SuperAPI AI Directory
Partner中文AI newsFavorites
Skillssciverse

sciverse

Writing & research

View repository

Updated 10-16 03:10

About this skill

Sciverse — Academic Paper Retrieval

Retrieval skill for the Sciverse open platform. Exposes six tools for working with scientific literature: field introspection, structured metadata search, semantic chunk retrieval for RAG, citation / reference pagination, character-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 text 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 six 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).

list_paper_relations

Paginate the full relation list of a paper. citations/references/related_works are unbounded arrays (up to 340k entries for a single paper) and are NOT projectable in search_papers, so this endpoint is the only way to read them. Use when: "What does paper X cite?" (relation=REFERENCES), "Which papers cite paper X?" (relation=CITATIONS), "Works related to paper X" (relation=RELATED_WORKS). Note: CITATIONS (incoming: who cites me) and REFERENCES (outgoing: who I cite) are opposite directions. Typical chain: get unique_id from search_papers / semantic_search, then paginate here by relation. Two limits (CITATIONS only; REFERENCES/RELATED_WORKS max out at 11833/20 in practice): more than 10000 relations returns 429; page*page_size above 10000 returns 400. In both cases switch to search_papers with filters_advanced on references_unique_id — it supports deep paging and arbitrary sorting. total_count counts in-corpus matches only, so it can differ from the paper's own citation_count by about 1%.

read_content

Read a range of a paper's original text addressed in Unicode code points (offset/limit count characters like Python len(), not bytes). Typically used with a doc_id/offset returned by semantic_search to expand context (read more text before or after a chunk). Returns: text fragment, bytes_returned (UTF-8 byte length of text, for reference only), next_offset (code-point offset of the next fragment — page with it, never with bytes_returned), more (boolean). Server behaviour: limit above 524288 is silently clamped; omitting offset returns the whole document ignoring limit — the SDKs / MCP server send offset=0 and limit=4096 by default, so pass offset explicitly when calling the HTTP API directly.

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. Scoped semantic search (constrained corpus):

semantic_search(
    query="attention",
    filters={"author": ["Hinton"],
             "publication_published_year": {"gte": 2020}}
)
    # filters apply at recall time (server-side), AND across fields;
    # soft semantics: chunks missing that metadata are NOT excluded

For a hard guarantee, or meta-only constraints (fwci, citation graph, complex hit-sets), scope by doc_id — a HARD recall-time filter:

search_papers(..., fields=["doc_id","title"])
    └─▶ collect hits[].doc_id (only fulltext papers carry one)
semantic_search(query="attention", filters={"doc_id": [...]})
    # hits never leave the set; empty list → empty hits (never global);
    # up to 1000 deduped ids (400 SCOPE_TOO_LARGE beyond)

6. Bias fuzzy search ranking (soft boosts — stackable):

Three multiplicative boosts reorder fuzzy-search results while keeping relevance. Only effective when query is non-empty; ignored when any sort is set; shallow paging while active (no next_cursor). sort_by_year defaults to auto: relevance when query is set, newest-first for pure filters. Never combine query with sort_by_year=desc expecting "relevant and recent" — explicit sort degrades the query to a match filter (OR, no ranking) and disables all boosts; use freshness_boost instead.

search_papers(query="large language model", freshness_boost="STRONG")
    # recent first: STRONG=3-year decay, MILD=10-year
search_papers(query="protein folding", impact_boost="MILD")
    # highly-cited float up (bounded; zero-citation stays neutral)
search_papers(query="深度学习", language_affinity="MILD")
    # demote (never exclude) results not in the query's language;
    # target detected from query text (kana→ja/hangul→ko/Han→zh/Latin→en);
    # unknown-language papers stay neutral. Hard-exclude instead:
    # filters_advanced=[{"field":"language","value":"zh"}]
search_papers(query="...", freshness_boost="MILD", impact_boost="MILD",
              language_affinity="MILD")   # stack: relevant+recent+cited+same-lang

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

8. Search authors or journals (collection):

Set collection to authors or sources (default papers) to search those entities instead of papers. Each collection has its own fields — call list_catalog(collection="authors") first. Use filters_advanced + sort_advanced; the papers convenience fields (authors/year_from/...) apply to papers only.

# Top authors by h-index, sorted by citations
search_papers(
    collection="authors",
    filters_advanced=[{field: "summary_stats.h_index", operator: "FILTER_OP_GTE", value: 50}],
    sort_advanced=[{field: "cited_by_count", order: "SORT_ORDER_DESC"}]
)
# Enrich a paper result: take an author orcid / venue issn, then look up the entity
search_papers(collection="authors", filters_advanced=[{field: "orcid", value: "https://orcid.org/..."}])

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.
  • Before reading a paper's fulltext: check is_content_accessible on the search_papers hit — true means the paper has fulltext AND you're authorized, so read_content(doc_id, ...) will work; false means no fulltext or no permission.
  • When a chunk looks promising but truncated: read_content(doc_id, offset) to expand. read_content returns more: true when more text is available; offset/limit are Unicode code points (not bytes) — page with next_offset.
  • Pagination: semantic_search top_k allows up to 100, but balanced truncates to ~50 server-side — use quality (or fast) when you need more. At most ~3 chunks come back per paper, so a high top_k needs many distinct papers to be matched. search_papers returns max 50 per page; use page.