mcp-sparkit
science agent for literature search and summary
README
sparkit-mcp
MCP server for SPARKIT — call the scientific research agent from Claude Desktop, Cursor, Claude Code, or any other MCP-compatible client.
Two tools are exposed:
research— submit a scientific question. SPARKIT searches the literature, reads the relevant papers, and returns a cited Markdown report. Blocks until the job finishes (default 4 min) and returns the full report inline.get_job_status— fetch a previously-submitted job by id. Useful whenresearchreturned before the job finished, or to revisit a past report.
Install
uv tool install sparkit-mcp
Or with pip:
pip install sparkit-mcp
Either installs a sparkit-mcp console script. (Pre-release: install
straight from GitHub with
uv tool install "git+https://github.com/SPARKIT-science/sparkit-mcp.git"
until the first PyPI release lands.)
Get an API key
- Sign up at https://app.sparkit.science/signup (Try-it costs $10 for 5 queries; subscriptions start at $50/mo).
- Visit https://app.sparkit.science/keys and create a key.
- Copy the key — it's shown only once.
Configure your MCP client
Claude Desktop
Edit claude_desktop_config.json:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
Add:
{
"mcpServers": {
"sparkit": {
"command": "sparkit-mcp",
"env": {
"SPARKIT_API_KEY": "sk_sparkit_..."
}
}
}
}
Restart Claude Desktop. You should see sparkit appear in the tools
icon next to the chat input.
If sparkit-mcp isn't on Claude Desktop's PATH (common with uv tool),
use the absolute path:
"command": "/Users/you/.local/bin/sparkit-mcp"
(Find the path with which sparkit-mcp after uv tool install.)
Cursor
Edit ~/.cursor/mcp.json (or .cursor/mcp.json in your project):
{
"mcpServers": {
"sparkit": {
"command": "sparkit-mcp",
"env": {
"SPARKIT_API_KEY": "sk_sparkit_..."
}
}
}
}
Reload Cursor (Cmd+Shift+P → "Reload Window").
Claude Code
claude mcp add sparkit -e SPARKIT_API_KEY=sk_sparkit_... -- sparkit-mcp
Try it
Once configured, ask the LLM:
Use SPARKIT to look up the most recent literature on the role of WRNIP1 as a synthetic-lethal target in cancer.
The LLM will call research. Expect a 60-180s wait, then a Markdown
report with inline citations and a numbered Sources list.
Configuration
| Env var | Default | Description |
|---|---|---|
SPARKIT_API_KEY |
(required) | Bearer key from https://app.sparkit.science/keys. |
SPARKIT_API_BASE |
https://jlsteenwyk--sparkit-api-web.modal.run |
Override the API base URL. Useful for staging or self-hosted deployments. |
SPARKIT_API_TIMEOUT_SECONDS |
30 |
Per-HTTP-request timeout. Doesn't affect total wait time for research; that's max_wait_seconds. |
Tool reference
research(question, response_format?, include_citations?, max_wait_seconds?)
| Arg | Type | Default | Description |
|---|---|---|---|
question |
string | — | The scientific question. Required. Be specific. |
response_format |
"full" or "brief" |
"full" |
Length of the returned Markdown report. |
include_citations |
boolean | true |
Keep true for sourced reports. |
max_wait_seconds |
int (30-540) | 240 |
How long to block waiting before returning the job_id with instructions to poll. |
Returns Markdown. On timeout, returns a status line with the job_id so
the LLM can call get_job_status later.
get_job_status(job_id)
Returns the cited Markdown report if the job has completed, a status line if it's still running, or an error message otherwise.
Troubleshooting
Authentication failed — SPARKIT_API_KEY isn't set or is invalid.
Check claude_desktop_config.json for typos; restart Claude Desktop
after edits.
Quota exhausted — out of monthly queries / Try-it credits. Visit https://app.sparkit.science/billing.
Tool isn't appearing in Claude Desktop — check the Claude Desktop log:
- macOS:
~/Library/Logs/Claude/mcp-server-sparkit.log - Windows:
%LOCALAPPDATA%\Claude\Logs\mcp-server-sparkit.log
The most common issue is command: sparkit-mcp not being on PATH;
substitute the absolute path from which sparkit-mcp.
Job times out — max_wait_seconds cap is 540s (9 min). For very
deep questions, submit then poll get_job_status instead of waiting
inline. SPARKIT will also auto-cancel jobs that exceed its own
internal limit.
License
MIT.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。