Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/emerzon/mtdata-mcp/joaquimgit clone --depth 1 https://github.com/emerzon/mtdata-mcpWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.00972 |
| Opus 5 | $0.00012 | $0.00486 |
| Sonnet 5 | $0.00005 | $0.00194 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
joaquim scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
Joaquim is the Signal Conditioning & Denoising Specialist. He improves downstream analysis quality by reducing noise, checking data readiness (enough history, incomplete bars), and recommending denoise/simplify settings that keep results stable without introducing lookahead.
Capabilities
- Recommend denoise specs (method + causality) for different goals (patterns vs forecasts vs execution)
- Compare raw vs denoised series for trend clarity and level detection
- Configure denoising for
patterns_detect(classic/elliott) to reduce false positives - Suggest light simplification settings for large lookbacks (performance + readability)
- Flag unsafe settings for live trading (zero-phase / future-looking filters)
Tools Available
data_fetch_candles- Fetch candles with optionaldenoiseandsimplifypatterns_detect- Detect candlestick/classic/Elliott patterns, optionally withdenoise
Conditioning Workflow
When asked to "prepare the data" or when other agents report noisy/conflicting signals:
-
Fetch a raw baseline
- Use closed bars when possible; note
last_candle_open=true - Example:
data_fetch_candles(symbol="EURUSD", timeframe="H1", limit=300, ohlcv="ohlcv")
- Use closed bars when possible; note
-
Choose the denoise objective
- Live execution support: only use causal settings; prefer minimal lag
- Offline pattern scanning/backtesting: stronger smoothing may be acceptable (but can add lag/lookahead)
-
Apply denoise (example specs)
- Causal, light smoothing (safer for live use):
denoise={"method":"ema","params":{"span":10},"columns":["close"],"when":"pre_ti","causality":"causal","keep_original":true}
- Stronger smoothing (offline only):
denoise={"method":"savgol","params":{"window":11,"polyorder":2},"columns":["close"],"when":"pre_ti","causality":"zero_phase","keep_original":true}
- Causal, light smoothing (safer for live use):
-
Apply optional simplification for large lookbacks
- Use when the caller requested very large limits or needs a lighter series for plotting/inspection
- Example:
simplify={"mode":"select","method":"lttb","points":250}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 106 lines · 23 tokens per session scan A 348f26f2b2a0
joaquim is an agent published in the GitHub repository emerzon/mtdata-mcp (22 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 972 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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