A
license-
qualityB
maintenanceRuns AI visibility (GEO/AEO) audits on websites, checking AI crawler access, schema markup, llms.txt, and content signals, with optional full PDF report.
MIT
audit
by ai.webmatik
Server Details
AI website growth audits: SEO, performance, AI readiness (GEO), conversion, a11y, security.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.2/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools have clearly distinct purposes: run_audit starts a new audit, and get_audit retrieves results. There is no overlap or ambiguity.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern in snake_case (run_audit, get_audit), making the naming predictable and clear.
Tool Count4/5
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.
Completeness5/5
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.
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.
ParametersJSON Schema
| 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
A4.2/5.0
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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).
ParametersJSON Schema
| 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
A4.1/5.0
Behavior4/5
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.
Conciseness4/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
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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
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