sciverse

name	sciverse
description	Use when the user needs academic paper retrieval — searching scientific literature by author/year/journal, finding paper chunks for RAG-style citations, or expanding original text around a known paper offset. Provides three Sciverse tools (search_papers, semantic_search, read_content) via the sciverse-mcp-server MCP server.

Sciverse — Academic Paper Retrieval

Retrieval skill for the Sciverse open platform. Exposes five tools for working with scientific literature: field introspection, structured metadata search, semantic chunk retrieval for RAG, byte-range content reading, and figure / table image fetching.

When to use

Trigger this skill when the user's request involves any of:

• Locating academic papers by structured criteria (authors, year, journal, subjects)
• Grounding an answer in paper excerpts (RAG / citations)
• Expanding the original text around a known doc_id (more bytes before/after a chunk)

Do NOT use this skill for general web search, news, or non-scientific content — the underlying index only covers peer-reviewed and preprint scientific literature.

Prerequisites

This skill is a thin wrapper around the sciverse-mcp-server MCP server. Before invoking any tool, ensure the server is reachable:

1. Install the MCP server:

   npm install -g sciverse-mcp-server
   

   Or add it to your project .mcp.json:

   {
     "mcpServers": {
       "sciverse": {
         "command": "npx",
         "args": ["-y", "sciverse-mcp-server"],
         "env": { "SCIVERSE_API_TOKEN": "${SCIVERSE_API_TOKEN}" }
       }
     }
   }
   

2. Obtain an API token from https://sciverse.space and export it:

   export SCIVERSE_API_TOKEN=sv-...
   

Optional: set SCIVERSE_BASE_URL to override the default API base URL (for dev / self-hosted gateways; must remain on *.sciverse.space).

Tools

All five tools are exposed by the MCP server. Claude Code will surface them automatically when this skill is active.

search_papers

Search academic papers by structured filters (title, authors, journal, year, subjects, etc.). Use when: "find Hinton's papers from 2020-2023", "Nature papers on CRISPR". Not for: natural-language Q&A retrieval (use semantic_search) or full-text snippets (use read_content). Returns: list of papers; each entry has unique_id (always present), doc_id (only when full text exists), title, author, abstract, publication_venue_name_unified, publication_published_year.

semantic_search

Natural-language semantic search returning relevant paper chunks for RAG-style answering. Use when: "How does Transformer attention work?", "What are recent methods for protein structure prediction?". Not for: precise field filtering (use search_papers) or fetching full original text (use read_content). Returns: list of chunks; each entry has chunk_id, doc_id, abstract, chunk, score, title, offset. Typical chain: semantic_search → pick chunk → read_content(doc_id, offset).

list_catalog

Returns the schema catalog for search_papers: every field name, type, whether it's filterable / sortable, default-return status, human description, and applicable FilterOperators. Use when: "Which field do I filter by DOI?", "What values can access_oa_status take?", "What's the right enum for metadata_type?". Not for: actually searching papers (use search_papers / semantic_search). Typical pattern: call once when first encountering Sciverse or facing an ambiguous field need, then construct precise search_papers filters from the returned schema. Pass include_sample_values=true to also fetch top-20 values for enum-like fields (OpenSearch terms aggregation, 24h cached).

read_content

Read a UTF-8 byte range of a paper's original text. Typically used with a doc_id/offset returned by semantic_search to expand context (read more bytes before or after a chunk). Returns: text fragment, bytes_returned, next_offset, more (boolean).

get_resource

Returns the binary bytes of a paper figure / table image referenced inside read_content's Markdown via ![alt](file_name) placeholders. Use when the user asks to see / display / describe a figure and read_content output contains an image reference. Input file_name comes from the Markdown URL part (relative path, no \\ or ..). Returns: raw image stream + image/* Content-Type. The SDK / MCP server wraps the bytes as base64 + mimeType so Claude (multimodal) can read the image directly.

Bootstrap: learn the schema first

If you don't yet know which fields exist or what values they take (e.g. "is oa_status a field?", "what does metadata_type accept?"), call list_catalog once at the start of the conversation. The result includes every field name, type, filterability, default-return status, and — for enum-like fields — sample values. Cache the catalog in your working memory; subsequent search_papers filters become precise instead of guessed.

list_catalog(include_sample_values=true)
    └─▶ fields[].name + sample_values  →  pick the right filter field

Recipes

1. Natural-language RAG (most common):

semantic_search(query="How does Transformer attention work?", top_k=5)
    └─▶ for each hit: read_content(doc_id, offset, limit=8192)
    └─▶ cite doc_id + title in the answer

2. Look up a paper by DOI / doc_id:

search_papers(filters_advanced=[
    {field: "doi", operator: "FILTER_OP_EQ", value: "10.1038/..."}
])

3. Find OA papers in a year range:

search_papers(
    filters_advanced=[
        {field: "access_is_oa", value: "true"},
        {field: "access_oa_status", operator: "FILTER_OP_IN",
         value: ["gold", "green", "hybrid"]}
    ],
    year_from=2024
)

4. Filter by language / metadata_type (enum fields):

# First check the enum: list_catalog(include_sample_values=true)
# Then filter precisely:
search_papers(
    query="transformer",
    filters_advanced=[
        {field: "language", value: "en"},
        {field: "metadata_type", value: "paper"}
    ]
)

5. Structured pre-filter + semantic refine (hybrid):

search_papers(authors=["Hinton"], year_from=2020, page_size=50)
    └─▶ collect hits[].doc_id list
semantic_search(query="attention", top_k=20)
    └─▶ filter hits to those whose doc_id appears in step-1 list

6. Bias fuzzy search toward recent work (freshness boost):

Set freshness_boost to weight results by publication date with gauss decay over publication_published_date. Only effective when query is non-empty; mutually exclusive with sort_by_year.

search_papers(query="large language model", freshness_boost="STRONG")
    # STRONG: 3-year decay, for tracking research directions
search_papers(query="protein folding", freshness_boost="MILD")
    # MILD:   10-year decay, for everyday literature search

7. Show a figure / image from the paper:

When read_content Markdown contains ![alt](file_name) placeholders and the user wants to see the figure (e.g. "show me Figure 3"), fetch the binary with get_resource. The MCP server wraps the bytes as a base64 image content block so Claude can read it directly.

read_content(doc_id, offset) → markdown with ![Figure 3](dt=xxx/p_yyy/f3.png)
    └─▶ get_resource(file_name="dt=xxx/p_yyy/f3.png")
    └─▶ Claude sees the image inline

Notes for Claude

• Always cite doc_id and title when surfacing paper-based facts.
• Prefer semantic_search for natural-language questions; only fall back to search_papers when the user provides structured criteria.
• When stuck on a field name: call list_catalog instead of guessing. Field name typos return 400 with a clear message, but waste a turn.
• When a chunk looks promising but truncated: read_content(doc_id, offset) to expand. read_content returns more: true when more bytes are available.
• Pagination: max 30 hits per semantic_search, max 50 per search_papers page; use page to paginate.