Connection check
local install
neurarch-mcp
Reads a PyTorch .py, a Neurarch graph, or a HF repo; agents inspect, lint, verify and rank designs.
Tools
19
GitHub stars
1
Installs / wk
—
Licence
MIT
Transport
stdio
Last checked
never
Tools & capabilities
19 toolsRead from the published package source — this server runs locally, so there is no endpoint to query. Names are taken from the code, not observed at runtime, and descriptions are often absent.
add_connection
from source
architecture
from source
A library id from list_architectures, e.g. A library id from list_architectures, e.g.
compare_with_reference
from source
Put the current model next to a published one from the bundled library and explain the structural differences that matter. Put the current model next to a published one from the bundled library and explain the structural differences that matter.
delete_connection
from source
explain_finding
from source
What a lint rule or check_design finding means for this model, the evidence behind it, and the smallest edit that clears it. What a lint rule or check_design finding means for this model, the evidence behind it, and the smallest edit that clears it.
flops_by_block
from source
focus
from source
Optional: what to review for, e.g. Optional: what to review for, e.g.
get_layer
from source
get_model_summary
from source
list_blocks
from source
list_connections
from source
list_hyperparams
from source
mermaid_diagram
from source
param_count_by_block
from source
pre_train_checklist
from source
The checks worth running before spending GPU time: structural, design rules, cost, GPU fit, and what is still unknown about the graph. The checks worth running before spending GPU time: structural, design rules, cost, GPU fit, and what is still unknown about the graph.
review_design
from source
A structured design review of the current model: readiness, risks, parameter budget, and the edits worth making, every number from the tools. A structured design review of the current model: readiness, risks, parameter budget, and the edits worth making, every number from the tools.
rule
from source
The rule id or finding title, e.g. The rule id or finding title, e.g.
shrink_for_target
from source
Find the edits that bring the model under a parameter, memory, latency or GPU budget with the least damage, and rank the resulting variants. Find the edits that bring the model under a parameter, memory, latency or GPU budget with the least damage, and rank the resulting variants.
target
from source