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Citehound

by io.github.andacuzel
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Get citation questions for a vertical

get_citation_prompts
Read-onlyIdempotent

Returns the set of buying-intent questions Citehound uses to test whether an AI assistant names a brand, for one vertical, plus a short protocol a person can run in their own assistant: ask each question in a separate conversation three to five times, note whether the brand is named and whether it appears in a heading, list label, table or bold text, and judge the spread rather than one answer. The questions don’t name any brand in the category being measured. A few mention an integration platform such as Slack or Google Workspace, because that is how buyers ask. Read-only: it reads a content file and queries no model. There is deliberately no tool that runs the check for you: a run is about 90 model calls at about five seconds each, far past this server’s time limit, and it would spend a quota anyone could drain. Question sets exist for: consumerapps, crm, cybersecurity, devtools, ecommerce, fintech, health, hospitality, hrtech, legal, localservices, marketplaces, martech, realestate. Use when the user asks how to check whether an assistant names their brand, or wants the questions for their category.

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": {
      "vertical": "<vertical>"
    },
    "name": "get_citation_prompts"
  }
}

Input Schema

NameRequiredDescriptionDefault
verticalYesA vertical slug. Valid: consumerapps, crm, cybersecurity, devtools, ecommerce, fintech, health, hospitality, hrtech, legal, localservices, marketplaces, martech, realestate. Question sets exist for: consumerapps, crm, cybersecurity, devtools, ecommerce, fintech, health, hospitality, hrtech, legal, localservices, marketplaces, martech, realestate.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, closed-world behavior. The description adds substantial context beyond that: it reads a content file and queries no model, the questions avoid brand names, some mention integration platforms, and a run would be about 90 model calls at roughly five seconds each, exceeding server limits and consuming quota.

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 description is front-loaded and well organized, but it is long for a single-parameter lookup tool. It repeats the valid vertical list that already appears in the schema and includes several explanatory sentences that could be tightened.

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?

No output schema exists, so the description must explain the return value, and it does: questions plus a short protocol. It also covers scope, safety, limitations, and supported verticals, leaving no significant gap for an agent to call the tool 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 the schema already fully documents the required vertical slug and enumerates valid values. The description repeats the valid vertical list but adds little parameter-specific meaning beyond what the schema provides.

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: returns the set of buying-intent questions used to test whether an AI assistant names a brand, for one vertical. It also explains what the result includes (questions plus a short protocol), distinguishing it from checking tools and sibling tools.

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 says when to use it: when the user asks how to check whether an assistant names their brand or wants questions for their category. It also explains why there is no tool that runs the check, effectively ruling out an expected alternative.

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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