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SPARKIT-science

mcp-sparkit

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by SPARKIT-science
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Python
Remote

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 when research returned 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.)

Related MCP server: pubmed-search-mcp

Get an API key

  1. Sign up at https://app.sparkit.science/signup (Try-it costs $10 for 5 queries; subscriptions start at $50/mo).

  2. Visit https://app.sparkit.science/keys and create a key.

  3. 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.

Available Tools

2 tools
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.

ParametersJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes return outcomes (completed report, running status, failure message). No annotations, but behavior is well-covered. Lacks explicit statement of non-destructiveness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded, each sentence adds value. Structured into purpose, usage, argument, returns. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all necessary aspects for a simple tool: usage, parameter, return behavior. Output schema exists, so description suffices.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description explains job_id as 'The id returned by a prior `research` call', adding meaning beyond schema's title 'Job Id'. Schema coverage 0%, so description compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states 'Fetch the current status (and result if done) of a SPARKIT job', specifying verb and resource. Distinguishes from sibling 'research' by context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this when `research` returned before the job finished, or to revisit a previous result by id', providing clear when-to-use and alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
questionYes
response_formatNofull
include_citationsNo
max_wait_secondsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description discloses key behaviors: async execution with timeout (max_wait_seconds), return types (inline report vs job_id), and parameter defaults. Could add rate limits or error handling, but overall thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured: concise opening, contextual paragraph, bullet-like Args section, and return value explanation. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers input, usage, return types, and sibling relationship. Missing explicit error scenarios, but output schema likely covers that. Overall very complete for a complex async tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining each parameter in detail: question specificity, response_format options, include_citations rationale, and max_wait_seconds range and purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool submits a scientific question to the SPARKIT research agent, which searches literature and returns a cited Markdown report. It distinguishes from sibling 'get_job_status' by describing async behavior and polling instructions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Best for questions where a correct answer requires synthesizing across multiple primary sources.' Provides context on when to use, and mentions alternative polling via get_job_status.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv0.1.0
    • First observedget_job_status
    • First observedresearch
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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

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