Utility tools for AI agents: hashing, text stats, validation, DNS, currency, GEO audits.
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Average 3.9/5 across 9 of 9 tools scored.
Each tool has a clearly distinct purpose: domain availability, currency conversion, DNS records, SEO audit, hashing, text analysis, email validation, IBAN validation, and timezone time. Even the two domain-related tools differ fundamentally: check_domain verifies availability, while dns_lookup retrieves records.
All names use snake_case, but the verb/noun order is inconsistent. Some are verb-first (check_domain, convert_currency, hash_text, validate_email, validate_iban), while others are noun-first (dns_lookup, geo_audit, text_stats, world_time). This split makes the naming pattern less predictable.
With 9 tools, the server sits comfortably in the ideal 3-15 range. Each tool provides a unique utility, so the count feels well-scoped for a general-purpose toolkit.
As a mixed utility server, it covers a broad set of common functions: domains, DNS, currency, text, validation, and time. There are minor gaps (e.g., no phone validation or URL utilities), but the existing tools are self-contained and don't create dead ends.
check_domainAInspectCheck whether a domain name is available for registration (WHOIS + DNS).
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name, e.g. example.com |
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 method (WHOIS + DNS) which is helpful. However, it does not explain the return value format, potential errors, or any network side effects, leaving some transparency gaps for a read-only check 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 a single sentence that is concise and front-loaded with the key purpose. It wastes no words and conveys both what and how.
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 tool with one parameter and no output schema, the description is mostly complete. It clearly states the purpose and method. A small gap is the lack of explicit return value description, but the tool's nature implies a yes/no or status 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?
The schema already fully describes the 'domain' parameter with an example. The description adds no additional semantic meaning beyond what the schema provides, 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 the tool checks domain availability for registration, using specific verb 'check whether' and resource 'domain name'. It distinguishes itself from sibling dns_lookup by explicitly mentioning registration, not just DNS records.
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 usage is implied: use this tool when you need to know if a domain is available for registration. However, there is no explicit guidance on when not to use it or alternatives like dns_lookup. The mention of WHOIS + DNS hints at the method, but no exclusionary guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convert_currencyAInspectConvert an amount between currencies using current ECB exchange rates.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Target currency code, e.g. USD | |
| from | Yes | Source currency code, e.g. EUR | |
| amount | Yes | Amount to convert |
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 mentions 'current ECB exchange rates' as a data source, but does not disclose potential side effects, error handling for invalid currency codes, rounding behavior, or whether the result is returned as a number. This is minimal behavioral context for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately conveys the tool's core function. It is front-loaded with 'Convert an amount' and includes the important source-of-rates detail without redundancy. Every word 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?
The tool has three parameters, all fully described in the schema, and no nested objects. The description is brief but covers the core purpose. However, since there is no output schema, the description does not mention the return format, which could be ambiguous (e.g., number vs string with currency code). It is adequate but leaves some gaps.
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 full descriptions for all three parameters (from, to, amount), covering 100% of them. The description does not add additional parameter-level details beyond the schema, which aligns with the baseline of 3. It does implicitly suggest that currency codes are expected, but the schema already states that.
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 action (convert), the resource (amount between currencies), and a distinguishing detail (using current ECB exchange rates). None of the sibling tools perform currency conversion, so this uniquely positions the tool.
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 this tool: whenever a currency conversion is needed based on current rates. No exclusions or alternatives are mentioned, but the sibling tools are unrelated, so no explicit when-not guidance is necessary. It lacks explicit alternative references but provides sufficient context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dns_lookupAInspectLook up DNS records (A, AAAA, MX, TXT, NS, CNAME, SOA) for a domain.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | Record type | A |
| domain | Yes | Domain name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The phrase 'look up' implies a read-only operation, but the description does not disclose potential error conditions, return format, or network dependency. For a simple DNS query, this may be acceptable, but it lacks explicit safety or behavioral context beyond the core action.
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 with no redundant words. It efficiently conveys the tool's purpose and lists the supported record types without any bloat.
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 is adequate. It clearly states the action and supported record types. It does not explain the return format, but that is not required when no output schema exists. The main gap is the lack of usage alternatives, but that is covered under the guidelines dimension.
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 covers both parameters with descriptions ('domain' and 'record type'), but the description adds value by listing the supported record types, which gives the agent concrete allowed values for the 'type' parameter. Schema coverage is 100%, so the description's enumeration elevates it above 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 ('look up') and identifies the resource ('DNS records') with a clear list of record types (A, AAAA, MX, TXT, NS, CNAME, SOA). It is unambiguously distinct from sibling tools like check_domain, which likely concerns domain availability rather than DNS records.
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 guidance is provided on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or when to prefer check_domain or other sibling tools. The description only states what it does, leaving the agent without explicit decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
geo_auditAInspectRun a CiteReady GEO audit: how well can AI search engines (ChatGPT, Perplexity, Google AI Overviews) crawl, understand and cite a website. Returns score, grade and actionable findings.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Page URL to audit | |
| pages | No | Pages to audit via sitemap (max 3 via MCP) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden of disclosing behavior. It states the tool 'runs an audit' and returns outputs, implying a read-only operation, but it does not explicitly confirm that it makes no changes to the website, or mention permissions, rate limits, or potential side-effects of crawling. It adds reasonable context but could be more transparent about its access patterns.
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 tool's purpose and includes examples of target engines and the return type. Every word earns its place with no redundancy 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?
Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description is largely complete: it explains what the tool does, what it returns, and the context. Minor omissions like explaining the 'grade' scale or the exact meaning of 'actionable findings' do not significantly impair understanding for an agent.
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 descriptions for both parameters (url: 'Page URL to audit', pages: 'Pages to audit via sitemap (max 3 via MCP)'). The description adds no additional meaning beyond the schema, 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 uses a specific verb ('Run') and names a particular resource ('CiteReady GEO audit', 'website'). It clearly explains what the tool does: assesses how well AI search engines crawl, understand, and cite a website, and returns a score, grade, and actionable findings. This distinguishes it from the unrelated sibling tools like dns_lookup or convert_currency.
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 to use the tool: when you need to evaluate a website's visibility to AI search engines. It does not explicitly contrast with alternatives or state when not to use it, but the context is clear enough given the sibling list. No exclusions or edge-case guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hash_textAInspectCompute a cryptographic hash (md5, sha1, sha256, sha512) of a text.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to hash | |
| algorithm | No | md5 | sha1 | sha256 | sha512 | sha256 |
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 action but does not explicitly disclose that hashing is a pure, side-effect-free operation or describe the output format. For a simple tool, this is adequate but not rich.
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, concise sentence that clearly conveys the tool's purpose and options without any unnecessary words.
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, and the schema covers parameters. However, without an output schema, the description does not specify the return format (e.g., hex string). This is a minor gap for an otherwise clear and complete description.
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 parameters described. The description reiterates the algorithm options already present in the schema, adding no new parameter semantics 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 computes a cryptographic hash, listing specific algorithms. This distinguishes it from sibling tools, which are unrelated to hashing.
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 for when to use the tool (whenever a cryptographic hash of text is needed). No exclusions or alternatives are mentioned, but none are necessary given the sibling tools are unrelated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
text_statsAInspectAnalyze text: word/sentence counts, reading time and Flesch readability scores.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the computations performed, but does not mention any side effects, safety profile, or limitations. For a simple analysis tool, the read-only nature is obvious, so transparency is adequate but not deeply detailed.
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 sentence that is front-loaded with the verb and resource, and every word adds value. 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?
No output schema exists, so the description appropriately lists the returned metrics. It is complete enough for the tool's simplicity, though it does not mention edge cases or output format. This is acceptable given the low complexity.
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%, but the schema only says 'Text to analyze'. The description adds meaning by specifying what 'analyze' entails—word/sentence counts, reading time, and Flesch scores—thus giving the agent a richer understanding of the parameter's purpose.
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 specific verb 'Analyze' and the resource 'text', and enumerates the exact outputs (word/sentence counts, reading time, Flesch readability scores). This fully distinguishes it from sibling tools like convert_currency or validate_email, which have different purposes.
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 text analysis metrics are needed. It provides clear context without explicit alternatives or exclusions. Since the tool is so distinct from its siblings, the lack of explicit 'do not use for X' is not a major gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_emailAInspectValidate an email address: RFC syntax, MX records, disposable/role-based detection.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to validate |
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 mentions RFC syntax, MX records (implying network operations), and disposable/role-based detection, offering some transparency. However, it does not mention whether the operation is read-only, what the return value format is, or potential error/rate-limit behavior, leaving meaningful gaps.
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, tightly written sentence that front-loads the primary purpose ('Validate an email address') and then lists specific checks. Every word contributes value, with no redundancy 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?
Given the lack of an output schema and annotations, the description should explain what the tool returns and any side effects. It does not indicate whether the result is a boolean, a structured report, or how errors are handled. This is a significant omission for an agent to confidently interpret the tool's response.
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% for the single 'email' parameter, so the baseline score is 3. The description adds context about the types of validation performed but does not provide additional parameter-specific semantics beyond what the schema already states.
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 validates an email address and enumerates specific validation checks (RFC syntax, MX records, disposable/role-based detection). This specific verb+resource clearly distinguishes it from sibling tools like validate_iban or check_domain.
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 (email validation) and implies its specific capabilities (syntax and deliverability checks). However, it does not explicitly state when not to use it or contrast with alternatives (e.g., check_domain or dns_lookup) for related tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_ibanAInspectValidate an IBAN (ISO 13616 MOD-97) and return bank/country details.
| Name | Required | Description | Default |
|---|---|---|---|
| iban | Yes | IBAN to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. 'Validate' and 'return' imply a read-only operation with output, but the description does not disclose edge cases (e.g., invalid IBAN responses) or whether the operation has side effects. This is adequate for a simple validation utility but not comprehensive.
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?
A single, well-structured sentence that front-loads the tool's core function and includes the standard. Every word is informative and there is no redundancy.
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 with one parameter and no output schema. The description mentions return of bank/country details but does not specify the result format for valid or invalid IBANs. Given the absence of annotations, more detail about expected output would improve completeness, but the current level is minimally viable.
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 'iban' parameter described as 'IBAN to validate'. The description adds the ISO standard reference, but this is not substantial additional meaning beyond the schema. Baseline 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 validates an IBAN using the ISO 13616 MOD-97 standard and returns bank/country details. This specific verb+resource combination distinguishes it from siblings like validate_email and check_domain.
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 use when IBAN validation is needed, and the context is clear. However, it does not explicitly mention exclusions or alternatives, so it stops short of full when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
world_timeAInspectGet the current time and UTC offset for an IANA timezone.
| Name | Required | Description | Default |
|---|---|---|---|
| timezone | No | IANA timezone, e.g. Europe/Berlin | UTC |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It accurately states the operation and output (current time and UTC offset) but does not disclose edge cases like invalid timezone handling, network dependencies, or the optional nature of the timezone parameter (though the schema covers the default).
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 immediately conveys the core function. There is no wasted wording or redundant information, earning a perfect score for conciseness.
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 with one optional parameter and no output schema. The description adequately explains what the tool returns (current time and UTC offset). While it doesn't mention the default UTC timezone or potential errors, the schema compensates for the default, and the description is complete enough for the tool's 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 input schema has 100% coverage for the only parameter 'timezone', including its default value and description. The tool description adds no additional parameter information beyond what the schema already provides, aligning with the baseline score for high 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 clearly states the tool's function with a specific verb('Get') and resource('current time and UTC offset for an IANA timezone'). This distinguishes it from all sibling tools, none of which involve time-related functionality.
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 on when to use the tool (to retrieve time information for a timezone), and while it doesn't explicitly name alternatives or exclusions, the uniqueness of the purpose among siblings makes the usage straightforward.
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.
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