stagenth · Excel 数据可视化
Server Details
Excel analytics: inspect, query (JSON rows), charts, and JSON-to-xlsx workbook writing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.5/5 across 4 of 4 tools scored. Lowest: 3.9/5.
Each tool has a clearly distinct purpose: inspect structure, query data, create chart, and write Excel. No overlap or ambiguity.
All tool names follow the pattern 'excel_<verb>', using consistent snake_case with clear verbs: inspect, query, to_chart, write.
Four tools is well-scoped for an Excel data visualization server, covering the essential operations without redundancy.
The set covers inspection, querying, chart creation, and writing new Excel files. Missing update/delete for existing files, but the domain is visualization, not full spreadsheet editing.
Available Tools
4 toolsexcel_inspectAInspect
查看 Excel 结构：各 sheet 的列名/类型/行数 + 前 N 行预览。免费(0 credit)。
出图前先调它看清有哪些 sheet、列、哪些列是数值，再据此选 chart_type 与 x/y。
| Name | Required | Description | Default |
|---|---|---|---|
| file_id | No | 已上传到文件中转站的 Excel 文件 ID（与 data_base64 二选一） | |
| data_base64 | No | Excel(.xlsx) 内容的 base64（与 file_id 二选一，适合未上传的临时数据） | |
| preview_rows | No | 每个 sheet 预览的前 N 行 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description implies a read-only inspection tool (view structure, preview rows). It does not explicitly state safety or side effects, but the 'free' note is a cost hint. Could be more explicit, but sufficient for a simple inspection tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first states what the tool does, second provides usage guidance. No redundant words, front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and complete schema descriptions, the description covers the return values (sheet names, columns, types, row counts, preview) and usage context. Absence of output schema is mitigated by the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions for each parameter. The description adds context for file_id vs data_base64 (uploaded vs temporary) and preview_rows restrictions, but the schema already provides adequate meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it inspects Excel structure (column names, types, row counts) and previews rows, distinguishing it from the sibling tool excel_to_chart by advising to call it first before charting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to call this before generating charts to identify sheets, columns, and numerical columns for selecting chart_type and x/y. Also mentions it's free (0 credit), setting clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
excel_queryAInspect
查询 / 过滤 / 分组聚合 Excel，返回**实际数据行（JSON）**供 AI 直接分析（1 credit/次）。
取数工具，区别于 excel_to_chart（出图）和 excel_inspect（只看结构）。典型用法：
· 各地区销售额合计：group_by=["地区"], measures=["销售额"], agg="sum"
· 销量 Top5 商品：group_by=["商品"], measures=["销量"], agg="sum", sort_by="销量", descending=true, limit=5
· 每类别多少行：group_by=["类别"], agg="count"
· 筛失败订单明细：filters=[{"column":"状态","op":"eq","value":"失败"}]
返回 {ok, sheet, mode, columns, total_rows, matched_rows, returned_rows, truncated, rows[]}。
失败自动退款。
| Name | Required | Description | Default |
|---|---|---|---|
| agg | No | 聚合方式 sum/avg/count/min/max/median（默认 sum；count=数每组行数、无需 measures） | |
| limit | No | 最多返回多少行（取前 N / Top-N）；硬上限 1000 | |
| sheet | No | 目标工作表名；缺省第一个 sheet。先用 excel_inspect 看列名 | |
| columns | No | 明细模式：只返回这些列；缺省返回全部列 | |
| file_id | No | 已上传的 Excel 文件 ID（与 data_base64 二选一） | |
| filters | No | 行过滤条件（AND 组合），每项 {column, op, value}。op 可选：eq/ne/gt/ge/lt/le/contains/in/notnull/isnull。例：[{"column":"地区","op":"eq","value":"华东"},{"column":"金额","op":"ge","value":1000}] | |
| sort_by | No | 按哪一列排序（可为分组后的 measure 或 count 列） | |
| group_by | No | 分组维度列。给了就进聚合模式：按这些列分组，对 measures 求 agg | |
| measures | No | 聚合模式：要统计的数值列；缺省=所有数值列 | |
| descending | No | 降序排序（取 Top-N 常用 true） | |
| data_base64 | No | Excel(.xlsx) 内容 base64（与 file_id 二选一） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost (1 credit/次), return structure (ok, sheet, mode, columns, etc.), and auto-refund on failure. No annotations, but description provides sufficient behavioral context for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with core purpose, uses bullet points and examples. Every sentence serves a purpose; no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 11 parameters and no output schema, the description explains return format, two operational modes (detail and aggregated), and handles edge cases like auto-refund. Complete for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed parameter descriptions. The description adds value by showing how parameters combine in real use cases (e.g., group_by with measures, filters syntax).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it queries/filters/groups Excel and returns JSON data, distinguishing from siblings (excel_to_chart for charts, excel_inspect for structure).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly explains when to use this tool vs alternatives ('取数工具，区别于 excel_to_chart 和 excel_inspect') and provides typical usage examples covering different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
excel_to_chartAInspect
把 Excel 数据渲染成图表，产物存进你的文件中转站并返回下载 URL。
计费(生成类)：基础 3；高级图型 +2；矢量 SVG +1；大数据(>5000 行) +2。
调用失败自动退款。建议先 excel_inspect 看清列结构再出图。
返回: {ok, file_id, filename, format, credit_charged, download_url(15分钟有效),
chart_type, sheet, rows}
| Name | Required | Description | Default |
|---|---|---|---|
| x | No | X 轴/类别列名（饼图=标签列）；缺省取第一列 | |
| y | No | Y 轴数值列名，可单个或多个(数组)；缺省取所有数值列 | |
| agg | No | 聚合方式 count/sum/avg；缺省智能默认（分布类 饼/环→count 数每类多少条，其余→sum）。看「分布/各类别占比」用 count，无需 y（如性别男女各几人）；看「某数值在各类别的合计/平均」用 sum/avg 并给 y（如各地区销售额）。 | |
| fmt | No | 输出格式: png(默认) / svg(矢量, +1 credit) | png |
| sheet | No | 目标工作表名；缺省取第一个 sheet。先用 excel_inspect 查名字 | |
| title | No | 图表标题 | |
| series | No | 可选分组列：按它把 y 透视成多系列（分组/堆叠/多线） | |
| file_id | No | 已上传的 Excel 文件 ID（与 data_base64 二选一） | |
| options | No | 可选渲染项, 如 {"stacked":true,"bins":30,"cmap":"viridis","dpi":144} | |
| chart_type | Yes | 图表类型。标准型(基础3 credit): bar/column/line/pie/scatter/area/barh; 高级型(+2): stacked_bar/grouped_bar/radar/heatmap/histogram/box/donut。 | |
| data_base64 | No | Excel(.xlsx) 内容 base64（与 file_id 二选一） |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: billing based on chart type and format, auto-refund on failure, and download URL timeout of 15 minutes. The return format is also detailed, exceeding what annotations might cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, starting with the main purpose and using bullet points for billing and return info. Every sentence adds value, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (11 params, 1 required, no output schema), the description provides essential context: billing, workflow recommendation, return fields, and format details. It is complete enough for 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers all 11 parameters with descriptions, so the tool description does not add significant new parameter semantics. It only provides context like billing and return fields, which does not enhance parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: rendering Excel data into charts, saving the product to a file transfer station, and returning a download URL. It clearly distinguishes from the sibling tool 'excel_inspect' by focusing on chart generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using 'excel_inspect' first to understand column structure before charting, providing a clear recommendation for tool usage. It also includes billing details and refund policy, but lacks explicit when-not-to-use or alternatives beyond the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
excel_writeAInspect
把 JSON 数据写成 Excel 工作簿（.xlsx），落文件中转站返下载 URL（上限 20 万单元格）。
补上「只能读不能写」的缺口：AI 算完的结果一步落成正式 Excel 交付。失败自动退款。
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | 数据行：对象列表 [{列:值},...]，或行数组 [[v1,v2],...]（需配 columns） | |
| columns | No | 列名（rows 为行数组时必填；对象列表时忽略） | |
| filename | No | 输出文件名（可选，自动补 .xlsx） | |
| sheet_name | No | 工作表名 | Sheet1 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cell limit (200k cells) and auto refund on failure, but lacks details on auth requirements, overwrite behavior, or response format. Without annotations, more transparency would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with core function. The second sentence adds context but includes marketing language slightly exceeding strict necessity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers key aspects: core function, input parameters detailed in schema, cell limit, failure handling. No output schema, but return of download URL is mentioned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of parameters. The tool description adds no additional meaning beyond the schema, so baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool writes JSON data to Excel (.xlsx) and returns a download URL, with a cell limit of 200k. It distinguishes itself from read-only siblings by positioning as the write counterpart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage after computation to output as Excel, contrasting with read-only tools. However, it does not explicitly name alternatives or provide 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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