zzet--gortex/bench/daemon-latency

Daemon-mode MCP-tool latency

Per-tool p50 / p95 / p99 latency for the production MCP dispatch path. Builds an in-process MCP server against a target corpus, fires N Handler.CallToolStrict invocations per tool, aggregates latencies into a published table.

What it measures

• Handler-end-to-end latency for each MCP tool: JSON arg parse → tool dispatch → handler logic → response encode. Same code path the production stdio / HTTP / daemon-socket front-ends use.
• Per-tool spread: cheap tools (graph_stats, get_callers) separate from heavy ones (smart_context, get_repo_outline) so the published table shows realistic operating envelope.

What it does NOT measure

• Stdio framing (gortex mcp's pipe overhead)
• Daemon socket dispatch (gortex daemon's UNIX socket / HTTP ingress overhead)
• Network RTT (if reaching the daemon remotely)

Each adds a roughly constant ~0.1-1 ms per call on a warm pipe; the handler latency below dominates user-perceived response time.

Running

# Default: index `.` and fire 200 iters per tool
go run ./bench/daemon-latency

# Higher iter count for tighter percentiles
go run ./bench/daemon-latency -iter 500

# Specific subset of tools (useful for tuning one signal)
go run ./bench/daemon-latency -tools graph_stats,search_symbols

# CSV / JSON outputs for downstream tooling
go run ./bench/daemon-latency -csv bench/results/dl.csv -json bench/results/dl.json

Flags:

• -repo PATH — corpus to index (default .)
• -iter N — iterations per tool (default 200; warm-up of N/10 is added on top)
• -tools LIST — comma-separated subset
• -out PATH — primary output (default stdout)
• -csv PATH / -json PATH — companion outputs
• -format markdown|csv|json — primary format

Or via the CLI surface:

gortex bench daemon-latency --out-dir bench/results

Tools benchmarked

tool	shape
graph_stats	no-arg snapshot; cheap
search_symbols	1 query arg; rotated through 10 fixtures so a per-query cache doesn't trivially hit
get_symbol_source	1 id arg; pinned to a sampled function from the indexed graph
get_callers	1 id arg + limit
find_usages	1 id arg
get_file_summary	1 path arg; pinned to a sampled file
smart_context	1 task arg; expensive, fewer iters per cycle
get_repo_outline	no-arg; walks whole graph

Sampled targets are picked once at start so each tool sees the same target across iterations — the per-call latency reflects handler arithmetic, not target lookup.

Methodology

• Warm-up of iter/10 (min 5) per tool primes any lazy initialisation in the handler / graph before the measured loop starts.
• Per-iteration latency captured via time.Since(start) with μs precision.
• Percentiles computed via the nearest-rank method: idx = (pct × n) / 100. For N=200 → p95=sorted[190].
• Errors are counted in error_rate but their latencies are still measured (an error path that takes 3× the happy-path time is itself a signal).

Honest caveats

• Numbers are operator-machine-specific. Absolute values vary 2-5× across hardware classes; the relative spread between tools (cheap vs heavy) is what publishes reproducibly.

• Cold-cache effects show up most in search_symbols (BM25 re-ranks under load) and smart_context (assembles fresh context each call). Warm-up reduces but doesn't eliminate them.

• Smoke run on the gortex repo (71k nodes, Apple M3 Max):

  • graph_stats p50 4.2ms · p95 5.5ms
  • search_symbols p50 1.2ms · p95 22.4ms
  • get_symbol_source p50 0.19ms · p95 0.9ms
  • get_callers / find_usages p50 < 0.02ms (graph lookup)
  • smart_context p50 1.5ms · p95 24ms
  • get_repo_outline p50 60ms · p95 217ms

  Median p95 across tools: 5.5 ms. Median p99: 5.9 ms.