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SupplyGraph.AI Data

by io.github.SupplyGraphAI

Enterprise Change Branch Setup

enterprise_change_branch_setup

基于具体企业名称，按企业查询经营活动方面的周期变化，用于查询异地分公司子公司的新设、注销与存量。不用于对外股权投资或融资引入等投融资活动查询。 涉及指标/类型：是否新设立了异地分公司；是否注销了异地分公司；是否新设立了异地子公司；是否注销了异地子公司；是否新设立了异地控股子公司；是否注销了异地控股子公司；是否新设立了异地全资子公司；是否注销了异地全资子公司；拥有的异地分公司总数是多少；拥有的异地子公司总数是多少；拥有的异地控股子公司总数是多少；拥有的异地全资子公司总数是多少 不包含：非本分类指标；按园区/产业链批量筛企业名单 典型问法：中国比亚迪股份有限公司是否新设立了异地分公司；美国Tesla, Inc.是否注销了异地分公司；日本丰田自动车株式会社是否新设立了异地子公司

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 120, "unit_description": "optional"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYes企业名称，如「比亚迪股份有限公司」「Tesla, Inc.」。
country_nameYes国家名称，如「中国」「美国」「Japan」「China」。

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText or Markdown response generated by the agent. Returned for completed results as well as in-progress, failed, cancelled, or waiting-user messages.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
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 clearly defines the exact data coverage and exclusions, and implies it is a read-only query. It does not mention rate limits or pagination, but these are partially covered by the output schema. No contradiction with annotations.

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?

The description is moderately long due to exhaustive indicator listing, but it is well-structured: purpose first, then included metrics, exclusions, and examples. The length is justified by the need to define the tool's narrow scope among many siblings. No filler content.

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 tool's complexity (12 distinct indicators) and the presence of an output schema, the description is remarkably complete. It lists all indicators, explicitly states exclusions, provides typical questions, and includes pricing. It sufficiently equips an agent to select and invoke the tool correctly.

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 coverage is 100% with descriptions for both parameters (company_name, country_name). The description adds representative question examples that illustrate parameter usage, but does not introduce constraints or formats beyond the schema. Meets the baseline for full schema coverage.

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 operational cycle changes for out-of-town branches/subsidiaries (new, cancellation, stock) based on enterprise name, and explicitly differentiates from investment financing queries and batch filtering. It enumerates specific indicators, making the scope unambiguous.

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 (queries about branch/subsidiary changes) and when not to use (investment financing, non-category indicators, park/chain batch filtering). It includes typical question examples, giving clear usage context and exclusions.

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).

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