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SupplyGraph.AI.Daasmart

query_company_chattel_mortgage

company_chattel_mortgage

基于明确指定的企业名称,查询该企业涉及的动产抵押信息,包括登记日期、状态、被担保债权数额、登记机关、登记编号等。

Pricing: {"unit": "credits", "billing_model": "per_run", "per_run": 0.2}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo指定返回第几页结果,从 1 开始,默认 1;与 limit 配合使用。
limitNo指定单次请求最多返回的记录数,默认 20,最大 100。
company_nameYes企业名称(必填)。用于查询该企业的动产抵押。示例:通威股份有限公司

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations include openWorldHint=true, which already signals potential side effects/external dependencies. The description adds no extra transparency (e.g., rate limits, authentication, or side-effect details). It does not contradict the annotation and correctly frames the tool as a query operation, so a neutral 3 is appropriate given the annotation's presence.

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 a single sentence that directly states the purpose and key fields, followed by pricing info that is concise and non-intrusive. No redundant or irrelevant information is present, making it highly efficient and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is a straightforward query with a small parameter set and an output schema. The description mentions key returned fields and provides a clear use case. It is complete enough for an agent to understand the tool’s function without needing additional context, though it could mention pagination behavior more explicitly, but the schema covers that.

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 each parameter (page, limit, company_name) having clear descriptions, including an example for company_name. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 applies.

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 chattel mortgage information for a specified enterprise, listing specific fields (registration date, status, secured claim amount, etc.). It is specific and distinguishes from sibling tools like company_equity_pledge, which focuses on equity pledges. The purpose is unambiguous.

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?

The description implies usage (when the user needs chattel mortgage info for a company), but it does not explicitly mention when to use this tool over alternatives or provide exclusions. Given the large sibling tool list, some explicit guidance would help, but the purpose is strong enough that an agent can infer when to invoke it.

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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