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extract_graph_missing

Finds documents without mentions and runs extraction to build knowledge graph entries for missing items.

Instructions

批量补跑未抽取的文档

找出没有 mentions 的文档,并对它们执行 extract_graph_v1。

Args: limit_docs: 最大处理文档数,默认 50 llm_model: LLM 模型,默认使用环境变量 LLM_MODEL 配置 min_confidence: 最小置信度阈值

Returns: 处理的文档数和文档 ID 列表

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limit_docsNo
llm_modelNo
min_confidenceNo
concurrencyNo
doc_concurrencyNo
max_chunksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations provided; the description lacks behavioral details such as whether the operation is destructive, idempotent, or has rate limits. Concurrency parameters are present but unexplained.

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 concise and includes an Args and Returns section, but some text is dense and could be better organized.

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?

Given no annotations and incomplete parameter coverage, the description provides adequate context for basic usage but lacks depth on behavior and edge cases.

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?

The description explains three of six parameters (limit_docs, llm_model, min_confidence) but omits concurrency, doc_concurrency, and max_chunks, leaving gaps in understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: batch rerun extraction on documents missing mentions, distinguishing it from sibling extract_graph_v1 by focusing on missing documents.

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 implies usage when documents lack mentions but does not provide explicit conditions for use or alternatives, relying on context from sibling tool.

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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