Chartlibrary MCP Server
chartlibrary-mcp
PyPI
v6.3.3
Published by grahammccain — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.
Cohort intelligence engine for stock chart patterns. Anchor any (symbol, date, timeframe) and your AI agent gets the comp set of historical analogs, the full calibrated forward-return distribution, and the drivers that separated winners from losers. Flagship tool pull_comps plus 14 canonical tools and a full-cohort handover surface; the core loop is search → pull_comps → cohort_introspect. 25M+ patterns, 19K+ symbols, 10 years. Validated 50–0 in a blind paired AI-agent evaluation.
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 4 = 96. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −3 | capability blast radius (moderate) — client exposure if the model is manipulated |
| −1 | publisher verification (public source) — no provenance, but the source is public and inspectable |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 0
Tools 26
Each tool and what it can reach — statically extracted from the published source.
contextingests untrusted inputanalyzeno sensitive capabilitycalibration_benchmarkno sensitive capabilitycohortno sensitive capabilitycohort_attributionno sensitive capabilitycohort_groupbyno sensitive capabilitycohort_introspectno sensitive capabilitycohort_membersno sensitive capabilitycohort_rerankno sensitive capabilitydaily_noteno sensitive capability
Show 16 more tools ↓
explainno sensitive capabilitymarket_briefingno sensitive capabilitymarket_stateno sensitive capabilitymicro_compsno sensitive capabilityportfoliono sensitive capabilitypull_compsno sensitive capabilityread_researchno sensitive capabilityreplayno sensitive capabilityreport_feedbackno sensitive capabilityresearch_qualityno sensitive capabilitysearchno sensitive capabilitysearch_researchno sensitive capabilitysize_positionno sensitive capabilitystate_packetno sensitive capabilitysymbol_intelligenceno sensitive capabilityvol_premiumno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (26 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 4
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v6.3.3 latest |
A 96/100 | 0 | 1.14.0 | 2026-09-20 |
v6.3.1 |
A 96/100 | 0 | 1.13.0 | 2026-09-10 |
v6.2.0 |
A 96/100 | 0 | 1.13.0 | 2026-09-07 |
v6.1.0 |
A 96/100 | 0 | 1.13.0 | 2026-08-25 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
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