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 skills add mattmre/EVOKORE-MCP-PUBLIC --skill v3-mcp-optimizationgit clone --depth 1 https://github.com/mattmre/EVOKORE-MCP-PUBLICWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mattmre/evokore-mcp-public/v3-mcp-optimization)<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/v3-mcp-optimization"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/v3-mcp-optimization/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mattmre/evokore-mcp-public/v3-mcp-optimization"><img src="https://agentmods.dev/badge/skills/mattmre/evokore-mcp-public/v3-mcp-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00058 | $0.01939 |
| Opus 5 | $0.00029 | $0.00970 |
| Sonnet 5 | $0.00012 | $0.00388 |
| Haiku 4.5 | $0.00006 | $0.00194 |
Grade A, and why
v3-mcp-optimization 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 11d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
v3 MCP Optimization Skill
Catalog of six concrete optimization patterns for EVOKORE-MCP's ProxyManager in v3. Each pattern has a measured-or-expected gain, a trigger condition, a TypeScript snippet, and an anti-pattern note. Apply these only when a profiler or benchmark identifies the corresponding bottleneck — premature application of (3) or (5) can regress cold-start.
Trigger
Use this skill when:
- Tool dispatch shows up as hot in a flame graph (indicates pattern 1)
- Child server boot dominates session startup time (indicates patterns 4 and 6)
- Repeated tool-schema lookups allocate heavily (indicates patterns 2 and 5)
- Multi-tool workflows show N× round-trip overhead (indicates pattern 3)
Pattern Catalog
1. O(1) Hash-Map Tool Lookup
Replace the linear tools.find(t => t.name === name) scan with a Map<string, Tool> built at registration time. With ~300 tools across proxied servers, this moves dispatch from O(n) to O(1).
Expected gain: 20–60× speedup on dispatch for aggregators with 100+ tools.
// Before
const tool = this.tools.find(t => t.name === name); // O(n)
// After
private toolIndex: Map<string, Tool> = new Map();
registerTool(tool: Tool) {
this.toolIndex.set(tool.name, tool);
}
dispatch(name: string) {
const tool = this.toolIndex.get(name); // O(1)
if (!tool) throw new Error(`Unknown tool: ${name}`);
return tool;
}
2. 3-Tier Cache (L1 in-memory, L2 filesystem, L3 remote registry)
Skill and tool metadata flows through a three-level cache. L1 is a Map with LRU eviction; L2 is an on-disk JSON cache under ~/.evokore/cache/; L3 is the remote registry fetch. Hits cascade downward and fill upward.
Expected gain: cold-start ~40% faster on warm L2, ~95% faster on warm L1.
async getSkill(name: string): Promise<Skill> {
// L1
const hit1 = this.l1.get(name);
if (hit1 && !this.isStale(hit1)) return hit1;
// L2
const hit2 = await this.l2ReadJson(`skills/${name}.json`);
if (hit2 && !this.isStale(hit2)) {
this.l1.set(name, hit2);
return hit2;
}
// L3
const fresh = await this.registry.fetchSkill(name);
await this.l2WriteJson(`skills/${name}.json`, fresh);
this.l1.set(name, fresh);
return fresh;
}
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.
- 11d ago First seen · 192 lines · 58 tokens per session scan A dfaa6ff3126f
v3-mcp-optimization is a skill published in the GitHub repository mattmre/EVOKORE-MCP-PUBLIC (3 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 1,939 once invoked, about $0.0003 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 2026-08-31.
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