MCP server
Whatic IC Datasheets Official
io.whatic/datasheets
Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools
IC datasheet search: parametric part finding, spec lookup, price comparison — grounded in real PDFs.
Working at last checkVerified publisher: whatic.ioAbout 2,314 tokens per request once connected7 tools
https://whatic.io/mcp
At a glance
- Type
- MCP server
- Publisher
- whatic.io (verified by the MCP Registry)
- Status
- Working at last check
- Tools
- 7
- Token cost
- About 2,314 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
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 datasheets https://whatic.io/mcp
Claude Desktop
Settings → Connectors → Add custom connector, and paste the URL.
Codex (config.toml)
[mcp_servers.datasheets] url = "https://whatic.io/mcp"
Gemini CLI
{"mcpServers":{"datasheets":{"httpUrl":"https://whatic.io/mcp"}}}
Cursor
{"mcpServers":{"datasheets":{"url":"https://whatic.io/mcp"}}}
VS Code
{"servers":{"datasheets":{"type":"http","url":"https://whatic.io/mcp"}}}
Antigravity
agy mcp add datasheets https://whatic.io/mcp
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":"datasheets","server_url":"https://whatic.io/mcp","require_approval":"always"}
Grok (xAI API tools)
{"type":"mcp","server_label":"datasheets","server_url":"https://whatic.io/mcp"}
Tools
compare_parts~130 tokens
Relative price tier, stock posture, and library class for a set of IC part numbers (a ranking from a distributor snapshot, not a live quote).find_parts~1057 tokens
Shortlist IC part numbers by parametric spec constraints, ranked by how many constraints each part satisfies, then by headroom past those thresholds, and spread across device families. Each constraint is {canonical, op: gte|lte|eq|range, value, unit?}. Returns per-constraint pass/fail/unknown, plusget_image~203 tokens
Fetch datasheet figure images (PNG/JPEG) for opaque `ref` tokens obtained from search/lookup/get_segments. Returns each image plus its caption/description/page metadata. Only `type='image'` segments have image data; a ref that is not one comes back as an error record whose `available_images` lists tget_segments~113 tokens
Expand opaque `ref` tokens (from search/lookup) into full datasheet segment content.get_specs~302 tokens
Read canonical extracted specs (min/typ/max, unit, conditions) for known IC part numbers from a pre-extracted parametric index — not from datasheet text. This is the cheapest and most precise source for numeric parameters where it has coverage. Optionally restrict to specific canonical parameter namlookup~203 tokens
One-shot spec/section retrieval: full content of the most relevant datasheet segments for one or more parts (fuses search+get_segments). Already groups results per part (via `parts`), so prefer `lookup` over `search` when you know the part(s).search~306 tokens
Search the datasheet corpus; returns hit records (metadata + snippet, each with an opaque `ref`). Pass a ref list to `get_segments` for full content. If you already know the part number(s), prefer `lookup` — it fuses this search with `get_segments` in one call and groups full content per part. Use `
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