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add_entity

Adds a named entity to the knowledge graph with optional type and metadata, returning the created entity's ID.

Instructions

Add an entity to the knowledge graph.

Args: name: Entity name (e.g., "MyClass", "Authentication", "Paris"). type: Entity type (class, function, concept, person, place, etc.). metadata: Optional metadata dict.

Returns: Created entity details including entity_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
typeNoconcept
metadataNo
Behavior2/5

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

No annotations are provided, so the description must fully convey behavioral traits. It indicates mutation ('Add') and mentions a return value, but does not disclose idempotency, uniqueness constraints, or error scenarios. This leaves significant ambiguity for an agent.

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 concise with a single introductory sentence followed by a clear list of parameters. Every sentence serves a purpose, and the structure is easy to parse.

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 simple tool with no output schema and no annotations, the description covers the basic functionality and parameters. However, it omits the default value for 'type' (concept) and does not elaborate on the return structure beyond 'entity details'. It is adequate but not fully comprehensive.

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?

The description includes an Args section that explains the purpose and acceptable values for each parameter, providing examples for 'name' and clarifying 'type' and 'metadata' meaning. This adds value beyond the schema which only has titles and defaults. With 0% schema description coverage, the description compensates well.

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 adds an entity to the knowledge graph, specifying the verb 'Add' and the resource 'entity to the knowledge graph'. It is distinct from sibling tools like add_relation and add_tag.

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 does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusion criteria. It is minimally adequate but lacks contextual direction.

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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