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ontopoc

ontopoc

by Longado
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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list_relations

List ontology relations with their endpoints, meaning, source table, cardinality, and human judgments; by default omit incorrect relations, or include them when needed.

Instructions

本体里的关系：两端、含义、来源表、基数、人的判断。cardinality 是 one_to_one / one_to_many / many_to_one / many_to_many，方向是 from 到 to；reads 用两端对象的名字把它读成一句中文，most_from 是一个 from 对象最多对应几个 to，most_to 是一个 to 对象最多对应几个 from。判错的、或一端对象判错的默认不给，列在 left_out 里；include_wrong=true 全给。

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": "list_relations"
  }
}

Input Schema

NameRequiredDescriptionDefault
saved_asYes运行的保存名，来自 list_runs 或 build_ontology
include_wrongNo连人判错的也给（默认不给）

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations supplied, the description carries the behavioral burden and does disclose a key non-obvious trait: by default, relations judged wrong (or with a wrong end object) are excluded and placed in left_out, while include_wrong=true returns everything. It also explains what reads, most_from, and most_to mean. It stops short of stating read-only/non-destructive semantics, but the filtering disclosure is substantial.

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 a single dense paragraph but every clause carries information about return fields or filtering behavior. It front-loads the resource and field list before explaining cardinality, direction, and exclusion semantics. It could be split for readability, but no sentence is wasted.

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 two-parameter list tool with no output schema, the description is largely complete: it explains the relation record's fields, cardinality values, direction, and the default exclusion behavior. The saved_as origin is left to the schema, which already covers it, so no critical gap remains.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents saved_as and include_wrong. The description still adds meaning beyond the schema by defining what 'wrong' means (wrong judgment or wrong end object) and where excluded records appear (left_out), which clarifies the effect of include_wrong=true.

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 resource clearly: relations in an ontology, with the fields each relation carries (two ends, meaning, source table, cardinality, human judgment). It does not explicitly say 'list all relations' or distinguish itself from suggest_relations, but the enumerated content makes the tool's output scope 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?

There is no explicit when-to-use guidance, no mention of prerequisites (e.g., needing a saved run from list_runs/build_ontology), and no comparison to siblings such as suggest_relations. The only usage-adjacent information is the default filtering behavior, which is a parameter detail rather than tool-selection guidance.

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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