MentionedOn Visibility and Website Fixes

See share of voice and cited sources

get_share_of_voice

Read-onlyIdempotent

Needs a connected MentionedOn account. Use for 'who does AI recommend instead of us?': returns how often saved answers name this business versus every other business they name, its rank over saved checks, and the kinds of sites cited (own site, directories, community, press, competitors). Needs businessId. Read-only; built from saved checks, no scan.

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": {
      "businessId": "<businessId>"
    },
    "name": "get_share_of_voice"
  }
}

Input Schema

Name	Required	Description	Default
`businessId`	Yes	Business id returned by list_monitored_businesses.

Output Schema

Name	Required	Description	Default
`reportUrl`	Yes
`businessId`	Yes
`capturedAt`	Yes
`citationMix`	Yes
`limitations`	Yes
`shareOfVoice`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 3, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already cover readOnly, idempotent, non-destructive and closed-world, so the bar is lower. The description still adds genuinely new context: an external-account prerequisite (connected MentionedOn account) and provenance/freshness (results derived from saved checks, not a fresh scan). It does not address empty states or what happens when no saved checks exist.

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 prerequisite, then the use case, then the return contents, then the parameter constraint. Dense and mostly waste-free, though the return clause runs long and the telegraphic fragments ('Read-only; built from saved checks, no scan.') read as clipped notes rather than prose.

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?

For a single-parameter read tool with full annotations and an output schema, the description covers everything an agent needs: prerequisite, selection trigger, return shape at a high level, and the required identifier. Return value detail is correctly delegated to the output schema.

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% and the schema already explains businessId as the id returned by list_monitored_businesses. The description only restates the requirement ('Needs businessId') without adding format or sourcing detail, so 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?

States a specific verb and resource set: returns how often saved answers name this business versus every other business, its rank, and the kinds of sites cited. The parenthetical taxonomy (own site, directories, community, press, competitors) makes the output shape unambiguous and distinguishes it from report-style siblings.

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?

Gives an explicit trigger query ('who does AI recommend instead of us?') and a clear data-source boundary (built from saved checks, no scan), which separates it from scan-driven siblings. It stops short of naming an alternative tool or stating when-not-to-use, so it is strong but not complete.

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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