localsend-mcp

LocalSend MCP Skill

Use This Skill When

• The user mentions LocalSend, AirDrop-like transfer, or peer-to-peer file sharing.
• The task asks for an MCP server or tool set around LocalSend.
• Discovery/advertising needs to use NATS or Tailscale before transferring data.

Reality Check (LocalSend in This Repo)

• localsend (Flox package) launches a GUI and does not exit for --help.
• Discovery uses UDP multicast 224.0.0.167:53317 (LAN only).
• Transfer runs HTTPS on port 53317; direct IPs are required across subnets.
• For headless automation, prefer a CLI client (e.g., jocalsend) or a small protocol wrapper.

Architecture: Advertise -> Negotiate -> Transfer -> Tune

1. Advertise capabilities over NATS (or Tailscale if LAN multicast is blocked).
2. Negotiate transport and parameters (LAN multicast vs direct IP).
3. Transfer via LocalSend protocol/CLI.
4. Tune throughput until spectral gap <= 0.25 (>= 75% of target throughput).

MCP Tool Set (Draft)

Discovery / Advertising

• localsend_advertise:
  • Inputs: agent_id, device_name, localsend_port, tailscale_ip?, capabilities, spectral_gap_target
• localsend_list_peers:
  • Inputs: source = localsend_multicast | nats | tailscale

Session Negotiation

• localsend_negotiate:
  • Inputs: peer_id, preferred_transport, max_chunk_bytes, max_parallel
  • Output: session_id, transport, target_ip, port

Transfer

• localsend_send:
  • Inputs: session_id, file_path, chunk_bytes, parallelism
• localsend_receive:
  • Inputs: session_id, dest_dir, accept

Throughput Tuning

• localsend_probe:
  • Inputs: session_id, probe_bytes, probe_parallelism
  • Output: throughput_bps, rtt_ms, loss_rate
• localsend_session_status:
  • Inputs: session_id
  • Output: bytes_sent, bytes_received, throughput_bps, spectral_gap

Spectral Gap Heuristic (Practical)

Define:

spectral_gap = 1.0 - (observed_throughput / target_throughput)

Stop tuning when spectral_gap <= 0.25.

Tuning Loop:

1. Start chunk_bytes = 256KB, parallelism = 1
2. Increase parallelism to 2, 4, 8 while loss < 1%
3. Increase chunk size up to 1MB while RTT stable
4. Recompute spectral gap each step

Integration Points in This Repo

• NATS broadcast helpers: lib/synadia_broadcast.rb
• Tailscale patterns: lib/tailscale_file_transfer_skill.rb
• MCP server reference: mcp_unified_server.py

GitHub GraphQL (GH CLI) Reference

Use gh api graphql for contributor snapshots (limit: history(first: 100)):

gh api graphql \
  -F owner=localsend \
  -F name=localsend \
  -F history=100 \
  -f query='query($owner:String!,$name:String!,$history:Int!){
    repository(owner:$owner,name:$name){
      defaultBranchRef{name target{
        ... on Commit{
          history(first:$history){
            nodes{author{user{login} name}}
          }
        }
      }}
    }
  }'

Aggregate top contributors:

gh api graphql -F owner=localsend -F name=localsend -F history=100 -f query='query($owner:String!,$name:String!,$history:Int!){repository(owner:$owner,name:$name){defaultBranchRef{name target{... on Commit{history(first:$history){nodes{author{user{login} name}}}}}}}}' \
  | jq -r '.data.repository.defaultBranchRef.target.history.nodes[].author | if .user then .user.login else .name end' \
  | sort | uniq -c | sort -nr | head -20

Duck Lake Snapshots (Feedback + Bidirectional Flow)

Persist LocalSend sessions and GitHub snapshots into DuckDB for time travel:

CREATE TABLE IF NOT EXISTS localsend_sessions (
  session_id TEXT,
  peer_id TEXT,
  direction TEXT, -- send|receive
  bytes BIGINT,
  throughput_bps DOUBLE,
  spectral_gap DOUBLE,
  created_at TIMESTAMP
);

CREATE TABLE IF NOT EXISTS localsend_contributors_snapshot (
  snapshot_at TIMESTAMP,
  repo TEXT,
  contributor TEXT,
  commit_count INT
);

Store snapshots per run and query later with existing DuckDB time-travel commands.

Implementation Notes

• Avoid assuming LocalSend has a stable CLI; verify with jocalsend --help if installed.
• If multicast discovery fails (Tailscale), use NATS to exchange target_ip + port.
• Keep tool outputs structured; avoid dumping large blobs through MCP.

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Graph Theory

• networkx [○] via bicomodule
  • Universal graph hub

Bibliography References

• general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.