Searcherries: AI Visibility & SEO

Get Competitors

get-competitors

Read-onlyIdempotent

Brands the AI platforms name for the tracked questions. dataset=ranking (default) returns one row per brand for the period, own brand included, ranked by visibility with mentions, citations, average position, per-platform numbers and a few deduplicated highlights of what platforms say about each brand. dataset=detail returns one brand (name fragment in "competitor") with all its highlights. dataset=history returns the stored month-by-month snapshots (own brand and top competitors per month).

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-competitors"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Rows per page for ranking (default 15, max 50) or months for history (default 12).
`offset`	No	Rows to skip in the ranking. Use with has_more.
`period`	No	Report period anchored to the latest check. Default last_30_days.
`dataset`	No	ranking (default), detail (one brand, needs competitor), history (monthly snapshots).
`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.
`competitor`	No	Brand name or fragment for dataset=detail, e.g. "otterly".
`project_id`	Yes	The AI Visibility project ID returned by list-projects.
`include_highlights`	No	Include per-platform highlight sentences in the ranking. Default true.
`highlights_per_platform`	No	Highlights per platform in the ranking, 0-5. Default 3.

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"
+}

First observedSep 30, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds value by explaining what each dataset returns (ranked rows with mentions/citations/position, per-brand highlights, monthly snapshots), though it omits auth/rate-limit context.

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?

It is front-loaded with the core purpose, then breaks down the three datasets in an ordered, scannable way. Slightly dense but every clause carries information about output shape.

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?

With no output schema, the description steps up by describing the return contents for each dataset, which an agent needs to call it correctly. Given annotations cover the safety profile, this is largely complete.

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%, so all 9 parameters are already documented. The description adds the dataset-mode semantics and the 'name fragment in competitor' detail, but for a fully covered schema this is baseline added meaning.

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 states a specific verb (list/brands the AI platforms name) and resource (competitors/brands for tracked questions) and details three dataset modes. It does not explicitly distinguish itself from siblings like get-project-summary or get-citations, but the scope is clear.

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?

It explains which dataset to choose (ranking default, detail needs a competitor, history for monthly snapshots) and notes own brand is included in ranking. It gives clear context for mode selection without naming sibling alternatives for related queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Searcherries: AI Visibility & SEO tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.