Skip to main content
Glama
Sign In

Refora

by ai.refora
OverviewDiscussionsAdmin

Visibility score

get_visibility_score
Read-onlyIdempotent

How visible the workspace's own brand is in AI answers: the share of AI answers that mention it, overall and per AI model (ChatGPT, Perplexity, Gemini, Claude, Mistral, Google AI Overviews), and the change from the window before.

Use this tool when the user asks about current performance -- phrases like 'how are we doing', 'what's our score', 'how visible are we in AI', or 'current visibility'. Do NOT use for trends over time (use get_visibility_trend) or competitors (use get_competitor_comparison).

Returns: visibility in percent (null means no answers to measure, not zero), the change in percentage points, one row per model with its visibility and number of answers, and a summary.

Example: 'How is Kassaro doing in AI search right now?'

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>"
    },
    "name": "get_visibility_score"
  }
}

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.
workspace_idYesThe workspace's numeric id, as returned by list_workspaces.

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 genuinely non-obvious semantics: null means no answers were measured rather than zero, and the change is expressed in percentage points against the prior window. It stops short of stating data freshness or rate limits, and the schema's note about re-run scheduling is the only hint about latency.

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-loads the metric definition, then usage, then exclusions, then return shape — a logical order with no filler sentences. The closing example slightly restates the usage trigger already covered above, which is the only mild redundancy.

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: percentage value, null-vs-zero caveat, point delta, one row per model with answer counts, and a summary. Combined with the fully documented input schema and read-only annotations, an agent has everything needed to call and interpret this tool.

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% and the parameter docs are detailed (days max 90, start_date overrides days, end_date cannot be future). The description adds nothing about windowing or defaults, so the schema does all the work — the expected baseline.

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?

Opens with a precise definition of the metric (share of AI answers mentioning the workspace's brand), names the breakdown dimensions (per-model, overall) and the comparison baseline. An agent can tell exactly what this returns and how it differs from a trend or competitor tool 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?

Explicit when-to-use triggers are given as user phrasings ('how are we doing', 'what's our score'), plus an explicit negative routing rule naming get_visibility_trend for time-series and get_competitor_comparison for competitors. Both the inclusion and exclusion conditions are stated, which is the top of the scale.

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

All Refora toolsInspect
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.

Resources