AI Visibility Checker
MCPB · AIVIS.MCPB · 2 COMPONENTS · SCANNED SEP 20
AI visibility checker: GPTBot vs OAI-SearchBot, robots.txt audit, raw-HTML schema. 165 crawlers.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security13
- Malware scan not yet available for this package.Unverified
- Known CVEs could not be checked: this artifact ships no SBOM, so there is no dependency list to read. Publishing one would let us assess it.Unverified
- No install/post-install scripts declared.Pass
- Dependency health could not be checked: this artifact ships no SBOM, so there is no dependency list to read. Publishing one would let us assess it.Unverified
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 22 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability77
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 426 tokens (~213/item across 2 items; 2 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management77
- Stability observed for 23 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 2 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 2 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities60
- Spec-recency check failed: implements MCP spec 2025-06-18; the latest is 2026-07-28. See how to fix → Fail
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
mcpb · aivis.mcpb
Download bundleEvery change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 73 to 77. That category is still filling its 30-day observation window: 22 days of observed history at the previous scan, 23 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 at this one. The score rises as the window fills, whether or not the server changes.
- 15 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 57 to 60. That category is still filling its 30-day observation window: 17 days of observed history at the previous scan, 18 at this one. The score rises as the window fills, whether or not the server changes.
- 13 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 50 to 53. That category is still filling its 30-day observation window: 15 days of observed history at the previous scan, 16 at this one. The score rises as the window fills, whether or not the server changes.
- 11 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.
- 9 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.
- 7 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.
- 5 Sept 26 +4
- Stability: unverified → 0.27 ▲ functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed mcpb/https://github.com/krisdiallo/ecom-agent/releases/download/v1.5.3/aivis.mcpb@1.5.3
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | mcpb |
Background: How many MCP packages publish verified provenance →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
build_recommendation_test ~206
Generate the prompt set for testing whether AI assistants actually recommend a brand in its category, plus a scoring rubric. This tool does NOT query any assistant — it cannot, and any tool claiming a definitive 'AI ranking' is ahead of the evidence, because rankings are not public and vary by wording, location and session. What it does is remove the part that does not scale: writing varied, non-leading prompts and scoring the answers consistently. If you (the calling assistant) can search the web, run these yourself and report the results back to the user. Otherwise hand them to the user to run monthly.
| Name | Type | Req | Description |
|---|---|---|---|
| brand | string | yes | Brand name, e.g. 'Allbirds'. |
| category | string | yes | What they sell, in the words a buyer would use, e.g. 'merino wool sneakers'. |
| market | string | – | Optional market/locale, e.g. 'UK'. Answers vary by location, so a result is only comparable within one. |
No output schema declared.
No examples provided.
check_ai_visibility ~220
Check whether AI assistants can find, read and transact with an ecommerce store. Three checks: (1) robots.txt, distinguishing crawlers whose blocking removes you from AI answers (OAI-SearchBot, PerplexityBot, Claude-SearchBot, Amzn-SearchBot, Applebot) from training-only crawlers where blocking costs nothing (GPTBot, ClaudeBot, Google-Extended); (2) product page RAW HTML — not the rendered DOM, because most AI crawlers do not run JavaScript — covering structured data, offer completeness and how many concrete measurements the page gives; (3) the agent-commerce layer (Universal Commerce Protocol / MCP), which decides whether an agent can actually buy rather than merely describe. Read-only: nothing is created, purchased or uploaded.
| Name | Type | Req | Description |
|---|---|---|---|
| pages | integer | – | Sample N product pages to see whether a problem is systemic rather than a one-off. |
| store | string | yes | Store domain, e.g. 'allbirds.com'. |
| url | string | – | Optional specific product page URL to check. |
No output schema declared.
No examples provided.
What is the AI Visibility Checker MCP server?
AI Visibility Checker is an MCP server listed in the public MCP registry as io.github.krisdiallo/aivis. AI visibility checker: GPTBot vs OAI-SearchBot, robots.txt audit, raw-HTML schema. 165 crawlers. This page covers its MCPB bundle (https://github.com/krisdiallo/ecom-agent/releases/download/v1.5.3/aivis.mcpb).
Is the AI Visibility Checker MCP server safe to use?
AI Visibility Checker scores 54 out of 100 on VerifyMCP. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the AI Visibility Checker MCP server expose?
AI Visibility Checker exposes 2 tools: check_ai_visibility, build_recommendation_test. Their descriptions and schemas cost roughly 426 tokens of context every time the server is loaded.
What licence is the AI Visibility Checker MCP server under?
AI Visibility Checker declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.