Free technical-SEO audit MCP: crawl a site, run checks, return an LLM-ready shareable report.
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 has a clearly distinct purpose: submit scan (scan_url), retrieve report in JSON or Markdown (get_report, get_report_markdown), drill into an issue (get_issue_detail), and compare scans (diff_scan). No overlapping functionality.
All tool names follow a consistent verb_noun pattern (e.g., scan_url, get_report, diff_scan). Format variants like get_report_markdown are logical extensions, not inconsistencies.
Five tools perfectly cover the SEO auditing workflow: initiating a scan, retrieving results in two formats, expanding issue details, and comparing scans. The count is neither excessive nor minimal.
The tool set covers the essential lifecycle: scan submission, result retrieval in multiple formats, issue detail expansion, and diffing. Missing list_scans or cancel_scan, but these are minor gaps; agents can work with existing tools.
diff_scanAInspectDiff a scan against the previous scan of the same domain: health movement plus the issue codes that were fixed or newly appeared/regressed. Use this when auditing in a loop to verify fixes landed and nothing regressed.
| Name | Required | Description | Default |
|---|---|---|---|
| scan_id | Yes | The current scan id; the previous scan of the same domain is found automatically. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must bear the transparency burden. It states the previous scan is found automatically and lists outputs, but does not disclose whether it is read-only, permissions needed, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the action and output. Every sentence adds value; no wasted 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?
With one required param and no output schema, the description adequately explains what the tool returns (health movement, issue changes). Could mention additional details like format or error cases, but sufficient for a simple diff 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 coverage is 100% (only scan_id described). Description adds meaning: 'the previous scan of the same domain is found automatically,' explaining parameter behavior 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 'Diff a scan against the previous scan' with specific outputs: health movement and issue codes fixed/regressed. It distinguishes from siblings like get_issue_detail, get_report, etc., which focus on single scans or reports.
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 usage context: 'Use this when auditing in a loop to verify fixes landed and nothing regressed.' Does not explicitly mention when not to use, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_issue_detailAInspectDrill into one issue code: every affected URL plus step-by-step remediation (what to change, why, and when it's a false alarm). Call this after get_report_markdown to expand a code from the collapsed table before applying fixes.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The issue code to expand, e.g. "links.broken_internal" or "title.too_long". | |
| scan_id | Yes | The scan id returned by scan_url. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears burden. It discloses that it returns URLs and remediation details, but doesn't mention authentication, rate limits, or if it's read-only. Lacks full behavioral disclosure.
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: first explains function, second gives usage guidance. No extraneous text. Highly efficient.
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?
Describes return content (URLs and remediation) but lacks structure details. No output schema, so more specific format description would improve completeness.
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 descriptions. The description adds example patterns for 'code' and clarifies that 'scan_id' comes from scan_url, adding value beyond 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?
Clearly states it drills into one issue code, showing affected URLs and remediation steps. Differentiates from siblings by specifying it follows get_report_markdown.
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 says to call after get_report_markdown to expand a code before applying fixes. Provides clear context but no explicit when-not or alternatives for other scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_reportAInspectPREFERRED for reasoning about what to fix. Fetch a finished report as structured JSON (health score, category subscores, all issues with URLs, stats). Use this when you need to decide which problems matter.
| Name | Required | Description | Default |
|---|---|---|---|
| scan_id | Yes | The scan id returned by scan_url. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral burden. It clearly states the tool fetches a finished report as structured JSON, is read-only, and requires a scan_id. It does not mention error conditions or prerequisites beyond a 'finished report,' but for a read-only 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 consists of two sentences with no extraneous words. The key purpose statement is front-loaded, followed by concise output details. 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?
Given no output schema, the description adequately explains the output structure (health score, subscores, issues, stats). It also specifies the input (scan_id) and relative priority over siblings. However, it does not mention potential errors or edge cases, but the tool is simple enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents the single parameter scan_id. The description does not add any additional meaning beyond what is in the schema. Baseline is 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 clearly states the verb 'Fetch', the resource 'finished report', and the output format 'structured JSON' with specific fields (health score, category subscores, all issues with URLs, stats). It differentiates from siblings like get_report_markdown by emphasizing it's 'PREFERRED for reasoning about what to fix.'
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 guidance on when to use: 'Use this when you need to decide which problems matter' and 'PREFERRED for reasoning about what to fix.' It implies alternatives through sibling tool names but does not explicitly state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_report_markdownAInspectFetch a finished report as compact, agent-ready Markdown: health, a prioritized fix plan with remediation, and issues collapsed to one row per code (code | severity | count | example URLs) — small enough to keep in context on a real site. Set full=true for the complete human report (large). Use get_issue_detail to expand any single code.
| Name | Required | Description | Default |
|---|---|---|---|
| full | No | When true, return the full human report (all occurrences, checks coverage). Default false = compact agent view. | |
| domain | No | Optional domain label for the report heading. | |
| scan_id | Yes | The scan id returned by scan_url. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses output format details and size considerations (compact vs large). It mentions the compact version is 'small enough to keep in context,' which is helpful. It does not cover error handling or prerequisites, but given the read-only nature, the transparency is good.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no wasted words. It front-loads the main purpose, then quickly covers parameter behavior and sibling guidance. 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 fetch tool with explicit schema, the description covers what is returned, parameter effects, and links to siblings. It mentions size considerations. It does not cover what happens if the report isn't ready, but this is minor for a well-defined 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 coverage is 100% but the description adds value by explaining the 'full' parameter's effect on output size and how 'domain' is used for the heading. It also clarifies that 'scan_id' is returned by scan_url, enhancing the schema's brief descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a finished report as compact, agent-ready Markdown with specific content (health, prioritized fix plan, issues collapsed). It distinguishes from sibling tools like get_issue_detail and get_report, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use this tool (compact agent view vs full human report) and directs to get_issue_detail for expanding any single code. It also explains the effect of the 'full' parameter, providing clear guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_urlAInspectSubmit a URL to Crawlinx for an SEO audit. Crawls up to max_pages pages (default 50, max 200), runs the technical-SEO checks, and returns a scan id; poll get_report / get_report_markdown until the crawl finishes.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute http(s) URL to audit. | |
| mode | No | Scan mode: "free" (default, multi-page) or "instant" (single-page quick check). | |
| max_pages | No | How many pages to crawl. Default 50; maximum 200. Ignored for mode=instant. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the async nature (returns scan id, requires polling) and the page limit, but omits potential side effects, authentication needs, or rate limits. The behavioral disclosure 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?
Two front-loaded sentences: first gives the core action, second provides technical details (page limits, mode, polling). No unnecessary words; 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?
The description covers the main flow (submit, get scan id, poll for report), but lacks error handling, timeouts, or mention of the scan id format. Siblings are referenced properly (poll get_report/get_report_markdown). For a 3-param tool with no output schema, 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?
Despite 100% schema coverage, the description adds meaningful context: explains max_pages defaults and max, clarifies mode='instant' as a single-page quick check, and notes that max_pages is ignored for instant mode. This goes beyond the schema's dry parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it submits a URL for an SEO audit, specifies crawling behavior and result retrieval via polling, and distinguishes from siblings like get_report and get_report_markdown by establishing this as the initiation step.
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 this is the tool to start a scan, but does not explicitly state when to use it vs siblings (e.g., diff_scan or get_report). No when-not-to-use guidance is provided, though the polling mention hints at the asynchronous workflow.
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.
👋 Maintainer here. Crawlinx is a free, no-signup technical-SEO audit crawler exposed over MCP — point it at any URL and get a health score, a prioritized list of fixes, and a shareable report link. What's different: it runs a real browser engine, so it renders JavaScript and gets past most bot-walls. That means it sees a page the way Googlebot — and AI crawlers like GPTBot/ClaudeBot, which mostly skip JS — actually see it, not the raw HTML most crawlers get. 156 checks, including 2026-era ones others skip: structured-data deprecations, AI-bot accessibility, and agent-readiness. Quick start (Claude Code): claude mcp add --transport http crawlinx https://crawlinx.com/mcp Typical flow: scan_url (start an audit, returns a scan id) → get_report_markdown / get_report → get_issue_detail (expand one issue) → diff_scan (after fixing, confirm it landed and nothing regressed). No API key, no signup, nothing to install. Feedback and check requests very welcome!