Eeg MCP vs Context7
A side-by-side look at the Eeg MCP and Context7 MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Eeg MCP for developer tools work as a local process; choose Context7 for developer tools as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Eeg MCP when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
list_boards, start_stream, stop_stream.
Choose Context7 when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You want tools like
resolve-library-id, query-docs.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 56/100 · Good | 65/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | API key required |
| Pricing | Not declared | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 2 | 63,150 |
| Directory activity | 2 views 0 installs 0 upvotes | 9 views 0 installs 0 upvotes |
| Tools | 47 | 2 |
| Full listing | View Eeg MCP | View Context7 |
Tools & capabilities
Eeg MCP tools 47
list_boardsList every board BrainFlow can acquire from, with ids and sampling rates. Start here when you do not know what hardware is available. Two entries need no hardware at all: ``SYNTHETIC_BOARD`` generates a signal for development, and ``PLAYBACK_FILE_BOARD`` replays a BrainFlow CSV.start_streamOpen a board and begin buffering EEG in the background. Data flows into a ring buffer from a background thread as soon as this returns, so a subsequent read_window will have samples waiting. The session keeps running until stop_stream is called.stop_streamStop a stream and release the board, serial ports, and LSL outlets. Always call this when finished. A board left prepared can block the next process from acquiring the device.list_sessionsList all active stream and replay sessions with a one-line summary each.stream_statusHealth and timing report for a session. The fields that matter for closed-loop work: ``throughput_ratio`` below 1.0 means the producer is not keeping up and samples are being dropped; ``filters.group_delay_sec`` is how far behind real time a filtered feature is; ``last_error`` explains a stopped producer.read_windowRead the most recent samples from the buffer. Returns filtered signal when a filter chain is set (see set_filters), otherwise raw. Long windows are decimated to ``max_samples`` per channel so the response stays readable -- for numeric work prefer get_band_power or get_psd, which compute on the full-resolution data.get_channelsList the channels a session is producing, with their buffer index.inspect_recordingOpen a recording and describe it without starting playback. Use this before start_replay to see how long the file is, what channels it has, and which annotations will be re-emitted as events during replay.start_replayReplay a recording against a wall clock, re-emitting its events. This is the safe way to build a real-time pipeline: the session behaves exactly like a live stream -- same buffering, same chunked delivery, same tools -- so code developed here runs unchanged against hardware. The recording's annotations arrive in the event log at their original timings.replay_controlPause, resume, seek, or change the speed of a replay session.list_recording_eventsList all events present in a replay session's source file. Distinct from get_events: this is the *complete* event list from the file, including events that have not been reached yet. get_events returns only what has actually been emitted so far.set_filtersInstall a stateful online filter chain on a running stream. The chain filters continuously in the background as samples arrive, carrying filter state across chunk boundaries. This matters: filtering each query window independently would inject a transient at every boundary and corrupt any phase-sensitive measure. Filtered output goes to a second buffer, so raw signal remains available via read_window(filtered=False). These are causal filters -- there is no zero-phase option, because that needs future samples. Check ``group_delay_sec`` in the response to know how far behind real time your filtered features are.- +35 more on the listing page
Context7 tools 2
resolve-library-idResolves a general library name into a Context7-compatible library ID.query-docsRetrieves documentation for a library using a Context7-compatible library ID.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Eeg MCP
{
"mcpServers": {
"eeg-mcp": {
"command": "uvx",
"args": [
"eeg-mcp"
]
}
}
}
Context7
{
"mcpServers": {
"upstash-context7": {
"command": "npx",
"args": [
"-y",
"ctx7"
]
}
}
}
Frequently asked questions
Eeg MCP is categorized under Developer Tools and uses a local stdio subprocess. Context7 belongs to Developer Tools and uses a local stdio subprocess. Pick Eeg MCP for developer tools work and Context7 for developer tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Eeg MCP has no declared pricing — check the project's own site. Context7 is listed as free. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add eeg-mcp and upstash-context7 as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Eeg MCP has a quality signal of 56/100 (Good) with 2 views, 0 installs and 2 GitHub stars. Context7 has 65/100 (Great) with 9 views, 0 installs and 63,150 GitHub stars.