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ontopoc

ontopoc

by Longado
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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data_layout

Profiles uploaded tables to report row counts, skipped headers, and column types, lengths, and nulls, while identifying join columns when tables are not connected.

Instructions

上传的每张表：行数、跳过的标题行、每列类型长度空值；表没连上时，能把它们连起来的列。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "saved_as": "<saved_as>"
    },
    "name": "data_layout"
  }
}

Input Schema

NameRequiredDescriptionDefault
saved_asYes运行的保存名，来自 list_runs 或 build_ontology

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full disclosure burden. It helpfully reveals the substance of the report (per-table stats plus join-column suggestions), but says nothing about read-only behavior, response format, size limits, or performance.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is short and dense with no wasted words, but it is phrased as one telegraphic, semicolon-linked clause written in Chinese while the rest of the environment is English. It conveys content efficiently but lacks a clear lead sentence stating purpose.

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 low-complexity, single-parameter read tool with no output schema, the description adequately conveys what is reported. However, it omits when-to-use guidance and any return-format cues, leaving gaps for an agent deciding between this and other inspection tools.

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?

There is a single parameter, saved_as, and schema description coverage is 100%, with the schema already explaining it comes from list_runs or build_ontology. The description adds nothing about this parameter, so the baseline 3 for full schema coverage applies.

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 concretely lists what the tool surfaces for each uploaded table: row count, skipped header rows, per-column type/length/null info, and candidate join columns when tables aren't linked. This makes the resource and content clear, though it reads as an output inventory rather than a verb+resource statement and does not distinguish it from siblings like data_check or preview_upload.

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?

There is no explicit guidance on when to invoke this tool, what question it answers, or how it relates to alternatives such as data_check or preview_upload. Usage is only inferable from the content it reports.

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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