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Junemind

june-mcp

Official
by Junemind

june_search

Retrieves matching nodes and snippets from a knowledge graph by combining lexical, dense, and graph signals into a ranked list.

Instructions

Fused retrieval over the knowledge graph: lexical + dense + graph signals in one ranked list. Use when you need matching items (nodes/snippets with scores and provenance) — e.g. to find entities or check what the graph holds on a topic; use june_answer for a finished cited answer, june_context for a prompt-ready pack. Returns {items[], degraded_lanes, …}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
seedsNo
min_confidenceNo
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It describes the tool as performing fused retrieval with multiple signal types, returning items with scores and provenance, and mentions degraded lanes. Though it does not explicitly declare read-only or state side effects, the description provides sufficient behavioral context for a search-like operation.

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 at three sentences, front-loading the core functionality in the first sentence, followed by usage guidance and return structure. Every sentence adds value without redundancy.

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 output schema and no annotations, the description gives a reasonable overview and distinguishes from siblings, but lacks parameter explanations and detailed return field descriptions. The mention of degraded_lanes is vague. It is adequate but not complete for an agent to fully understand all aspects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning no parameter descriptions in the JSON schema. The tool description does not explain the individual parameters (limit, seeds, min_confidence) beyond stating the required query. The agent is left to infer meanings, which is insufficient given the low schema coverage.

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 performs fused retrieval over a knowledge graph combining lexical, dense, and graph signals into a ranked list. It explicitly distinguishes itself from siblings by specifying when to use june_answer or june_context instead, making the 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?

The description provides explicit guidance on when to use this tool ('use when you need matching items...e.g. to find entities or check what the graph holds on a topic') and when not to, with specific alternatives named (june_answer, june_context). This fully supports correct tool selection.

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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