Citation Intelligence MCP

domain_cited_for

Read-onlyIdempotent

Retrieve the search queries a domain is cited for from a local cache, avoiding API costs. Populate the cache first using citation checks.

Instructions

List queries that the given domain has been cited for, served from the local cache. Build up a corpus by calling check_citations or am_i_cited first; cited_for queries it without spending API budget.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Maximum results.
`since`	No	ISO date floor, e.g. '2026-01-01'. Only return entries fetched on or after this date.
`domain`	Yes	Domain to look up, e.g. 'automatelab.tech'.
`engine`	No	Filter by engine. Omit to include all.

Output Schema

TableJSON Schema

Name	Required	Description
`since`	No	ISO date floor applied, if any.
`total`	Yes	Total entries returned.
`domain`	Yes	Domain that was looked up.
`source`	Yes	Always 'local_cache' — no external calls made.
`results`	Yes	Cache entries where this domain was cited.
`engine_filter`	No	Engine filter applied, if any.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.1.2Oct 2, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations cover readOnly, idempotent, openWorld=false, and non-destructive. The description adds key behavioral context beyond annotations: it is served from local cache and does not spend API budget. This is valuable context not in annotations.

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 tight sentences, front-loaded with what the tool does, followed by the prerequisite and cost-saving trait. No wasted words.

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?

Given an output schema exists and annotations cover safety, the description provides the key missing context: the tool reads from local cached corpus and is free. It doesn't mention pagination or return format, but those are covered by the output schema and parameter descriptions.

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 documents all four parameters (limit, since, domain, engine). The description doesn't add parameter-level semantics beyond what the schema provides, so baseline 3 is appropriate.

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 ('List queries that the given domain has been cited for') with clear scope. It also names the sibling tools (check_citations, am_i_cited) that build the corpus, distinguishing it from related tools.

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 to call check_citations or am_i_cited first to build a corpus, and notes it 'queries it without spending API budget.' Clear usage context and prerequisite, though it doesn't explicitly state when not to use this tool.

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