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Unilytics MCP Server

Unilytics MCP Server

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by TeamUnilytics
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JavaScript
Remote

get_ai_citation_check

Read-only

Retrieve the status, progress, and per-engine metrics of an AI citation check, plus top cited domains and URLs, to track citation performance and diagnose failures.

Instructions

Read one of the user's AI citation checks: status, progress and every metric so far.

status is running (some engines still answering), complete, expired (runs unfinished after 24 hours), or failed
(the check didn't start; it has no results and wasn't charged). Metrics come per engine, per prompt and across
engines (each engine weighted equally), each with its numerator and denominator; a null value means nothing to
measure yet (a zero denominator), not 0%. Also returns the top cited domains (up to 30) and URLs (up to 25) with
their source category, the category mix per segment, failures by engine and the metric definitions. Free.

Args:
    job_id: From ai_citation_check.

Input Schema

NameRequiredDescriptionDefault
job_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Well beyond the readOnly/destructive annotations, the description defines each status value (including that 'failed' means no results and no charge), explains that metrics are per engine/prompt/cross-engine with equal engine weighting, and clarifies that null means a zero denominator rather than 0%. It also discloses result cardinality limits (30 domains, 25 URLs) and that the call is free.

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?

Purpose is front-loaded and every section (status semantics, metric semantics, return contents, cost, args) carries distinct information. The prose is dense with parentheticals and long clauses, but no sentence is purely filler.

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?

With no output schema, the description compensates by enumerating the returned payload (status, per-engine/per-prompt metrics with numerators and denominators, cited domains and URLs with categories, category mix, failures, metric definitions). For a simple one-parameter read tool, nothing material is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the only parameter is a bare string 'job_id', so the description must carry the meaning — and it does, stating the ID comes from ai_citation_check. It stops short of any format or validity detail (e.g., whether an unknown/expired job_id errors), which keeps it below a 5.

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 first sentence gives a specific verb and resource — 'Read one of the user's AI citation checks' — plus what it returns (status, progress, metrics), which cleanly separates it from the sibling ai_citation_check that initiates a check. An agent can identify the operation 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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: the arg note 'job_id: From ai_citation_check' and the enumeration of status values (running/complete/expired/failed) suggest a post-start polling workflow, but the description never explicitly says 'call ai_citation_check first' or 'poll until status is complete'. No when-not-to-use guidance is given.

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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