspectRun the Luhn checksum on a numeric string such as a payment-card-like identifier to verify the check digit before accepting or storing the value.
Use when:
Does this number pass a Luhn check?
Validate a payment-card-like identifier checksum
Verify a numeric ID that uses a Luhn check digit
Do not use when:
Charge a card, tokenize payments, or call a payment processor
Validate ISBNs (use isbn_validate) or UUIDs (use uuid_validate)
Look up currency metadata (use currency_lookup)
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Numeric string to validate with Luhn |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It clearly explains the tool's core behavior (validating check digits) and implies a yes/no outcome via usage examples. However, it does not explicitly state the return type (e.g., boolean) or how invalid inputs (like non-numeric strings) are handled, which would be beneficial for full transparency.
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 well-structured and appropriately sized, with a clear opening sentence and organized 'Use when' / 'Do not use when' sections. Every sentence adds value without redundancy or fluff.
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 single-parameter tool with no output schema, the description is mostly complete. It explains the purpose, usage, and exclusions. However, it does not explicitly state the return format (e.g., a boolean indicating validity), which would make it fully self-contained for an agent. This minor gap prevents a perfect score.
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 the parameter 'value' already described as 'Numeric string to validate with Luhn'. The description adds contextual examples ('payment-card-like identifier') but does not add new format or syntax details beyond what the schema provides. Thus the schema does the heavy lifting, and the description meets the baseline without significantly enhancing parameter understanding.
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 the tool's function: running the Luhn checksum on a numeric string to verify the check digit. It uses a specific verb ('Run') and resource ('numeric string'), and distinguishes itself from sibling validators like isbn_validate and uuid_validate.
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?
Explicit 'Use when' and 'Do not use when' sections provide clear guidance on appropriate usage, including concrete examples like 'Does this number pass a Luhn check?' and explicit alternatives for ISBNs, UUIDs, and currency lookup. This fully satisfies the dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mime_lookupAInspectMap a file extension such as webp or png, or a MIME type string, to curated Content-Type metadata when setting headers, validating uploads, or choosing a media type.
Use when:
What MIME type should I use for a .webp file?
Look up metadata for Content-Type application/json
Resolve a file extension to the correct MIME type for an upload or response header
Do not use when:
Sniff or detect MIME type from raw file bytes
Encode or decode Base64 file payloads (use base64_encode / base64_decode)
Look up DNS top-level domain metadata (use tld_lookup)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses a key boundary: 'Do not use when: Sniff or detect MIME type from raw file bytes', which clarifies that this tool does not perform byte-level detection. Phrases like 'Map ... to curated Content-Type metadata' imply a read-only operation, but it does not explicitly state 'read-only' or mention side effects, rate limits, or output format. Given the tool's simplicity, this is a minor shortfall.
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 starts with a concise definition, followed by well-organized 'Use when' and 'Do not use when' bullet lists. Every sentence contributes value, and the structure makes it instantly scannable. It is appropriately sized for the tool's complexity.
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 a single parameter, no output schema, and no annotations, the description is remarkably complete. It covers the purpose, provides lookup examples, lists exclusions, and names sibling tools for redirection. The agent gets enough context to invoke the tool correctly without missing critical details.
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 only says 'query' is a string with minLength 1, and schema coverage is 0%. The description compensates by clarifying acceptable input forms: 'a file extension such as webp or png, or a MIME type string' and gives a real-world example ('Content-Type application/json'). It adds useful context beyond the schema, though it doesn't cover every edge (e.g., dot handling, case sensitivity).
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 and clear statement: 'Map a file extension such as webp or png, or a MIME type string, to curated Content-Type metadata.' This clearly indicates the verb (map), resource (file extension/MIME type to metadata), and purpose, and it is distinct from sibling lookup tools like tld_lookup or 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 explicitly provides 'Use when' and 'Do not use when' sections with concrete examples like 'What MIME type should I use for a .webp file?' and alternatives such as 'use base64_encode / base64_decode' and 'use tld_lookup'. This gives the agent clear guidance on when to select this tool vs others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
timestamp_convertAInspectConvert Unix epoch seconds or milliseconds to ISO-8601, or an ISO-8601 datetime to Unix epoch values, when bridging APIs that disagree on time formats.
Use when:
Convert this Unix timestamp to ISO-8601
Turn an ISO-8601 datetime into epoch milliseconds
Bridge APIs that mix Unix seconds and ISO-8601 strings
Do not use when:
Convert a datetime between IANA timezones while preserving the instant (use timezone_convert)
Convert length, mass, or temperature units (use unit_convert)
Interpret relative phrases like 'next Tuesday' or 'in 3 hours'
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Unix timestamp or ISO-8601 datetime | |
| input_format | Yes | Format of value |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the bidirectional conversion and the API-bridging context, but it does not state what output format is returned (e.g., whether an input of unix_seconds produces an ISO-8601 string) or any error handling/edge cases. This leaves 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 well-structured with a one-sentence summary followed by bullet-point use/not-use cases. Every sentence adds value, and the structure is easy to parse.
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?
Despite lacking an output schema, the description comprehensively covers what the tool does and when to use it. However, it omits details about the return value format and potential precision issues, which would be helpful for a converter. It is still quite complete given its simplicity.
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 covers both parameters with descriptions, but the description adds usage context by giving concrete conversion directions ('Unix timestamp to ISO-8601', 'ISO-8601 datetime into epoch milliseconds') that clarify how the parameters are used together. This complements the enum values.
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 the tool's function: converting between Unix epoch seconds/milliseconds and ISO-8601 datetime. It uses a specific verb 'Convert' and identifies the resource (time formats). It also distinguishes itself from sibling tools like timezone_convert and unit_convert in the 'Do not use when' section.
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 has explicit 'Use when' and 'Do not use when' sections with concrete query examples and alternative tool names (timezone_convert, unit_convert). This gives the agent clear guidance on when to select this tool over its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
timezone_convertAInspectConvert an ISO-8601 datetime from one IANA timezone to another while preserving the same instant, including daylight-saving-aware wall times such as Toronto to Tokyo.
Use when:
What time is 3pm Toronto in Tokyo?
Convert an ISO-8601 datetime from America/New_York to Europe/London
Show the same instant in another IANA timezone with DST-aware offsets
Do not use when:
Only convert between Unix epoch and ISO-8601 without changing timezone (use timestamp_convert)
Convert length, mass, or temperature units (use unit_convert)
Guess a city timezone from a postal address without an IANA zone id
| Name | Required | Description | Default |
|---|---|---|---|
| datetime | Yes | ISO-8601 datetime, with or without offset | |
| to_timezone | Yes | IANA timezone for output | |
| from_timezone | Yes | IANA timezone for interpreting naive datetimes |
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 reveals that the conversion preserves the same instant and is DST-aware, which is critical context beyond the schema. However, it does not mention potential edge cases like ambiguous/nonexistent times, error handling for invalid timezone IDs, or the exact output structure, leaving a small gap for a timezone conversion tool.
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 well-structured and front-loaded with the core action in the opening sentence. The 'Use when' and 'Do not use when' sections are concise, scannable, and each bullet adds direct value. No superfluous sentences; the length is justified by the clarity it provides for tool selection.
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 tool has moderate complexity with DST awareness and three required parameters. The description covers purpose, usage guidelines, and key behavioral traits, and the schema covers parameter semantics. There is no output schema, but the description does not explicitly state the return format (e.g., does it return the converted ISO string with an offset?), which would enhance completeness. Overall, it is nearly complete but leaves a minor gap.
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 each parameter (datetime, from_timezone, to_timezone). The description adds example usage and clarifies the role of from_timezone for naive datetimes, but this largely mirrors the schema descriptions. It does not introduce new parameter-level semantics beyond what the schema provides, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Convert'), a clear resource ('ISO-8601 datetime'), and the exact transformation ('from one IANA timezone to another while preserving the same instant'). It also highlights DST-aware behavior and gives a concrete example (Toronto to Tokyo), which clearly distinguishes it from sibling tools like timestamp_convert and unit_convert.
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 explicit 'Use when' examples (e.g., 'What time is 3pm Toronto in Tokyo?') and explicit 'Do not use when' exclusions with named alternatives (timestamp_convert, unit_convert). This gives an agent clear decision criteria for selecting this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tld_lookupAInspectIdentify what a DNS top-level domain such as com, io, or ai is classified as and commonly used for when classifying domains or answering TLD questions.
Use when:
What kind of TLD is .io?
Is .ai a country-code or generic top-level domain?
Get curated metadata for DNS TLD com
Do not use when:
Check whether a full domain name is registered or available
Resolve DNS records or WHOIS ownership
Look up MIME types for file extensions (use mime_lookup)
| Name | Required | Description | Default |
|---|---|---|---|
| tld | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It clearly states the tool returns classification and common usage ('curated metadata'), which is transparent for a simple lookup. However, it does not disclose edge cases (e.g., missing TLDs), data source, or error behavior, leaving some behavioral aspects implicit.
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 concise and well-structured: a one-sentence purpose followed by clear 'Use when' and 'Do not use when' bullet points. Every sentence earns its place, and the front-loaded purpose ensures quick comprehension.
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 one-parameter lookup tool with no output schema, the description covers the essentials: purpose, usage context, exclusions, and a hint of return value ('classified as and commonly used for'). It is adequately complete, though a bit more detail on return format would push it to 5.
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 has 0% description coverage, but the description compensates by explaining the parameter through examples ('com', 'io', 'ai') and clarifying that it takes a TLD without a leading dot. The parameter name 'tld' is self-explanatory, and the description adds usage context beyond the schema.
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 ('Identify') and resource ('DNS top-level domain'), clearly stating the tool classifies TLDs and describes their common usage. It distinguishes itself with concrete examples (com, io, ai) and is clearly distinct from sibling tools like mime_lookup.
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 'Use when' section provides explicit example queries ('What kind of TLD is .io?', 'Is .ai a country-code or generic top-level domain?'), and the 'Do not use when' section lists exclusions with an explicit alternative for MIME types (mime_lookup). This fully satisfies the dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
unit_convertAInspectConvert a numeric value between supported length, mass, or temperature units—for example miles to kilometers or Celsius to Fahrenheit—when you need a deterministic unit conversion.
Use when:
Convert 10 miles to kilometers
What is 25°C in Fahrenheit?
Convert a mass value between kilograms and pounds
Do not use when:
Convert currencies or fetch exchange rates (use currency_lookup for metadata only)
Convert timestamps or timezones (use timestamp_convert / timezone_convert)
Convert unsupported unit categories outside length, mass, and temperature
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | ||
| to_unit | Yes | ||
| from_unit | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral disclosure burden. It adds useful context (deterministic conversion, supported categories), but does not state what happens with invalid unit strings, case sensitivity, precision, or error behavior. This is a moderate gap, not a contradiction.
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 well-structured, concise, and front-loaded. The main purpose is stated first, followed by quick examples and clear exclusions. Every sentence earns its place with no redundant content.
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 tool is simple but the description still lacks a full list of supported units and any hint about return values or error handling. It covers usage boundaries well, but not enough to fully compensate for the missing schema descriptions and output schema.
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 0% and the description does not compensate by defining accepted values for from_unit/to_unit. Examples imply orientation (value, from_unit, to_unit) but no list of supported unit strings, abbreviations, or format requirements. This leaves the parameters underspecified.
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 the verb 'Convert' and the resource 'a numeric value between supported length, mass, or temperature units', with concrete examples. It explicitly differentiates from sibling tools by listing excluded conversions (currencies, timestamps, timezones) and suggesting alternatives.
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 'Use when' and 'Do not use when' sections, giving concrete example queries and explicitly naming alternative tools (currency_lookup, timestamp_convert, timezone_convert). This is exactly the kind of guidance needed for selecting this tool vs siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
url_decodeAInspectDecode a percent-encoded URL component into plain text when reading query parameters, path segments, or form values that contain escaped characters.
Use when:
Decode this percent-encoded URL component
Convert %20-style escapes back to plain text
Recover the original string from an encoded query parameter
Do not use when:
Percent-encode text for a URL (use url_encode)
Decode Base64 payloads (use base64_decode)
Parse HTML entities that are not URL percent-encoding
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It adds context about when decoding is appropriate but does not discuss edge cases, error handling, or potential side effects (e.g., whether plus signs are decoded). Since the core behavior is straightforward, the description is adequate 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 well-structured with bullets, but the 'Use when' bullets repeat the same idea as the opening sentence. A tighter version could remove redundancy while remaining helpful.
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 decode tool with no output schema, the description provides sufficient context on purpose and usage, and mentions alternatives. However, it omits explicit parameter mapping and edge-case behavior. Overall, it is mostly complete for a simple utility.
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 defines only 'value' with no description, and the description does not explicitly map this parameter to the encoded string. It implies the input via the tool's purpose but lacks a direct statement like 'Pass the percent-encoded string in the value parameter.' Schema coverage is 0%, so the description should compensate more.
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 'Decode a percent-encoded URL component into plain text', specifying a verb and resource, and differentiates from sibling encoders/decoders like url_encode and base64_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?
Provides explicit 'Use when' and 'Do not use when' sections with named alternatives (url_encode, base64_decode), giving clear guidance on appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
url_encodeAInspectApply encodeURIComponent-style percent-encoding so query parameters, path segments, or form values are safe to place in a URL.
Use when:
Percent-encode this string for a query parameter
Make a path segment URL-safe with encodeURIComponent-style encoding
Encode special characters before inserting text into a URL
Do not use when:
Decode a percent-encoded string (use url_decode)
Encode binary data as Base64 (use base64_encode)
Build or rewrite a full absolute URL structure beyond component encoding
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes |
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 encoding style is encodeURIComponent-like, which implies certain characters (e.g., !'()*) remain unencoded, but this is not explicitly disclosed. It also does not mention error handling or behavior with non-string inputs, leaving some gaps for a simple utility.
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 concise, front-loaded with the core purpose, and uses a clear bullet structure for usage guidelines. Every sentence contributes value, and the 'Do not use when' section avoids redundancy while adding essential alternative guidance.
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 one-parameter transformation tool, the description covers purpose, usage, and alternatives well. It lacks explicit return-value details (though the output is the encoded string) and does not specify which special characters are left unencoded, but given the low complexity and clear examples, it remains 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?
The input schema has 0% description coverage and only one parameter 'value' (type string). The description implies the 'value' parameter is the string to encode through examples like 'query parameters, path segments, or form values', but it never explicitly names the parameter or its role, leaving the agent to infer it from context.
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 the tool applies encodeURIComponent-style percent-encoding to make strings safe for URLs. It names the exact operation, the resource (URL components), and explicitly distinguishes from siblings by listing alternatives (url_decode, base64_encode, url building).
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 explicit 'Use when' and 'Do not use when' sections, covering when to use the tool (query params, path segments, form values) and when not to use it (decoding, base64 encoding, full URL building) with specific alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
uuid_validateAInspectCheck whether a string is a well-formed UUID and, when recognizable, report its version and variant so you can accept or reject identifier inputs confidently.
Use when:
Is this string a valid UUID?
What version and variant is this UUID?
Validate a UUID before using it as a primary key or request id
Do not use when:
Generate a new UUID
Validate ISBNs or other non-UUID identifiers (use isbn_validate)
Validate JSON structure (use json_validate)
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | UUID candidate string |
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 mentions that the tool reports version and variant 'when recognizable' and implies acceptance/rejection, but does not specify the return format, accepted UUID string forms, or edge cases like nil UUIDs. This is adequate but leaves room for 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 front-loaded with the primary purpose and uses a clear bulleted structure for usage guidance. Every sentence adds value, and there is no redundant or empty phrasing.
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 one-parameter tool with no output schema, the description covers purpose, usage, and exclusions comprehensively. The only notable gap is the lack of explicit return-value structure (e.g., whether it returns a boolean or an object with version/variant), which the agent would need to discover. Overall, it is nearly 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?
The input schema provides 100% coverage with a description for the single 'value' parameter ('UUID candidate string'). The description adds minimal new meaning beyond this, only implying the input is a string and its purpose. Since schema coverage is high, the baseline 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 the tool checks whether a string is a well-formed UUID and reports its version and variant. This specific verb+resource combination distinguishes it from siblings like isbn_validate and json_validate, which are explicitly called out.
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 includes explicit 'Use when' and 'Do not use when' sections, listing concrete scenarios (e.g., validating a UUID before using it as a primary key) and naming alternative tools (isbn_validate, json_validate). This fully guides the agent on when to select this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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