SupplyGraph.AI Data

query_gov_public_opinion_index

gov_data_public_opinion

查询地区舆情与安全感知宏观指标。覆盖：网络热度/口碑/抱怨影响力、官媒宣传影响力、社会与生态安全事件影响。不含环境物理监测值主口径（空气质量等请用生态环境）或 POI 明细。典型问法：某区空气污染网络热度、垃圾治理舆情较高的城市。

Pricing: {"unit": "credits", "billing_model": "per_data_unit", "meter": {"credits_per_unit": 1, "unit_description": "One data unit = one region × one indicator × one date version (example: Chengdu × permanent population × 2023). Charged by returned units after query, capped by the user request."}}

Input Schema

TableJSON Schema

Name	Required	Description
`versions`	No	可选期望年份/日期软约束，如 ['2022'] 或 ['2022-12-01']；取数以库内真实版本为准，不一致时标注 version_mismatch。
`gov_names`	No	可选地区名列表。point/compare：目标地区；rank/list/filter：父级范围（如 ['四川省']/'成都市'）；peer_rank：目标地区（可另附上级）；不传时尝试从 input_text 抽取。
`input_text`	Yes	用户查询文本，描述「网络舆情热度与安全感知指标」指标意图；支持点查、TOP/排名、多地对比、下级列表、阈值筛选、同级位次等。示例：武侯区空气污染网络热度指数

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedAug 19, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Annotations only include openWorldHint; no readOnlyHint or behavioral details. The description clarifies scope (what is included and excluded) but does not mention side effects, data volume, or read-only nature. For a query tool, this is a moderate gap but not contradictory.

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 concise, using two sentences plus examples. It avoids redundancy, clearly delineates covered and excluded items, and provides useful illustrations without unnecessary elaboration.

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?

For a query tool, the description is complete: it states the domain, lists coverage areas, excludes related but distinct data sources, gives typical use cases, and implicitly indicates it returns macro indicators. Absence of output schema details is acceptable given typical query semantics.

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?

All three parameters have schema descriptions (100% coverage). The tool description adds example queries but does not significantly enhance parameter meaning beyond what the schema already provides. 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?

The description clearly states the tool queries regional public opinion and safety perception macro indicators, enumerates covered types (network heat, official media influence, social/ecological safety events), and explicitly differentiates from sibling tools by excluding environmental physical monitoring and POI details. Typical query examples further illustrate the purpose.

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?

The description provides explicit guidance on when to use this tool versus alternatives, stating that for environmental physical monitoring (e.g., air quality) one should use the ecological environment tool and that POI details are not included. Typical query formats are given, helping the agent decide applicability.

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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TDQS

B3.2/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, with consistent scopes (e.g., chain_* vs park_* vs company_* vs gov_data_*). The list/num pairs are clearly differentiated. A few overlapping concepts exist (e.g., company_patent vs enterprise_change_innovation) but descriptions clarify the angle. Some typos (company_randomin_spection) don't cause ambiguity.

Naming Consistency4/5

Naming follows a mostly predictable snake_case pattern with prefixes indicating domain (chain_, park_, company_, enterprise_change_, gov_data_, poi_data_, business_surrounding_, cbd_surrounding_). Most tools use <prefix>_<entity>_<action> or <prefix>_<subject>. A few outliers (sg_chokepoint, tariff_calc, corporate_exception_report) deviate but are few and recognizable.

Tool Count1/5

With 198 tools, this is far beyond any reasonable scope for a single server. It exceeds even the 'extreme mismatch' threshold of 50+ tools. The large number makes selection and discoverability challenging, despite good internal organization.

Completeness4/5

The tool surface covers a vast range of enterprise data, regional macro stats, POI details, supply chain analysis, and tariffs. It appears to cover the primary domain comprehensively, with only minor potential gaps (e.g., no direct tool for company debt ratings or specific product catalogs, but these are addressed via enterprise_change_* and company_* tools).

Resources

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