Siteline Documentation - AI Search Analytics

The MCP server is currently in beta and available on paid plans only. To activate it for your team, contact us at [email protected].

The Model Context Protocol (MCP) is a standard for connecting Large Language Models (LLMs) to platforms like Siteline. Once connected, your AI assistants can query Siteline prompt and agent analytics on your behalf.

MCP installation

Follow our security best practices

Before connecting the MCP server, read the security risks section to understand what to review before giving an AI tool access to Siteline analytics.

Configure your AI tool

Choose your Siteline team and MCP client, then follow the installation instructions below.

Installation

Codex
Claude Code
JSON

Add the MCP server with:

codex mcp add siteline --url https://mcp.siteline.ai/mcp

Add the MCP server with:

claude mcp add --transport http siteline https://mcp.siteline.ai/mcp

Then authenticate from Claude Code:

/mcp

Alternatively, add this configuration to .mcp.json:

{
  "mcpServers": {
    "siteline": {
      "type": "http",
      "url": "https://mcp.siteline.ai/mcp"
    }
  }
}

After configuring the MCP server, authenticate using your MCP client’s authentication flow.

Authentication

Some MCP clients automatically prompt you to log in during setup, while others require manual authentication steps. Authentication opens a browser window where you log in to Siteline and grant read-only analytics access for the selected team.

Siteline MCP does not require you to generate or paste a public API key into the MCP client.

Next steps

After you log in, check that the MCP server is connected. Depending on the client, you may need to restart it to connect and detect all tools after authorization. To verify the client has access to the MCP server tools, ask it to query Siteline using natural language. For example: “Summarize prompt performance for my product using Siteline MCP.”

How the MCP server works

Siteline MCP is a hosted, read-only MCP server available at:

https://mcp.siteline.ai/mcp

MCP clients connect over Streamable HTTP at POST /mcp and authenticate with browser-based OAuth 2.0 Authorization Code with PKCE. During authorization, Siteline asks you to choose the team whose analytics the client can read. The MCP access token is then resolved on the server side to that team’s Siteline public API access; the token and public API key are never passed as tool arguments. After authorization, the server exposes tools that proxy Siteline’s public product analytics API. Product-scoped tools can discover the available product and website for the connection. When a request is underspecified, the tool returns structuredContent.selectionRequired with choices the client should show to you before retrying the same tool. The server asks for a selection when:

More than one product is available and productId was omitted
More than one website is available and domain was omitted
dateFrom or dateTo is omitted
A prompt-performance tool needs one or more platforms

Missing date ranges return the same presets used in the app: 7D, 30D, 3M, 6M, 12M, plus Custom for explicit RFC3339 dateFrom and dateTo values.

Available tools

The Siteline MCP server exposes seven read-only tools for product discovery, prompt analytics, citations, and agent analytics traffic.

Tool	Use it to
`list_products`	List authorized primary products, product IDs, and domains for the MCP connection.
`list_product_websites`	List active website domains and website IDs for a product.
`list_product_tracked_prompts`	List tracked prompt rows with platform, domain, position, visibility, rank, volume, and labels.
`summarize_product_prompt_performance`	Summarize aggregate prompt visibility, position, rank, and competitor count.
`analyze_product_prompt_trends`	Analyze daily prompt visibility, position, and rank trends.
`list_product_citations`	List citation source pages, source domains, or mentioned products.
`analyze_product_analytics_timeseries`	Analyze AI crawler and referred human traffic timeseries.

Product discovery

Use list_products and list_product_websites first when you do not know which productId or domain the MCP connection can access.

Prompt performance

Use list_product_tracked_prompts, summarize_product_prompt_performance, and analyze_product_prompt_trends to inspect visibility, rank, position, and competitors.

Citations

Use list_product_citations to inspect top cited pages, citation source domains, or mentioned products.

Agent analytics

Use analyze_product_analytics_timeseries to inspect AI crawler traffic and referred human traffic over time.

`list_products`

Use this discovery tool when you do not know which Siteline products the authorized MCP connection can read. Prompt input example: “Which Siteline products can this MCP connection access?” Tool input: none.

{}

Expected return: structuredContent.products, an array of products. Each product includes:

id: Product ID to pass as productId to product-scoped tools
name: Product name
website: Product website URL
domain: Primary domain
partnerWebsites: Website domains and website IDs linked to the product

`list_product_websites`

Use this discovery tool when you know a productId and need the active website domains attached to that product. Prompt input example: “Which websites belong to product 6330?” Tool input:

productId

number

required

Siteline product ID returned by list_products.

Expected return: structuredContent.websites, an array of product websites. Each website includes:

domain: Bare hostname, such as example.com
websiteId: Website ID used internally for product analytics

`list_product_tracked_prompts`

Use this prompt-performance tool to inspect individual tracked prompt rows for a product, website, date range, and platform set. Prompt input example: “List tracked prompts for product 6330 on example.com for the last 30 days on ChatGPT and Gemini.” Tool input:

productId

number

Siteline product ID. Omit it only when the MCP server should auto-select the only available product or ask you to choose one.

domain

string

Bare hostname, such as example.com. Omit it only when the MCP server should auto-select the only available website or ask you to choose one.

dateFrom

string

Inclusive RFC3339 timestamp. If omitted, the tool asks you to choose a preset or provide a custom range.

dateTo

string

Inclusive RFC3339 timestamp. Must be after dateFrom; date windows can be up to 366 days.

platforms

string[]

One or more of chatgpt, perplexity, google, google_ai_overviews, google_ai_mode, gemini, chatgpt_impl1, or claude. If omitted, the tool asks you to choose one or more platforms.

labelIds

number[]

Optional prompt label IDs to filter results.

Expected return: structuredContent.prompts, an array of tracked prompt rows. Each row includes:

promptText: The tracked prompt text
platform: Platform that produced the row
domain: Product domain used for the metric
position: Average position of the product within AI response text for this prompt. For example, a position of 1 means it was mentioned first. null when no data is available.
visibility: Percentage of AI responses in which the product appears for this prompt, expressed as a decimal (e.g. 0.42 = 42%). null when no data is available.
rank: Product’s ranking by visibility compared to all products appearing in this prompt’s results. A rank of 1 means highest visibility. null when no data is available.
volume: Prompt volume, or null when unavailable
labels: Applied prompt labels with id and name

`summarize_product_prompt_performance`

Use this prompt-performance tool when you want one aggregate summary across a product, website, date range, and platform set. Prompt input example: “Summarize prompt performance for product 6330 on example.com over the last 30 days.” Tool input: same as list_product_tracked_prompts: productId, domain, dateFrom, dateTo, optional platforms, and optional labelIds. Expected return: structuredContent.summary, a single prompt summary object. The summary includes:

position: Average position of the product within AI response text across all matching prompts. null when no data is available.
visibility: Aggregate percentage of AI responses in which the product appears across all matching prompts, expressed as a decimal. null when no data is available.
rank: Product’s ranking by visibility compared to all competing products across matching prompts. null when no data is available.
totalCompetitors: Total number of distinct competitor products appearing across matching prompt results.

`analyze_product_prompt_trends`

Use this prompt-performance tool when you want daily trend rows for visibility, position, and rank. Prompt input example: “Show daily Gemini prompt visibility trends for product 6330 on example.com in April 2026.” Tool input: same as list_product_tracked_prompts: productId, domain, dateFrom, dateTo, optional platforms, and optional labelIds. Expected return: structuredContent.trends, an array of daily prompt trend rows. Each row includes:

date: Trend date
platform: Platform for the row
domain: Product domain used for the metric
visibility: Percentage of AI responses in which the product appears on this day, expressed as a decimal. null when no data is available.
position: Average position of the product within AI response text on this day. null when no data is available.
rank: Product’s ranking by visibility compared to all competing products on this day. null when no data is available.

`list_product_citations`

Use this citation tool to inspect where a product is cited, which source domains cite it, or which products are mentioned in answers. Prompt input example: “List top cited pages for product 6330 on example.com over the last 30 days.” Tool input:

productId

number

Siteline product ID. Omit it only when the MCP server should auto-select the only available product or ask you to choose one.

domain

string

Bare hostname, such as example.com. Omit it only when the MCP server should auto-select the only available website or ask you to choose one.

dateFrom

string

Inclusive RFC3339 timestamp. If omitted, the tool asks you to choose a preset or provide a custom range.

dateTo

string

Inclusive RFC3339 timestamp. Must be after dateFrom; date windows can be up to 366 days.

platforms

string[]

Optional platform filters. Supported values are chatgpt, perplexity, google, google_ai_overviews, google_ai_mode, gemini, chatgpt_impl1, and claude.

labelIds

number[]

Optional prompt label IDs to filter citations.

groupBy

string

page, property, or product. Defaults to page.

Expected return: structuredContent.citations, grouped according to groupBy. When groupBy is page, the return includes byPage rows with:

domain: Citation source domain
url: Citation source page URL
products: Mentioned products with name and domain
count: Citation count
isMentioned: Whether the selected product is mentioned

When groupBy is property, the return includes byProperty rows with source domain, mentioned products, count, and isMentioned. When groupBy is product, the return includes byProduct rows with product name, product domain, count, and isMentioned.

`analyze_product_analytics_timeseries`

Use this agent analytics tool to inspect AI crawler traffic and referred human traffic for product-owned websites over time. Prompt input example: “Show weekly OpenAI bot visits and AI-referred human visits for product 6330 over the last 30 days.” Tool input:

productId

number

Siteline product ID. Omit it only when the MCP server should auto-select the only available product or ask you to choose one.

dateFrom

string

Inclusive RFC3339 timestamp. If omitted, the tool asks you to choose a preset or provide a custom range.

dateTo

string

Inclusive RFC3339 timestamp. Must be after dateFrom; date windows can be up to 366 days.

interval

string

day or week. Defaults to day.

metrics

string[]

Optional metrics: bot_visits, human_visits, referred_human_visits, ai_referred_human_visits, unique_human_visitors, unique_pages, unique_human_pages, unique_human_platforms, or bot_user_agents.

groupBy

string[]

Optional dimensions: platform, botType, urlPath, normalizedUrlPath, or urlPathDirectory.

platforms

string[]

Optional platform filters, such as openai.

botTypes

string[]

Optional bot type filters, such as chatgpt-user.

urlPath

string

Optional normalized URL path filter.

page

number

Result page. Defaults to 1.

perPage

number

Rows per page. Defaults to 20; maximum is 500.

topLimit

number

Optional top-N row limit per time bucket, ordered by the first requested metric.

Expected return: structuredContent.timeseries, a paginated timeseries result. The timeseries result includes:

previousPage, currentPage, nextPage, totalPages, totalItems, and perPage
items: Timeseries rows with optional dimensions like platform, botType, urlPath, normalizedUrlPath, and timestamp
Metric fields such as botVisits, humanVisits, referredHumanVisits, aiReferredHumanVisits, uniqueHumanVisitors, uniquePages, uniqueHumanPages, uniqueHumanPlatforms, and botUserAgents

Configuration options

The Siteline MCP server does not currently expose URL query configuration options. Access is read-only and scoped through OAuth to the Siteline team you authorize.

Manual authentication

The Siteline MCP server uses dynamic client registration and browser-based OAuth. Manual public API key authentication is not supported for MCP clients.

Security risks

Connecting any data source to an LLM carries inherent risks. Siteline MCP is read-only, but AI tools can still retrieve and summarize analytics data that your team has access to.

Prompt injection

Prompt injection can trick an AI assistant into treating untrusted content as instructions. Review MCP tool calls before allowing them, especially in clients that can run tools automatically.

Recommendations

Review tool calls before approving them
Connect only MCP clients you trust
Use a Siteline team with the minimum data access needed
Remove the MCP server from clients that no longer need access
Revoke or reconnect the MCP client if you suspect a client token was exposed

Getting Started

Agent Analytics Integrations

Reporting

Reference

Guides

MCP server

MCP installation

Installation

Authentication

Next steps

How the MCP server works

Available tools

Product discovery

Prompt performance

Citations

Agent analytics

`list_products`

`list_product_websites`

`list_product_tracked_prompts`

`summarize_product_prompt_performance`

`analyze_product_prompt_trends`

`list_product_citations`

`analyze_product_analytics_timeseries`

Configuration options

Manual authentication

Security risks

Prompt injection

Recommendations

Getting Started

Agent Analytics Integrations

Reporting

Reference

Guides

Documentation Index

​MCP installation

​Installation

Authentication

​Next steps

​How the MCP server works

​Available tools

Product discovery

Prompt performance

Citations

Agent analytics

​list_products

​list_product_websites

​list_product_tracked_prompts

​summarize_product_prompt_performance

​analyze_product_prompt_trends

​list_product_citations

​analyze_product_analytics_timeseries

​Configuration options

​Manual authentication

​Security risks

​Prompt injection

​Recommendations

MCP installation

Installation

Next steps

How the MCP server works

Available tools

`list_products`

`list_product_websites`

`list_product_tracked_prompts`

`summarize_product_prompt_performance`

`analyze_product_prompt_trends`

`list_product_citations`

`analyze_product_analytics_timeseries`

Configuration options

Manual authentication

Security risks

Prompt injection

Recommendations