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AutomateLab-tech

Citation Intelligence MCP

check_citations

Retrieve URLs cited by major AI engines (Perplexity, Claude, ChatGPT, Gemini, Bing) for any search query to verify sources.

Instructions

Return URLs cited by an AI engine (Perplexity, Claude, ChatGPT, Gemini, or Bing) for a query. Use this when an agent or user wants to see what sources an AI search engine grounds answers on. Requires at least one engine API key; auto-picks the first available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query to test (what would a user ask an AI?)
engineNoEngine to query. • perplexity / google_ai_mode — consumer_scrape: closest to real product behavior. • claude / openai / gemini — api_proxy: API-tier call, may differ from consumer product. • bing_serp / brave_serp — web_rank: traditional SERP rank, NOT LLM citation. 'auto' prefers SerpAPI (google_ai_mode) → Perplexity → LLM adapters → web_rank.auto
max_resultsNoMaximum citations to return.
perplexity_modelNoPerplexity model override (e.g. 'sonar', 'sonar-pro', 'sonar-reasoning'). Only used when engine='perplexity'. Defaults to 'sonar-pro'.
Behavior4/5

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

Without annotations, description carries full burden. It discloses auto-pick engine behavior and differences between engine types (consumer_scrape, api_proxy, web_rank) via the engine parameter description. However, it lacks mention of rate limits or failure modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that front-load the purpose and usage context. No extraneous content.

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?

No output schema, but description hints at returning a list of URLs. For a tool with engine selection logic and 4 params, it provides sufficient context, though explicit output structure would help.

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 baseline is 3. The tool description adds value by explaining the API key requirement and when to use, but parameter details are fully covered in the schema.

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 description clearly states the tool returns URLs cited by AI engines for a query, using a specific verb and resource. It distinguishes from sibling tools like 'citation_evidence' or 'citation_provenance' by focusing on AI search engine sources.

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?

Explicitly says when to use ('when agent or user wants to see sources an AI search engine grounds answers on') and mentions the API key prerequisite. Does not explicitly list alternative tools but context is clear.

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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