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Junemind

june-mcp

Official
by Junemind

june_context

Retrieve ranked evidence from a knowledge graph, merged by canonical entity and trimmed to a token budget, for use in custom reasoning or drafting.

Instructions

One call → a ready-to-use context pack: ranked evidence folded to canonical entities (aliases merged), trimmed to a token budget. Use when you want June's knowledge as raw material inside YOUR reasoning or a long draft; use june_answer when you want June to produce the answer itself. Returns {items[], budget, …} sized to token_budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
queryYes
seedsNo
max_itemsNo
token_budgetNo
Behavior4/5

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

Despite no annotations, the description discloses key behaviors: ranking, entity folding, token budgeting, and output structure. Does not mention side effects or error handling, but for a read-style tool this is acceptable.

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?

Two sentences pack the core purpose, usage guidance, and output format with no unnecessary words. Front-loaded with key details.

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?

With 5 parameters, no output schema, and no annotations, the description is too sparse. It omits parameter explanations and full output structure details, making it incomplete for reliable invocation.

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 0%, so description must explain parameters, but only token_budget is indirectly referenced. Other parameters (limit, seeds, max_items) are completely unexplained, leaving the agent to guess from names.

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 produces a context pack from a query, with ranked evidence and canonical entities. It distinguishes from sibling tool june_answer by specifying use cases: raw material for reasoning vs. answer generation.

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

Usage Guidelines4/5

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

Explicitly contrasts with june_answer for when to use this tool vs. when to delegate to the answer. Lacks mention of other siblings or conditions where not to use, but the provided guidance is specific and actionable.

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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