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.
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