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Sciverse-Agent-Tools MCP Server

opendatalab/Sciverse-Agent-Tools

Score: 56.4 Rank #8260 AI Tool
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Standardized tool schemas and SDKs that expose Sciverse Open Platform retrieval capabilities to LLM agents.

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Overview

opendatalab/Sciverse-Agent-Tools is a Python agent tool. Standardized tool schemas and SDKs that expose Sciverse Open Platform retrieval capabilities to LLM agents.

Ranked #8260 out of 24846 indexed tools.

Ecosystem

Python NOASSERTION

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 121

121 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → moderate activity

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 4

4 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads N/A
Forks 6
Description Good
License NOASSERTION

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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License low impact

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Freshness high impact

Last commit was 52 days ago — a recent commit would boost your freshness score

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Matched Queries

"agent tool"

From the README

# Sciverse Agent Tools

**English** | [简体中文](./README.zh-CN.md)

**Sciverse Agent Tools** provides standardized tool schemas and SDKs that expose the [Sciverse Open Platform](https://sciverse.space) academic retrieval capabilities to LLM agents. 

With these tools, you can easily empower your AI agents to search for academic papers, perform natural language semantic retrieval (RAG), and fetch original literature contents and multimodal resources (like figures and tables).

| Tool | Use case |
|---|---|
| `list_catalog` | Discover available fields, filter operators, and enum sample values (per `collection`) |
| `search_papers` | Structured metadata search over papers / authors / sources (set `collection`) |
| `semantic_search` | Natural-language semantic search over passages (RAG) |
| `list_paper_relations` | Paginate a paper's full citations / references / related works (by `unique_id`) |
| `read_content` | Fetch a character-range slice of the source document (offsets in Unicode code p
Read full README on GitHub →

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