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kevynf

AKBridge MCP Server

by kevynf

bond_gb_us_sina

Read-onlyIdempotent

Fetch US Treasury bond yields for selected maturities from Sina Finance, covering 1-month to 30-year rates. Monitor interest rate changes and make informed financial decisions.

Instructions

新浪财经-债券-美国国债收益率行情数据 https://stock.finance.sina.com.cn/forex/globalbd/cn10yt.html :param symbol: choice of {"美国1月期国债", "美国2月期国债", "美国3月期国债", "美国4月期国债", "美国6月期国债", "美国1年期国债", "美国2年期国债", "美国3年期国债", "美国5年期国债", "美国7年期国债", "美国10年期国债", "美国20年期国债", "美国30年期国债"} :type symbol: str :return: 美国国债收益率行情数据 :rtype: pandas.DataFrame

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo美国10年期国债
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds a source URL and return type, but does not disclose additional behaviors such as whether data is historical or real-time, or any rate limits. This is acceptable given the annotations, but not rich.

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 concise docstring-style entry with the title, source URL, parameter documentation, and return type. Every part provides necessary information with no fluff, and the structure is immediately scannable.

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

Completeness3/5

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

For a simple single-parameter data retrieval tool, the description provides a source URL, allowed symbols, and return type. However, it does not clarify whether the returned DataFrame contains historical or current yields, nor the columns included, leaving some ambiguity for the agent.

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

Parameters4/5

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

The input schema has no description for the 'symbol' property, but the description compensates by fully enumerating all 13 allowed choices (e.g., 美国10年期国债). This adds significant meaning beyond the schema, even though it doesn't explain the semantic differences between maturities.

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 explicitly states the tool provides US Treasury yield market data from Sina Finance, which identifies the resource and function. However, it does not distinguish itself from sibling tools like bond_gb_zh_sina or bond_zh_us_rate, so it lacks full differentiation.

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 by naming the data source and listing acceptable symbol values, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it state any exclusions or prerequisites.

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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