SupplyGraph.AI Data

query_gov_innovation_index

gov_data_innovation

查询地区创新与知识产权宏观指标。覆盖：高新技术/创新产业企业数量、专利与研发、科技服务等。不含普通全行业企业存量总览（请用市场主体规模）。典型问法：某区高新技术企业数量、专利相关指标TOP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「高新企业专利等创新指标」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：武侯区高新技术企业数量

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

A4.5/5.0

Behavior4/5

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

Annotations are minimal (only openWorldHint), so the description carries the transparency burden. It adds meaningful behavioral context via the pricing block (credit per region × indicator × date unit) and scoping statements. However, it does not explicitly state read-only behavior or other side-effect details, though 'query' implies read-only. Pricing is a valuable disclosure.

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 compact and front-loaded with the core purpose, followed by coverage, exclusions, and typical examples. The pricing block is structured and non-redundant. Every sentence earns its place with no fluff.

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 a rich output schema, detailed parameter descriptions, and the description's clear scope plus exclusion, the tool is complete for an agent to select and invoke. The description differentiates from many sibling tools and provides enough behavioral and cost context.

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% with detailed explanations for all three parameters (versions, gov_names roles, input_text semantics). The description itself adds little beyond schema-provided semantics, but it does reinforce the indicator domain. Baseline 3 is appropriate as the schema already carries the load.

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 innovation and IP macro indicators, listing specific coverage areas (high-tech enterprise counts, patents/R&D, tech services). It explicitly excludes general industry enterprise stock overviews and points to an alternative tool, distinguishing it from siblings like gov_data_enterprise_scale.

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 exclusion guidance ('不含普通全行业企业存量总览') and a named alternative ('请用市场主体规模'), plus typical question examples (district high-tech enterprise counts, patent TOP cities). This gives the agent clear when-to-use and when-not-to-use context beyond just coverage.

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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