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canonicalize_relations_v1

Normalize relations and merge duplicates in academic literature knowledge graphs, preserving all evidence. Supports scoped processing and predicate filtering.

Instructions

规范化关系并合并重复项,保留所有证据。

Args: scope: 处理范围,"all", "doc_id:...", "comm_id:..." predicate_whitelist: 只处理这些谓词 qualifier_keys_keep: 规范化时保留哪些 qualifier 字段 dry_run: 仅计算建议,不写入数据库

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoall
predicate_whitelistNo
qualifier_keys_keepNo
dry_runNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the dry_run mode (no writes) implying normal mode writes to the database, and states that duplicates are merged with evidence preserved. However, it does not detail the normalization process or potential side effects beyond writing.

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

Conciseness5/5

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

The description is extremely concise: a single sentence stating purpose followed by 4 parameter lines. No redundant information, and the purpose is front-loaded.

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?

While the description covers key parameters and basic behavior, it lacks usage guidance compared to siblings (e.g., no mention of relation normalization vs entity normalization). It does not explain the output schema or what 'normalize' entails, though an output schema exists. Given the presence of a sibling tool with similar purpose, completeness could be improved.

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 0%, and the description provides concise yet meaningful explanations for all 4 parameters: scope (all/doc/comm), predicate_whitelist (filter by predicates), qualifier_keys_keep (retain qualifiers), and dry_run (simulate only). This adds necessary context beyond the schema's type and default values.

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 normalizes relations and merges duplicates, preserving evidence. The verb 'canonicalize' and resource 'relations' are explicit, differentiating it from sibling tool canonicalize_entities_v1.

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 explains the scope and filter parameters, which guide when to use (e.g., for a specific document or community). However, it does not explicitly state when to use this tool versus alternatives like canonicalize_entities_v1 or merge_entities.

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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