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danielsimonjr

Enhanced Knowledge Graph Memory Server

search_nodes

Find nodes in a knowledge graph using search queries with optional filtering by tags and importance scores to locate relevant information efficiently.

Instructions

Search for nodes in the knowledge graph based on query string, with optional tag and importance filtering

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query
tagsNoOptional array of tags to filter by
minImportanceNoOptional minimum importance score (0-10)
maxImportanceNoOptional maximum importance score (0-10)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions optional filtering but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, result format, or whether it's read-only/destructive. For a search tool with no annotation coverage, this leaves significant gaps.

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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core functionality without unnecessary elaboration.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a search tool with no annotations, no output schema, and many sibling alternatives, the description is incomplete. It lacks behavioral context, usage differentiation, and output expectations, making it inadequate for reliable agent operation.

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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'optional tag and importance filtering', which aligns with but doesn't expand beyond the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 clearly states the verb ('search') and resource ('nodes in the knowledge graph'), making the purpose immediately understandable. However, it doesn't explicitly distinguish this tool from similar siblings like 'search_nodes_ranked', 'fuzzy_search', or 'semantic_search', which would require a 5.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling search tools (e.g., search_nodes_ranked, fuzzy_search, semantic_search), the lack of differentiation leaves the agent without clear usage context.

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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