HypePaper

library_get_paper_content

Your personal library: Fetch a paper's title, abstract, GitHub stats, AND its full deep-analysis markdown (the per-paper structured writeup with method, findings, numbers, limitations). Call this after search_nodes returns a Paper hit when you need to quote specific numbers, methods, or limitations from the paper rather than just the abstract. Returns the user's preferred-language version of the analysis (falls back to English). [Free · 1 credit]

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "paper_id": "<paper_id>"
    },
    "name": "library_get_paper_content"
  }
}

Input Schema

Name	Required	Description
`language`	No	Optional BCP-47 language override (e.g. 'ko', 'en'). Defaults to the user's preferred language with English as fallback.
`paper_id`	Yes	Either the papers.id UUID OR a kg_node id pointing at a Paper node (e.g. the `id` field from a `search_nodes` Paper hit, or the `source_paper.id` enriched payload).
`max_chars`	No	Truncate the deep-analysis body to this many characters to bound context cost. Lower it when calling for multiple papers at once.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description carries the burden and does reasonably well: it discloses the composition of the returned payload, the preferred-language resolution with English fallback, and the truncation behavior that bounds context cost. It omits any statement about access/auth scope for a 'personal library' read, which is the main remaining gap.

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?

Front-loaded with the payload summary, then the call-when rule, then the language caveat — a sensible ordering for a dense tool. The closing '[Free · 1 credit]' tag is internally odd (free vs. one credit) and adds noise to an otherwise efficient block.

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?

No output schema and no annotations exist, so the description has to describe the return payload — and it largely does, enumerating title, abstract, GitHub stats, and the analysis body plus its language fallback. A note on response shape/size beyond max_chars truncation is the only meaningful omission for this complexity level.

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 coverage is 100%, so all three parameters are already fully documented, making 3 the baseline. The description echoes the language-fallback and truncation concepts and hints at where paper_id originates (a search_nodes Paper hit), but adds no syntax or format detail beyond the schema's own text.

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?

Specific verb ('Fetch') plus an enumerated resource list — title, abstract, GitHub stats, and the deep-analysis markdown writeup (method, findings, numbers, limitations). It also scopes itself ('Your personal library') and distinguishes the payload from an abstract-only fetch, so an agent can tell it apart from get_paper or paper_fulltext without opening a schema.

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?

Gives an explicit trigger — 'Call this after search_nodes returns a Paper hit' — and names the condition that selects it: needing to quote numbers, methods, or limitations rather than just the abstract. It stops short of naming or excluding the concrete sibling alternatives (e.g. get_paper, paper_fulltext), so there is clear context but no explicit when-not guidance.

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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