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technical_factors_pro

技术面因子(专业版,含复权价+技术指标)(行情数据) 需要 Expert 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 trade_date: 单个交易日 YYYYMMDD(与 start/end 二选一)

Returns: JSON 数组;字段: symbol, trade_date, open, open_hfq, open_qfq, high, high_hfq, high_qfq, low, low_hfq, low_qfq, close, close_hfq, close_qfq, pre_close, change, pct_chg, vol, amount, turnover_rate, turnover_rate_f, volume_ratio, pe, pe_ttm, pb, ps, ps_ttm, dv_ratio, dv_ttm, total_share, float_share, free_share, total_mv, circ_mv, adj_factor, asi_bfq, asi_hfq, asi_qfq, asit_bfq, asit_hfq, asit_qfq, atr_bfq, atr_hfq, atr_qfq, bbi_bfq, bbi_hfq, bbi_qfq, bias1_bfq, bias1_hfq, bias1_qfq, bias2_bfq, bias2_hfq, bias2_qfq, bias3_bfq, bias3_hfq, bias3_qfq, boll_lower_bfq, boll_lower_hfq, boll_lower_qfq, boll_mid_bfq, boll_mid_hfq, boll_mid_qfq, boll_upper_bfq, boll_upper_hfq, boll_upper_qfq, brar_ar_bfq, brar_ar_hfq, brar_ar_qfq, brar_br_bfq, brar_br_hfq, brar_br_qfq, cci_bfq, cci_hfq, cci_qfq, cr_bfq, cr_hfq, cr_qfq, dfma_dif_bfq, dfma_dif_hfq, dfma_dif_qfq, dfma_difma_bfq, dfma_difma_hfq, dfma_difma_qfq, dmi_adx_bfq, dmi_adx_hfq, dmi_adx_qfq, dmi_adxr_bfq, dmi_adxr_hfq, dmi_adxr_qfq, dmi_mdi_bfq, dmi_mdi_hfq, dmi_mdi_qfq, dmi_pdi_bfq, dmi_pdi_hfq, dmi_pdi_qfq, downdays, updays, dpo_bfq, dpo_hfq, dpo_qfq, madpo_bfq, madpo_hfq, madpo_qfq, ema_bfq_10, ema_bfq_20, ema_bfq_250, ema_bfq_30, ema_bfq_5, ema_bfq_60, ema_bfq_90, ema_hfq_10, ema_hfq_20, ema_hfq_250, ema_hfq_30, ema_hfq_5, ema_hfq_60, ema_hfq_90, ema_qfq_10, ema_qfq_20, ema_qfq_250, ema_qfq_30, ema_qfq_5, ema_qfq_60, ema_qfq_90, emv_bfq, emv_hfq, emv_qfq, maemv_bfq, maemv_hfq, maemv_qfq, expma_12_bfq, expma_12_hfq, expma_12_qfq, expma_50_bfq, expma_50_hfq, expma_50_qfq, kdj_bfq, kdj_hfq, kdj_qfq, kdj_d_bfq, kdj_d_hfq, kdj_d_qfq, kdj_k_bfq, kdj_k_hfq, kdj_k_qfq, ktn_down_bfq, ktn_down_hfq, ktn_down_qfq, ktn_mid_bfq, ktn_mid_hfq, ktn_mid_qfq, ktn_upper_bfq, ktn_upper_hfq, ktn_upper_qfq, lowdays, topdays, ma_bfq_10, ma_bfq_20, ma_bfq_250, ma_bfq_30, ma_bfq_5, ma_bfq_60, ma_bfq_90, ma_hfq_10, ma_hfq_20, ma_hfq_250, ma_hfq_30, ma_hfq_5, ma_hfq_60, ma_hfq_90, ma_qfq_10, ma_qfq_20, ma_qfq_250, ma_qfq_30, ma_qfq_5, ma_qfq_60, ma_qfq_90, macd_bfq, macd_hfq, macd_qfq, macd_dea_bfq, macd_dea_hfq, macd_dea_qfq, macd_dif_bfq, macd_dif_hfq, macd_dif_qfq, mass_bfq, mass_hfq, mass_qfq, ma_mass_bfq, ma_mass_hfq, ma_mass_qfq, mfi_bfq, mfi_hfq, mfi_qfq, mtm_bfq, mtm_hfq, mtm_qfq, mtmma_bfq, mtmma_hfq, mtmma_qfq, obv_bfq, obv_hfq, obv_qfq, psy_bfq, psy_hfq, psy_qfq, psyma_bfq, psyma_hfq, psyma_qfq, roc_bfq, roc_hfq, roc_qfq, maroc_bfq, maroc_hfq, maroc_qfq, rsi_bfq_12, rsi_bfq_24, rsi_bfq_6, rsi_hfq_12, rsi_hfq_24, rsi_hfq_6, rsi_qfq_12, rsi_qfq_24, rsi_qfq_6, taq_down_bfq, taq_down_hfq, taq_down_qfq, taq_mid_bfq, taq_mid_hfq, taq_mid_qfq, taq_up_bfq, taq_up_hfq, taq_up_qfq, trix_bfq, trix_hfq, trix_qfq, trma_bfq, trma_hfq, trma_qfq, vr_bfq, vr_hfq, vr_qfq, wr_bfq, wr_hfq, wr_qfq, wr1_bfq, wr1_hfq, wr1_qfq, xsii_td1_bfq, xsii_td1_hfq, xsii_td1_qfq, xsii_td2_bfq, xsii_td2_hfq, xsii_td2_qfq, xsii_td3_bfq, xsii_td3_hfq, xsii_td3_qfq, xsii_td4_bfq, xsii_td4_hfq, xsii_td4_qfq

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolNo证券代码(带后缀),如 000001.SZ
end_dateNo结束日期 YYYYMMDD
start_dateNo起始日期 YYYYMMDD
trade_dateNo单个交易日 YYYYMMDD(与 start/end 二选一)

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

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It discloses the permission requirement (403 on lower tiers), the return format (JSON array), the full list of fields, and the mutual exclusivity between trade_date and start/end. It does not mention pagination, rate limits, or behavior with no parameters, but for a data retrieval tool, it covers the key operational facts.

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 structured with clear sections: a one-line purpose, a permission note, Args, and Returns. It is front-loaded and free of fluff. The extremely long field list is necessary for a data tool with many indicators, but it does make the description lengthy. Overall, it is appropriately organized and not redundant.

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?

For a complex data retrieval tool with hundreds of fields, the description provides the essential context: permissions, parameter formats, mutual exclusivity, and the full output field list. The output schema is present in the description itself. It lacks details like data frequency (implied daily by trade_date), error handling for invalid symbols, or date range limits, but these are not critical gaps given the richness of the rest of the description.

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 all four parameters already described in the input schema. The description's Args section repeats the same descriptions without adding new semantic information, except perhaps reinforcing the trade_date vs start/end exclusivity. Given the schema fully documents the parameters, the description adds marginal value beyond it.

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 clearly identifies the tool as a provider of technical factors (pro version) including adjusted prices and technical indicators, and labels it as market data. This distinguishes it from the sibling 'technical_factors' tool. However, it lacks an explicit action verb (e.g., 'get', 'retrieve'), relying on the noun phrase '技术面因子' to imply data retrieval.

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 states the prerequisite that Expert or higher tier is required and that lower tiers get a 403 error, which gives usage context. It also notes that trade_date is mutually exclusive with start/end dates. However, it does not explicitly compare this tool to siblings like 'technical_factors' or 'daily' or state when to prefer this over those alternatives. Usage is implied rather than directly guided.

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.

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