MCP server

Moltline Research Desk

com.moltlinestudio/research

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

7 research navigation, thesis, note-taking and citation skill products. 6 of 8 free.

Working at last checkVerified publisher: moltlinestudio.comAbout 2,817 tokens per request once connected8 tools

Website Source

https://mcp.moltlinestudio.com/research

At a glance

Type: MCP server
Publisher: moltlinestudio.com (verified by the MCP Registry)
Status: Working at last check
Tools: 8
Token cost: About 2,817 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http research https://mcp.moltlinestudio.com/research

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.research]
url = "https://mcp.moltlinestudio.com/research"

Gemini CLI

{"mcpServers":{"research":{"httpUrl":"https://mcp.moltlinestudio.com/research"}}}

Cursor

{"mcpServers":{"research":{"url":"https://mcp.moltlinestudio.com/research"}}}

VS Code

{"servers":{"research":{"type":"http","url":"https://mcp.moltlinestudio.com/research"}}}

Antigravity

agy mcp add research https://mcp.moltlinestudio.com/research

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"research","server_url":"https://mcp.moltlinestudio.com/research","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"research","server_url":"https://mcp.moltlinestudio.com/research"}

Tools

confidence_interval ~378 tokens
Compute a confidence interval for a mean (normal approximation). FREE. Typical input {"mean": 72.4, "std_dev": 8.1, "n": 64, "confidence_pct": 95} returns {"mean": 72.4, "margin_of_error": 1.9845, "interval": [70.4155, 74.3845], "note": "..."}. Use on data already collected, for a mean. Normal app
format_citation ~512 tokens
Format a citation in APA 7, MLA 9, or Chicago author-date style. FREE. Typical input {"style": "apa", "authors": ["Curie, Marie"], "year": 1911, "title": "Radium and radioactivity", "container": "Century Magazine"} returns {"style": "apa", "citation": "Curie, M. (1911). Radium and radioactivity. Ce
get_free_skill ~317 tokens
Load a product's free gateway skill with its complete instructions. FREE. Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "skill": "<skill name>", "instructions": "<full skill text>"}. Returns exactly one skill - the product's free gateway skill - chosen automatically from the slug,
get_full_product ~307 tokens
Load one product in full: its persona plus every paid skill. PREMIUM (license). Typical input {"slug": "thesis-advisor"} returns {"slug": ..., "name": ..., "persona": "<persona text>", "skills": [{"name": ..., "instructions": ...}, ...], "free_skill": {...}}. Returns persona plus every skill for o
get_full_skill ~331 tokens
Load one paid skill's complete instructions from a product. PREMIUM (license). Typical input {"slug": "thesis-advisor", "skill_name": "Outline Builder"} returns {"slug": ..., "skill": ..., "instructions": "<full skill text>"}. Returns one named skill, selected by skill_name. Use when the caller wa
list_products ~298 tokens
List every product in the Research Desk line with its included skills. FREE. Takes no arguments. Returns a list of 7 product objects, each {"slug": "thesis-advisor", "name": ..., "tagline": ..., "skills": ["Skill A", ...], "free_skill": "Gateway Skill Name"}. Use the returned slug values with get_f
sample_size ~380 tokens
Calculate the survey sample size needed for a confidence level and margin. FREE. Uses maximum variance (p=0.5) with a finite-population correction when population is given. Typical input {"population": 5000, "confidence_pct": 95, "margin_pct": 5} returns {"required_sample": 357, "assumptions": "p=0
stats_describe ~294 tokens
Describe a numeric dataset: center, spread, quartiles, and outliers. FREE. Typical input {"numbers": [12, 15, 14, 90, 13]} returns {"n": 5, "mean": 28.8, "median": 14.0, "std_dev": ..., "min": 12, "max": 90, "q1": ..., "q3": ..., "iqr_outliers": [90], "skew": "right (mean > median)"}. Use as a fir

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