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missing_report

Identify missing values in tabular datasets, generating per-column counts, rates, and co-occurrence patterns to support data quality assessment and cleaning.

Instructions

missing_report —— 数据探查组 · 缺失报告(工具 3,核心实现)。

docstring = agent 使用说明书,与 statlab_mcp/docs/design/01_data_exploration_batch1.md 同步维护。

参数: file_path (str): 本地数据文件(csv/tsv/xlsx/json),仅接受本地路径(拒绝 UNC)

返回: 成功 {"status":"ok","result":{...},"summary":"一句话中文结论"} 失败 {"status":"error","message":"中文原因"} result: {n_rows, n_columns, total_missing, overall_missing_rate, columns: {<列名>: {n_missing, missing_rate}}, complete_rows, rows_with_missing, patterns: [{columns, rows, note}]}

缺失定义: 空单元格与空串在读表层统一为 NaN(read_csv 默认),一律计入缺失; 全缺失列 rate=1.0 并注记;成对模式 = 两列同时缺失的行数(同源故障信号); patterns 最多 10 条,按缺失行数降序;无缺失时 patterns=[]。

示例: missing_report("samples/dirty.csv") inline 数据: 本工具支持可选 inline_data 参数(v1.2.0 起):与 file_path 二选一, 支持 records 数组或 {"header": [...], "rows": [[...], ...]} 对象两种形态; 规模上限/类型域/data_source 来源标注见 statlab_mcp/docs/SPEC.md 第 12 节。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathNo
inline_dataNo

Schema Changelog

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

  1. First observedv1.2.0

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and succeeds. It discloses the success/error envelope, defines missing as empty cells/strings converted to NaN, specifies all-missing columns rate=1.0 with a note, explains pairwise missing patterns, caps patterns at 10 sorted descending, and sets patterns=[] when no missing values exist.

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 long but well-organized into parameters, return format, missing definitions, example, and inline-data notes. It loses a point for the meta-maintenance sentence ('docstring = agent 使用说明书...') which is not actionable for an agent, though the core content is front-loaded and non-redundant.

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

Completeness5/5

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

Despite lacking an output schema and annotations, the description is self-sufficient. It provides input modes, constraints, a full text-based result schema, error response shape, and edge-case rules, giving an agent everything needed to invoke the tool and interpret results.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate fully. It enriches file_path with supported formats (csv/tsv/xlsx/json) and the local-path-only constraint, and inline_data with two accepted shapes (records array or header/rows object) plus the mutual-exclusivity rule. This goes far beyond the bare type-only schema.

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 missing-value report ('缺失报告') and details the output structure with counts, rates, and patterns. It does not explicitly call out sibling alternatives, so it falls just short of full differentiation, but the purpose is unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers parameter-level constraints such as '仅接受本地路径(拒绝 UNC)' and inline_data being mutually exclusive with file_path ('二选一'), but it never states when to choose this tool over sibling tools like impute_missing. No use-case scenarios or alternative recommendations are provided.

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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