Token Optimizer (Tjpatel16) MCP Server
token-optimizer-mcp
PyPI
v0.1.0
Published by tjpatel16 — 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.
High-performance MCP server for minimizing LLM token usage and API costs via structural code analysis and precision chunking
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 9
Each tool and what it can reach — statically extracted from the published source.
get_token_statsreads sensitive dataread_file_chunkreads sensitive dataclear_memoryno sensitive capabilityget_git_diffno sensitive capabilityget_memoryno sensitive capabilityreset_token_statsno sensitive capabilitysave_memoryno sensitive capabilitysearch_codebaseno sensitive capabilitysummarize_fileno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (9 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 1
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 |
|---|---|---|---|---|
v0.1.0 latest |
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.
npx mcptrustchecker scan token-optimizer-mcp --online --registry pypiOther implementations of Token Optimizer 4
Independent packages implementing the same tool, scanned with the same engine. Compare all 5 side by side →
MCP server that cuts LLM token costs: accurate token counting, cost estimates across GPT/Claude/Gemini, rule-based prompt slimming with measured savings, and cheapest-model recommendations. Works with Claude, Cursor, and any MCP client.
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