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Glama

disclosure_date

财报披露日历(财务数据) 需要 PRO 及以上套餐(低档位调用返回 403)。

Args: symbol: 证券代码(带后缀),如 000001.SZ start_date: 起始日期 YYYYMMDD end_date: 结束日期 YYYYMMDD pre_date: 预计披露日 YYYYMMDD actual_date: 实际披露日 YYYYMMDD

Returns: JSON 数组;字段: symbol, ann_date, end_date, pre_date, actual_date, modify_date

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo证券代码(带后缀),如 000001.SZ
end_dateNo结束日期 YYYYMMDD
pre_dateNo预计披露日 YYYYMMDD
start_dateNo起始日期 YYYYMMDD
actual_dateNo实际披露日 YYYYMMDD

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description discloses the plan requirement and error behavior for low-tier calls, and describes the return fields. However, it does not mention behavior when all parameters are omitted, sorting, or other potential side effects.

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 well-structured with a title, requirement note, args, and returns. It is relatively concise but repeats schema information, making it slightly redundant.

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?

An output schema exists and the description lists return fields, but it lacks details on parameter interactions, such as what happens if only symbol is provided or if no dates are specified. The optional nature of all parameters is unexplained.

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% and the description's Args section duplicates the schema descriptions exactly, adding no new meaning. The baseline of 3 applies as the schema already documents all parameters.

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 identifies the tool as a financial disclosure calendar and clearly states it returns a JSON array of disclosure dates. However, it lacks an explicit verb like 'get' or 'list', and does not differentiate from sibling tools.

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 only usage guidance is the PRO requirement and 403 error for lower tiers. It implies usage for retrieving disclosure dates but offers no explicit comparison to alternatives or when-not-to-use conditions.

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

B3.3/5.0
Disambiguation3/5

Several tools have overlapping purposes or unclear names, such as daily vs etf_daily vs index_daily vs fx_daily, and fundamentals vs technical_factors_pro (which also includes PE/PB). top_inst and top_list are also easily confused. Descriptions help, but the names alone are not always sufficient to distinguish them.

Naming Consistency3/5

All names use lowercase with underscores, which is consistent, but there is variation in number (daily vs stocks), specificity (daily vs index_daily), and verbosity (top_inst vs top_list). The pattern is not uniform across the set, making it less predictable.

Tool Count3/5

45 tools is on the heavy side for an MCP server, and there is redundancy (technical_factors and technical_factors_pro overlap significantly). For a broad financial data API, the count is justifiable, but it borders on overwhelming.

Completeness4/5

The tool set covers a wide range of financial data: quotes, fundamentals, technicals, financial statements, corporate actions, money flows, ETF data, index data, and news. There are minor gaps (e.g., no bond data) but the core domain of Chinese A-share/ETF/FX data is well covered.

Resources