audit
Server Details
AI website growth audits: SEO, performance, AI readiness (GEO), conversion, a11y, security.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
2 toolsget_auditAInspect
Fetch a Webmatik audit by id. While background analysis (AI vision, AI search visibility) is still running the response says so — poll every ~15s until status is "completed". Returns scores per category, failed checks, and the prioritized recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | No | Webmatik API key (wmk_...). Optional if sent as Authorization: Bearer header. | |
| auditId | Yes | Audit id returned by run_audit |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses asynchronous behavior (status polling) and return contents (scores, failed checks, recommendations). With no annotations, this adequately covers key behavioral traits. Does not mention authentication error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences covering action, asynchronous detail, and return values. No 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?
Completeness is high given no output schema: explains what data is returned and the polling mechanism. Could mention error responses or idempotency but not critical.
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 for both parameters. The description adds no further detail about parameter usage or formats, 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?
Clearly states 'Fetch a Webmatik audit by id', uses specific verb and resource, and distinguishes from sibling 'run_audit' which creates audits.
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 polling advice ('poll every ~15s until status is completed'), indicating when to use repeatedly. Lacks explicit 'when not to use' but context is clear given single sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_auditAInspect
Start a full Webmatik growth audit of a website: SEO, performance (Core Web Vitals), AI readiness (GEO), conversion, retention, UI/UX, accessibility, and security — 70+ checks with a 0–100 Growth Score and a prioritized action plan. The audit takes 60–180 seconds; this returns an auditId immediately — poll get_audit for the result. Requires a Webmatik API key (get one at https://webmatik.ai/account, Starter/Growth plans).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Website URL to audit, e.g. https://example.com | |
| apiKey | No | Webmatik API key (wmk_...). Optional if sent as Authorization: Bearer header. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description adequately discloses the audit's scope (70+ checks, 0–100 score), timing (60–180s), and that it returns auditId immediately. It also notes the API key requirement, but doesn't detail error handling or rate limits.
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 with two sentences that front-load the main purpose, then provide timing and next steps. It is well-structured with 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?
Given no output schema, the description clarifies the immediate return value (auditId) and directs to poll get_audit. It covers the key aspects of the async operation, though it could detail the returned object structure more.
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 both parameters have descriptions in the schema. The description adds no new information beyond the schema, meeting 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 clearly states it starts a full Webmatik growth audit covering SEO, performance, AI readiness, etc., and distinguishes from sibling tool get_audit by specifying it returns auditId immediately for polling.
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 indicates when to use this tool (to start an audit) and directs to use get_audit for results. It also mentions the prerequisite API key and where to obtain it, but doesn't explicitly exclude scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
get_audit - First observed
run_audit
Frequently Asked Questions
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/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
The two tools have clearly distinct purposes: run_audit starts a new audit, and get_audit retrieves results. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern in snake_case (run_audit, get_audit), making the naming predictable and clear.
With only 2 tools, the server is minimal but well-scoped for its purpose: starting and retrieving audits. While slightly below the typical 3-15 range, it covers the essential operations without unnecessary extras.
The tool set provides a complete workflow: run_audit initiates the audit and returns an ID, and get_audit allows polling for results. There are no obvious gaps, as the audit lifecycle is fully supported.