MCP server for SEO and web analysis data including keyword rankings, backlink profiles, site audits, and traffic analytics for AI agents.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.2/5 across 5 of 5 tools scored.
Each tool targets a distinct aspect of web analysis: DNS records, SSL certificates, full website crawling, technology stack detection, and WHOIS data. There is no ambiguity in their purposes.
All tool names follow a consistent verb_noun pattern: check_dns, check_ssl, crawl_website, detect_tech_stack, lookup_whois. The pattern is uniform and predictable.
5 tools is appropriate for a focused SEO and web analysis server. It covers the core areas without being excessive or too thin, though a couple more could be added.
Covers DNS, SSL, crawling, tech stack, and WHOIS, but misses common SEO tools like HTTP header inspection, robots.txt/sitemap analysis, or page speed evaluation. Notable gaps exist.
check_dnsAPerform DNS lookup to retrieve DNS record details for a domain. Returns A records (IP addresses), MX records (mail servers), CNAME records, NS records (nameservers), and TXT records. Use for email setup verification, DNS troubleshooting, or server infrastructure research.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name to look up (e.g. 'google.com', 'example.org', 'subdomain.example.com') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so no need to repeat. Description adds value by listing the types of records returned, providing context beyond annotations.
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?
Three sentences: operation, output, and use cases. No wasted words, front-loaded with the verb and resource.
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 simple schema with one parameter and good annotations, the description covers the purpose and output adequately. No output schema is expected, so the lack of return details is acceptable.
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%; the description adds no additional details about the 'domain' parameter beyond what the schema provides. 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 it performs DNS lookup and retrieves specific record types (A, MX, CNAME, NS, TXT). It distinguishes from sibling tools like check_ssl or lookup_whois, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists three use cases: email setup verification, DNS troubleshooting, server infrastructure research. No when-not-to-use or alternatives mentioned, but the guidance is direct and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_sslAInspect SSL/TLS certificate details for a domain. Returns certificate issuer, expiration date, subject alternative names (SANs), key strength, and certificate chain validation status. Use for security audits, certificate renewal tracking, or compliance verification.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain to check SSL certificate (e.g. 'example.com', 'api.example.com') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only. Description adds specific output details (issuer, expiration, SANs, key strength, chain validation), increasing transparency beyond the annotation's general read-only hint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words, front-loaded with the action. Perfectly concise for the tool's simplicity.
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?
Fully covers purpose, usage, and return details for a single-parameter tool with good annotations. No gaps given the tool's scope.
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 parameter 'domain' already well-described. Description adds no additional meaning beyond the schema's value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Inspect SSL/TLS certificate details for a domain' and lists specific return fields. It distinctly separates from sibling tools like check_dns (DNS records) and lookup_whois (domain registration).
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?
States use cases: 'security audits, certificate renewal tracking, or compliance verification.' Provides clear context but does not explicitly exclude scenarios or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
crawl_websiteACrawl a website and extract structured content from all accessible pages. Returns page titles, meta descriptions, headings, body text, internal/external links, and page structure. Use for SEO audits, content inventory, site mapping, or data extraction for analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Website URL to crawl (e.g. 'https://www.example.com', 'example.com') | |
| max_pages | No | Maximum number of pages to crawl (default 10, higher for full site scans) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe read with external effects. The description does not elaborate on crawling behavior (e.g., rate limits, robots.txt respect) beyond stating extraction, leaving gaps beyond annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. Core action and output are front-loaded; use cases are efficiently listed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately covers outputs and use cases for a simple crawler. Missing details on error handling or pagination are acceptable given no output schema and moderate complexity. Rules state output schema not needed, but context is sufficient.
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 description does not add meaningful detail beyond the schema. It implies URL and max_pages but no additional semantics, meeting 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 clearly states the tool crawls a website and extracts structured content, listing specific outputs. It distinguishes from sibling tools (DNS, SSL, tech stack, WHOIS) which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lists use cases like SEO audits and site mapping, providing clear context for when to use. However, it does not explicitly state when not to use or mention alternatives, though siblings are distinct enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detect_tech_stackAIdentify the technology stack and services used by a website. Returns framework names, CMS platform, JavaScript libraries, analytics services, CDN provider, hosting provider, and security tools detected. Use for competitive analysis, vendor intelligence, or understanding site architecture.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Website URL to analyze (e.g. 'https://www.example.com') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true (safe read operation) and openWorldHint=true (may discover unexpected information). The description aligns by listing what it returns and does not indicate any destructive behavior. It adds value by listing specific detected categories, though it omits potential limitations like false negatives.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first explains purpose and output, the second gives usage guidance. No unnecessary words. Well-structured and 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?
For a simple tool with one parameter and good annotations, the description covers purpose, output categories, and use cases. It is complete enough 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 has 100% description coverage for the single parameter 'url', including an example format. The description adds no new information beyond what the schema provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb ('Identify') and resource ('technology stack and services used by a website'), and lists specific outputs (framework names, CMS, etc.). It distinguishes itself from siblings like check_dns or crawl_website by focusing on tech stack detection.
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 usage contexts ('competitive analysis, vendor intelligence, or understanding site architecture') but does not mention when not to use or compare directly with siblings. The context is clear and helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_whoisAQuery WHOIS database for domain registration details. Returns registrant name, registrar, registration and expiration dates, registrant contact info, and nameserver list. Use for domain research, owner identification, or tracking registration status.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain name to look up in WHOIS (e.g. 'example.com', 'company.org') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and openWorldHint. The description adds details about returned fields but does not disclose potential limitations such as private registrations or rate limits. Reasonable but not exceptional.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the key action and result, followed by usage context. No redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers purpose, return fields, and use cases. Could mention limitations or error cases, but sufficient for most agents.
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?
With 100% schema coverage, the schema already describes the domain parameter adequately. The tool description does not add new semantic information beyond the schema's example.
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 queries WHOIS for domain registration details and lists the returned information, distinguishing it from sibling tools like check_dns or check_ssl which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides use cases like domain research and owner identification, implying when to use it. However, it does not explicitly exclude it from other scenarios or compare to siblings.
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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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
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