mcp-sparkit
Officialsparkit-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-mcpOr with pip:
pip install sparkit-mcpEither 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.)
Related MCP server: scholar-memory
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.jsonWindows:
%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-mcpTry 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 |
| (required) | Bearer key from https://app.sparkit.science/keys. |
|
| Override the API base URL. Useful for staging or self-hosted deployments. |
|
| Per-HTTP-request timeout. Doesn't affect total wait time for |
Tool reference
research(question, response_format?, include_citations?, max_wait_seconds?)
Arg | Type | Default | Description |
| string | — | The scientific question. Required. Be specific. |
|
|
| Length of the returned Markdown report. |
| boolean |
| Keep |
| int (30-540) |
| 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.logWindows:
%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.
Available Tools
2 toolsget_job_statusA
Fetch the current status (and result if done) of a SPARKIT job.
Use this when research returned before the job finished, or to
revisit a previous result by id.
Args:
job_id: The id returned by a prior research call.
Returns the cited Markdown report if the job has completed, a status line if it's still running, or a failure message otherwise.
Inputs: job_id (required). Returns: result
researchA
Submit a scientific question to the SPARKIT research agent.
SPARKIT searches the literature, reads relevant papers, and returns a cited Markdown report. Best for questions where a correct answer requires synthesizing across multiple primary sources.
Args:
question: Free-text scientific question. Be specific —
"Which kinases are upregulated in pancreatic cancer with
evidence from human tissue?" works better than "tell me
about pancreatic cancer."
response_format: "full" (default) for a multi-paragraph
Markdown report, or "brief" for a tighter summary.
include_citations: Keep True (default) so the report is
usable for downstream work; only set False if you
specifically want unsourced prose.
max_wait_seconds: How long to block waiting for the job before
returning the job_id with instructions to poll via
get_job_status. Default 240s (4 min). Range 30-540.
Returns the cited Markdown report on success. If the job is still
running at the wait limit, returns the job_id and status so the
caller can resume with get_job_status.
Inputs: question (required), response_format, include_citations, max_wait_seconds. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'research' submits a scientific question and returns either a report or a job ID, while 'get_job_status' retrieves the status or result of a previously submitted job. There is no overlap in functionality.
Both tool names use snake_case, but 'research' is a single-word noun while 'get_job_status' follows a verb_noun pattern. This minor inconsistency prevents a perfect score.
With only two tools, the server covers the essential workflow of submitting a research job and checking its status. While minimal, the count is appropriate for the narrow scope of a scientific research agent.
The tool set covers the primary use case (submit and retrieve results), but lacks features like job listing, cancellation, or retry. For a simple agent this may suffice, but there are notable gaps in lifecycle management.
Maintenance
Related MCP Connectors
Open scientific and engineering knowledge for AI agents: search, evidence, document publishing.
Research papers for AI agents: PubMed, arXiv, Crossref; citations, references, open access texts
Academic papers, reference verification and research data for AI agents.
Search peer-reviewed papers and research methodology guidance from your AI agent.
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