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Glama
SPARKIT-science

mcp-sparkit

Official
by SPARKIT-science
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
SPARKIT_API_KEYYesBearer key from https://app.sparkit.science/keys.
SPARKIT_API_BASENoOverride the API base URL. Useful for staging or self-hosted deployments.https://jlsteenwyk--sparkit-api-web.modal.run
SPARKIT_API_TIMEOUT_SECONDSNoPer-HTTP-request timeout. Doesn't affect total wait time for `research`; that's `max_wait_seconds`.30

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
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.

get_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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.5/5.0

Scored across 2 tools

Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count4/5

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.

Completeness3/5

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

ActivityInactive
ResponsivenessNo issues

Resources