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savantcat

geo-cn

by savantcat
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

score_visibility

Read-onlyIdempotent

Calculate AI search visibility across Chinese AI platforms using five core metrics and semantic-role weighting, then return per-question details, citation verification, and unverified claim flags.

Instructions

按五大核心指标 + 语义角色分权，计算 AI 搜索可见度并输出可自验的测量报告。

输入 samples 为 JSON 字符串：
{"brand":"合尘猫","domain":"savantcat.cn","baseline_negative":3,
 "fact_points":["服务范围","交付周期","定价方式"],
 "samples":[{"question":"Q1","platform":"DeepSeek","run":1,"answer":"原文回答……",
             "role":"independent|joint|citation_only|none",
             "sentiment":"positive|neutral|negative",
             "facts":"accurate|partial|wrong|unverifiable",
             "facts_found":["服务范围"],"as_of":"2026-09-19"}]}
口径：每题建议多轮（同一问题重复 7-8 次）；role 分级对应语义角色权重；
返回五大指标、语义角色加权分、逐题明细与「原文摘录」（便于客户自验）。
verify_citations=True 时会实测 AI 回答里引用的 URL 是否真的可访问（幻觉守卫）；
extract_claims=True 时会把回答中的数字声明单独列出并标记为未核验。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
samplesYes
extract_claimsNo
verify_citationsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral detail beyond that: it produces per-question breakdowns, original excerpts for client self-verification, live URL accessibility checks when verify_citations=True, and extracted numeric claims marked as unverified. This is consistent with the annotations and discloses important external-check behavior.

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?

The description is long but dense and every sentence earns its place: overview, sample JSON template, methodology note, output contents, and optional flag behaviors. The critical invocation details are front-loaded, and the inline JSON example is an efficient substitute for missing schema descriptions.

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?

Given the complexity of a nested JSON-string input and zero schema field descriptions, the description is complete enough for correct invocation. It covers required input structure, suggested sampling frequency, output composition, and optional verification modes. An output schema exists, so the description is not obligated to enumerate every return value.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by embedding the exact JSON structure for the samples string, including all relevant fields, enums for role/sentiment/facts, arrays, and the date format. It also explains the two optional boolean parameters and their effects. This is strong parameter-level guidance where the schema itself provides none.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's core operation: '按五大核心指标 + 语义角色分权，计算 AI 搜索可见度并输出可自验的测量报告'. This is a specific verb and resource, and the tool is clearly a measurement/reporting tool rather than a fixer or auditor. However, it does not explicitly contrast itself with sibling tools such as audit_cn_citability or plan_fixes, so it misses full sibling differentiation.

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?

The description gives concrete usage guidance: samples must be a JSON string, multiple rounds are recommended ('每题建议多轮（同一问题重复 7-8 次）'), and it explains what happens when verify_citations or extract_claims are enabled. It does not explicitly state when this tool should be preferred over alternatives or when it should not be used, so it falls just short of a 5.

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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