Searcherries: AI Visibility & SEO

Get Searcherries Project Data

get-project-data

Read-onlyIdempotent

Returns selected datasets of one AI Visibility project in one call: live visibility metrics, topics, customer questions, stored AI answers with their insights (answer text only with include_answer_text), cited URLs and domains, competitors aggregated for the period, GA4 AI traffic, Google Search Console and Bing performance snapshots (newest per 30/90/365-day window), business profile, monthly history and connection health. Omitting sections returns a compact overview profile (10 rows per list) with every dataset except ai_visibility.summary, which repeats the others.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "project_id": "<project_id>"
    },
    "name": "get-project-data"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Rows per paginated section, 1-100. Default 25; capped at 10 when sections is omitted.
`offset`	No	Rows to skip in every requested paginated section. Use with limit while has_more is true.
`period`	No	AI visibility period for metrics, answers, competitors and citations, anchored to the latest check date. Default last_30_days. Search and GA4 snapshots always return their 30/90/365-day windows.
`sections`	No	Sections to include. Omit for the compact overview of every dataset. Available: ai_visibility.metrics, ai_visibility.topics, ai_visibility.prompts, ai_visibility.mentions, citations.top_sites, citations.urls, competitors, recommendations, trust_mentions, ga4.ai_traffic, google_search_console.performance, bing_webmaster_tools.performance, ai_visibility.summary, ai_visibility.answer_insights, ai_visibility.search_queries, project.business_profile, history, connections (recommendations is retired and only returns a note).
`market_id`	No	Projects with several markets (country + language): narrow to one market, id from the markets list of list-projects or get-project-summary. Default: every market combined.
`project_id`	Yes	The AI Visibility project ID returned by list-projects.
`include_answer_text`	No	Include the stored answer text in ai_visibility.mentions (capped per answer, at most 20 rows). Default false.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 24, 2026

addedInput schema / properties / market_id

Added value: +{
+  "description": "Projects with several markets (country + language): narrow to one market, id from the markets list of list-projects or get-project-summary. Default: every market combined.",
+  "type": "integer"
+}

Changed1 schema field changedOct 24, 2026

changedInput schema / properties / sections / description

Previous value: -"Sections to include. Omit for the compact overview of every dataset. Available: ai_visibility.metrics, ai_visibility.topics, ai_visibility.prompts, ai_visibility.mentions, citations.top_sites, citations.urls, competitors, recommendations, trust_mentions, ga4.ai_traffic, google_search_console.performance, bing_webmaster_tools.performance, ai_visibility.summary, ai_visibility.answer_insights, ai_visibility.search_queries, project.business_profile, history, connections."New value: +"Sections to include. Omit for the compact overview of every dataset. Available: ai_visibility.metrics, ai_visibility.topics, ai_visibility.prompts, ai_visibility.mentions, citations.top_sites, citations.urls, competitors, recommendations, trust_mentions, ga4.ai_traffic, google_search_console.performance, bing_webmaster_tools.performance, ai_visibility.summary, ai_visibility.answer_insights, ai_visibility.search_queries, project.business_profile, history, connections (recommendations is retired and only returns a note)."

First observedSep 30, 2026

TDQS

A3.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish a safe, idempotent, closed-world read, so the bar is lower, and the description adds genuine behavioral detail beyond them: the 10-row cap when sections is omitted, the 20-row cap on answer text, the fact that summary repeats other datasets, and the retired recommendations section. It doesn't cover auth or rate-limit behavior, but for a read tool this is solid context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded, but the first sentence is a single run-on list of eighteen dataset names that duplicates the sections enum in the schema, and the second sentence re-explains omission behavior already covered by the schema. It's informative but carries avoidable redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and eighteen selectable sections, the description needs to carry return-shape information, yet it only names datasets without saying how the response is structured, ordered, or how has_more/pagination appears. It's adequate for deciding to call the tool but thin for a tool of this complexity.

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 description coverage is 100%, so all seven parameters are already documented in the schema, including the section-omission cap and market defaulting. The description's dataset enumeration largely restates the sections enum rather than adding syntax or edge-case meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete verb and resource ('returns selected datasets of one AI Visibility project') and enumerates the exact datasets returned, so an agent knows what it gets. It only implicitly distinguishes itself from the granular siblings (get-ai-answers, get-citations, get-competitors, get-search-performance) via the 'in one call' framing, without naming them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the key selection mechanic ('omitting sections returns a compact overview profile') and notes the retired recommendations section, which guides usage. However, it never states when to prefer this aggregate call over the sibling retrieval tools, nor any when-not conditions, leaving the choice to inference.

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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