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Junemind

june-mcp

Official
by Junemind

june_answer

Answer factual questions from a shared knowledge graph, grounded in evidence with citations. Abstains when the graph lacks knowledge.

Instructions

Answer a question from June's shared knowledge graph — grounded in stored evidence, with citations, and it abstains rather than guessing when the graph doesn't know. Use when you want a finished answer to a factual question about remembered knowledge (people, projects, documents, decisions); use june_context instead when you want raw material to reason over yourself, and june_search when you only need ranked matching items. May take longer than other tools (it runs one LLM synthesis). Returns {answer, citations, used_edge_ids, degraded, mode}; an empty answer or 'abstain' in degraded means the graph has no grounded answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
seedsNo
multihopNodecompose multi-hop questions
max_itemsNo
token_budgetNo
Behavior4/5

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

Given no annotations, description discloses key behaviors: abstains rather than guessing, uses citations, runs an LLM synthesis, returns specific fields, and explains empty/abstain meaning. Lacks explicit read-only indication but is largely transparent.

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 well-structured: purpose first, then usage guidance, performance note, and return format. Slightly lengthy but each sentence adds value. Could tighten parameter mentions.

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?

Covers purpose, usage, return format, and behavioral traits adequately. However, lacks parameter explanations for the five undocumented parameters, which is a gap given no output schema and low schema coverage.

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 coverage is only 17% (only multihop has description). The tool description does not explain the other five parameters (limit, seeds, max_items, token_budget, query beyond its obvious role). Insufficient compensation for 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?

Description clearly states it answers factual questions from a knowledge graph with citations and abstains when unknown. It explicitly differentiates from sibling tools june_context and june_search, making purpose distinct.

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 guidance on when to use this tool (for finished answers) and when to use alternatives (june_context for raw material, june_search for ranked items). Also mentions it may take longer.

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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