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Manage and query an entity-relationship knowledge graph for structured facts. Search modes include hybrid, semantic, and keyword to discover relationships between concepts, tools, and patterns.

Instructions

Entity-relationship graph for structured facts. Use for entities with relationships - concepts, tools, people, patterns. Check here before semantic_search. For free-form text/learnings, use memory instead. Hybrid search (70% semantic + 30% keyword). Limits: 10K entities, 100 obs/entity. Audit log for destructive ops. Actions: create_entities, create_relations, add_observations, delete_entities, delete_relations, search, neighbors, stats, read, prune, remove_observation, update_entity, rename_entity, merge_entities, batch. IMPORTANT: Always specify entity_type when creating entities - omitting it defaults to 'unknown' which pollutes the graph. Knowledge graph is stored as JSONL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoSearch mode: hybrid (default, 70% semantic + 30% keyword), semantic (embeddings only), keyword (substring only)
depthNo
limitNo
namesNo
queryNo
actionYes
targetNoEntity to merge into (will be kept)
dry_runNoPrune: if true, just report candidates (default true)
updatesNoFields to update: entity_type, description, aliases, parent_type
entitiesNo
new_nameNoNew entity name for rename_entity
old_nameNoCurrent entity name for rename_entity
relationsNo
operationsNoList of operations for batch action. Each op needs 'op' key: {op: 'add_observations', entity_name: 'X', observations: [...]}, {op: 'create_relations', relations: [{from, to, relation_type}]}, {op: 'create_entities', entities: [...]}, {op: 'delete_entities', names: [...]}
preprocessNoExpand synonyms in search query (default True)
entity_nameNo
observationNoSingle observation text for remove_observation
max_age_daysNoPrune: only entities older than this (default 30)
observationsNo
source_entityNoEntity to merge from (will be deleted)
min_similarityNoMinimum semantic similarity threshold (default 0.25)
min_connectionsNoPrune: only entities with <= this many connections (default 0)
Behavior4/5

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

With no annotations, description carries full burden and delivers substantive behavioral context: limits (10K entities, 100 obs/entity), audit log for destructive ops, storage format (JSONL), default inference behavior for entity_type, and hybrid search weighting. Lacks detail on specific action side effects, but substantial coverage.

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?

Description is dense but justified given 15 actions and multiple behavioral notes. Structured logically: purpose, usage, search mode, limits, actions, critical warning. No filler or redundancy despite length.

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 high-complexity tool with 22 params, 15 actions, nested objects, and no output schema, the description covers purpose, usage, limits, audit, storage, search behavior, and a key input pitfall. Lacks per-action behavior and return formats, but provides a strong operational overview.

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 59%, so this is moderate. The description adds critical semantics absent from schema by warning that omitting entity_type defaults to 'unknown', which pollutes the graph. Also mentions hybrid search weights and batch operation patterns (partially in schema). Adds value beyond structured definitions.

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?

Description clearly defines the tool as an 'Entity-relationship graph for structured facts' and states it's for entities with relationships, listing example domains. It distinguishes from siblings by explicitly contrasting with memory ('free-form text/learnings') and positioning it before semantic_search, making purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance: 'Use for entities with relationships - concepts, tools, people, patterns. Check here before semantic_search. For free-form text/learnings, use memory instead.' This directly addresses alternatives and search precedence.

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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