mcp server
DABYTE AI Visibility Index
Measured share of answer for 20 SaaS brands. An open dataset, not an audit of your site.
Description as published by the maintainer. Source
- version 1.0.1
- active
active — Most recent push to the repository was 2026-08-06.
What this server can do
5 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
get_brand_visibility(slug)- One brand's standing in the current DABYTE release: share of answer per engine, rank, quadrant, how many panel prompts name it, and which ones. Use this when a specific brand is named. Takes a slug, not a display name — call list_tracked_brands first if you are unsure, or read the slug from get_visibility_index. An unknown slug is not a failure to hide: the error names every valid slug, so a second attempt can succeed. A brand absent from the index has not been measured at all, which is different from a measured zero. Only SaaS & AI tools brands are tracked. For the field as a whole use get_visibility_index; for this brand over time, get_history. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number. Required: slug.
get_history- Every DABYTE release ever published, as a series per brand: share of answer at each weekly measurement with the date and panel version it was taken under. Use this for any question about change — is a brand rising, when did it enter the index, how volatile is the category. Two limits decide whether an answer is honest. Figures are comparable only WITHIN a panel version: the panel is frozen between releases and a version change alters the denominator, so a difference across that boundary is not a trend. And small moves sit inside language-model noise: since panel v3 (2026-08-10) each prompt runs three times per engine per release and the figure is the share of runs; earlier releases ran each prompt once, so one mention on one engine was a whole scale step there. Either way a one-step movement should not be reported as a gain or a loss. Call get_methodology for the exact step size. For the current release alone use get_visibility_index. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.
get_methodology- The rules behind every figure this server returns: the exact prompt panel and its version, which engines were measured, how share of answer is scored and rounded, the resolution of the scale in percentage points, and the editorial firewall and ownership disclosure. Call this before quoting a number as evidence, before comparing two releases, or whenever a user asks how the measurement was made or who publishes it. It is the only tool that tells you how much of a difference is meaningful, which is what stops a one-step wobble being reported as a movement. It returns rules, not figures — no brand appears in the response. For figures use get_visibility_index or get_brand_visibility; for the series, get_history. The panel is public and frozen between releases, so every published number can be recomputed by a third party from the archive at https://dabyte.ai/archive/. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.
get_visibility_index- The whole current release in one call: every tracked brand in SaaS & AI tools with its rank, share of answer overall and per engine, commercial intent and quadrant. Share of answer is the percentage of a fixed panel of category buyer prompts in which an engine names the brand. Use this when the question is about the field — who leads, who is absent, how the category looks. It is one response of roughly 8 KB for 20 brands, so prefer it over calling get_brand_visibility repeatedly. Do NOT use it for one named brand (get_brand_visibility is the direct answer), for movement over time (get_history holds the series; a single release cannot show a trend), or to audit a website's own AI visibility — this is a measured dataset about third-party brands, not a site audit. Covers SaaS & AI tools only; the sibling index at dablock.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.
list_tracked_brands- The names and slugs of every brand in the DABYTE index — a lookup table, nothing else. No scores, no ranks. Use it for two things: to turn a brand name into the slug get_brand_visibility needs, and to answer whether a brand is tracked at all. Do NOT use it when you want figures — get_visibility_index returns the same brands with their full measurements in a single call, so calling this one first is a wasted round trip. Absence here means the brand is not measured, not that it scores zero. Covers SaaS & AI tools only; the sibling index at dablock.ai covers the other niche. Re-measured weekly, so the same call returns the same figures until the next release. Data is CC BY 4.0 and free: no key, no account, no rate limit — cite the release date and dabyte.ai when quoting a number.
Last successful function declaration observed on . Source: https://dabyte.ai/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://dabyte.ai/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-08-06 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.0.1 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-08-05 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-08-05 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 5 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | dabyte.ai | |
| mcp endpoint status | ok | The server listed 5 functions when asked. | as of probe | dabyte.ai |
Where to get it
Related, by what their authors tagged them
-
DABLOCK AI Visibility Index
— last commit 2026-08-06, shares ai-visibility, benchmark, chatgpt
Measured share of answer for 24 crypto and Web3 brands. An open dataset, not an audit of your site.
-
refd
— last commit 2026-07-30, shares ai-visibility, chatgpt, gemini
Read AI search visibility, competitors, citations, prompts, and answer evidence from refd.
-
DigestSEO AI Visibility
— last commit 2026-07-27, shares ai-visibility, chatgpt, gemini
Track how ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews cite your brand.
-
DigestSEO mcp-geo — AI Visibility MCP Server
— last commit 2026-07-27, shares ai-visibility, chatgpt, gemini
DigestSEO mcp-geo: track brand citations across five AI search engines.
-
CrazySEO
— last commit 2026-08-05, shares ai-visibility, chatgpt, gemini
Audit whether ChatGPT, Gemini and Perplexity recommend a site. Readiness checks need no keys.
-
CrazySEO
— last commit 2026-08-05, shares ai-visibility, chatgpt, gemini
Audit whether ChatGPT, Gemini and Perplexity recommend a site. Readiness checks need no keys.
-
OpenChainBench
— last commit 2026-08-08, shares benchmark, crypto
Live, neutral benchmarks for public RPC latency, oracles, bridges, perp DEX, and prediction markets.
-
io.github.builtbyabs/shopify-geo-audit
— last commit 2026-07-21, shares ai-visibility, chatgpt, perplexity
Audit any Shopify store for AI search (GEO/AEO) readiness and generate the fixes. No signup.
-
dev.llmlatency/llm-latency-tracker
— last commit 2026-07-23, shares benchmark
Measured latency & uptime for AI inference APIs, by region. Exposes a get_ai_api_latency tool.
-
dev.safeprompt/mcp
— last commit 2026-08-03, shares benchmark
Detect prompt injection, jailbreaks, and code injection in untrusted text before it reaches an LLM.
These share tags the maintainers applied themselves, such as ai-visibility, benchmark, chatgpt, crypto. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-visibility, benchmark, chatgpt, crypto, gemini, llm, open-data, perplexity, saas, share-of-answer.
This record as data
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