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---
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 six Sciverse tools (search_papers, semantic_search, list_catalog, list_paper_relations, read_content, get_resource) via the sciverse-mcp-server MCP server.
---
# 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:
```bash
npm install -g sciverse-mcp-server
```
Or add it to your project `.mcp.json`:
```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:
```bash
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 `` 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 `` 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 
└─▶ 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`.