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Competitor visibility gap

get_competitor_visibility_gap
Read-onlyIdempotent

The prompts where one tracked competitor is mentioned in AI answers more often than the workspace's own brand.

Use this tool as a follow-up to get_competitor_comparison when the user wants to understand WHY a competitor is winning -- phrases like 'why is X beating us', 'where are we losing to X', 'what sources does AI cite for X', or 'how do we beat X'.

Returns: prompts where the competitor leads by at least min_gap_percent percentage points, each with the gap, the AI models that favor the competitor, and the URLs AI cites in those answers (the brand's own pages left out). Use this data for content briefs.

Example: 'Why is Sitoo beating Kassaro, and what sources does AI cite for them?'

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": {
      "workspace_id": "<workspace_id>",
      "competitor_name": "<competitor_name>"
    },
    "name": "get_competitor_visibility_gap"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNoHow many days back to look, counted back from end_date (default today). Default 30, max 90. Ignored when start_date is given. Prompts are re-run on a schedule, so a window shorter than a week can be empty.
end_dateNoOptional end of the window, as an ISO date (YYYY-MM-DD). Default today. Cannot be in the future.
start_dateNoOptional explicit start of the window, as an ISO date (YYYY-MM-DD). A full ISO datetime is accepted and its date is used. Overrides days.
prompt_limitNoHow many prompts to return. Default 10, max 50.
workspace_idYesThe workspace's numeric id, as returned by list_workspaces.
competitor_nameYesA tracked competitor's name, as get_competitor_comparison lists it. Case does not matter.
min_gap_percentNoSmallest lead to report, in percentage points (0-100). Default 5, meaning the competitor appears in at least 5 points more of the answers.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real context beyond that: the lead threshold filter, that each result carries the gap, favoring AI models, and cited URLs, and that the brand's own pages are excluded. It stops short of noting pagination or cost of re-running prompts, which keeps it off a 5.

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?

Front-loaded with the core definition, then usage, then return shape, then an example — a sensible order. The quoted example largely repeats the phrasings already listed in the usage paragraph, so it is the one element that does not fully earn its place.

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

Completeness5/5

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

There is no output schema, so the description must carry the return contract, and it does: what each row contains (gap, favoring models, cited URLs) and how results are filtered. Combined with the annotations and a fully documented 7-parameter schema, an agent has everything needed to call it correctly.

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 every parameter including min_gap_percent's point-based meaning is already documented in structured fields. The description's restatement of the min_gap_percent threshold is a mild reinforcement rather than new meaning, so the baseline 3 applies.

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

Purpose5/5

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

The first sentence names a specific resource (prompts where a tracked competitor is mentioned more often than the workspace's own brand) rather than restating the name. It also implicitly distinguishes the output from the aggregate comparison sibling, so an agent can tell what this returns without opening the schema.

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

Usage Guidelines5/5

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

Explicitly frames the tool as a follow-up to get_competitor_comparison and enumerates the user phrasings that should route here ('why is X beating us', 'what sources does AI cite for X'). That is a direct when-to-use signal plus a named alternative, which is the strongest form of guidance.

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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