neurograph
Related Servers
Alternatives to neurograph
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityBmaintenanceEnables AI clients to access virome datasets and external bioinformatics APIs through MCP tools, including Wikipedia, PubMed, NCBI Taxonomy, read-only SQL over S3 Parquet, pandas/Plotly analyses, and map visualizations, while keeping the client decoupled from data and business logic.-
- AlicenseNot gradedqualityBmaintenanceExposes a shared graph-based context engine as MCP tools for Claude, Copilot, and other AI agents, enabling knowledge ingestion, recall, search, and LLM-ready context assembly across sessions.1Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables a connected LLM to inspect brain MRI slices as crops and montages with coordinates, compare a region against its mirrored hemisphere, and review the pipeline's candidate hotspots. It lets the model write prioritised "area to review" annotations that appear live in the browser viewer, and move or read the clinician's crosshair position.MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI tools to access and contribute to shared semantic memory, supporting search, capture, and curation of knowledge across multiple brains via the MCP protocol.-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to procedurally generate, transform, inspect, and export 3D meshes and scenes through MCP tools.1MIT

ScholarMuse MCPofficial
FlicenseNot gradedqualityBmaintenanceConnects AI agents to a hosted remote MCP endpoint that searches academic papers, verifies references and retraction status, retrieves research datasets, grants, and biological or chemical data, and generates scientific figures across 50+ upstream sources. Supports OAuth sign-in with per-tool credit accounting, structured error reporting, and source attribution for results.-
TDQS
Scored across 12 tools
Most tools have clearly distinct purposes, but a few close pairs exist: render_brain vs render_network both visualize atlas connectivity, and calculate_laplacian vs calculate_spectrum both operate on graph spectra. The descriptions clarify the different output modalities well enough to avoid serious misselection.
All tool names follow a consistent verb_noun snake_case pattern: search_region, get_connectivity, render_network, find_homologues, propose_dataset_ingestion. There are no mixed conventions or vague generic verbs.
Twelve tools is well within the ideal range and each tool covers a distinct aspect of the domain: exploration, connectivity, spectral analysis, visualization, species comparison, and dataset ingestion. The count feels appropriately scoped for a specialized neuroimaging server.
The toolset covers the core workflows well: searching regions and tracts, computing connectivity and paths, spectral analysis, rendering visualizations, comparing species, and proposing dataset ingestion. Minor gaps exist, such as no direct list_atlases or list_species tool, but these can be worked around via search_region and find_homologues.