audit
Server Details
AI website growth audits: SEO, performance, AI readiness (GEO), conversion, a11y, security.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
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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
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one starts an audit, the other fetches results. No overlap or ambiguity.
Both tool names follow a consistent verb_noun pattern ('run_audit', 'get_audit'), making them predictable and easy to understand.
With only 2 tools, the set feels minimal but acceptable for a focused audit service. However, additional tools for listing or managing audits could be expected.
The core workflow (start and poll) is covered, but missing operations like listing past audits or canceling an audit leave notable gaps for a full lifecycle.
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description compensates by disclosing the polling behavior and status-based completion. It could be more explicit about idempotency or side effects, but the given details are sufficient for safe invocation.
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 purpose, followed by crucial behavioral detail. Every sentence adds value 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 adequately explains return values and the polling mechanism. The sibling context reinforces that this is used after run_audit. No gaps for a fetch 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%, and the description adds context: apiKey can be optional via header, auditId comes from run_audit. This goes beyond the schema's basic 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 an audit by ID, mentions the asynchronous polling behavior, and specifies the return content. It effectively distinguishes from the sibling run_audit 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 provides explicit guidance on polling every ~15s until status 'completed'. This indicates when and how to use the tool, though it does not explicitly state when not to use it or mention alternatives.
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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It discloses that the audit is asynchronous (returns auditId), takes 60–180 seconds, and requires an API key. It does not mention rate limits, errors, or side effects, but the async pattern is well communicated.
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 filler. It front-loads the purpose and includes all essential details in a compact, well-structured format.
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 complexity (70+ checks) and lack of output schema, the description covers the key aspects: async result, coverage areas, scoring, time estimate, and API key requirement. It could be enhanced with error handling or output format details, but is 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?
Schema coverage is 100%, so the description adds limited value beyond the schema. It mentions the apiKey can be sent as a header, which is additional context, but otherwise restates the parameter purposes.
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 starts a full Webmatik growth audit covering SEO, performance, AI readiness, and more. It distinguishes from the sibling tool 'get_audit' by noting that this returns an 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 explains when to use the tool (to run an audit) and what to do next (poll get_audit). It provides context on audit duration and prerequisites (API key), but does not explicitly state when not to use or alternative tools beyond get_audit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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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
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