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/superpyonchix/excel_mcp_server/generate-typescript-mcp-servergit clone --depth 1 https://github.com/SuperPyonchiX/excel_mcp_serverWrote 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/agents/superpyonchix/excel_mcp_server/generate-typescript-mcp-server)<a href="https://agentmods.dev/agents/superpyonchix/excel_mcp_server/generate-typescript-mcp-server"><img src="https://agentmods.dev/badge/agents/superpyonchix/excel_mcp_server/generate-typescript-mcp-server.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.04679 |
| Opus 5 | $0.00011 | $0.02339 |
| Sonnet 5 | $0.00004 | $0.00936 |
| Haiku 4.5 | $0.00002 | $0.00468 |
Grade A, and why
generate-typescript-mcp-server 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 4d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 4d ago First seen · 288 lines · 22 tokens per session scan A 8442845cafaa
generate-typescript-mcp-server is an agent published in the GitHub repository SuperPyonchiX/excel_mcp_server (0 stars, last pushed 7mo ago), with no licence file. It adds 22 tokens to every session and 4,679 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 2026-08-31.
Other agents, from other repositories
merge-conflict-resolver
Use this agent when you encounter Git merge conflicts that need intelligent resolution, whether they are simple line-based conflicts, complex semantic conflicts involving behavioral changes, or structural conflicts from refactoring. This agent should be used proactively when merge operations fail due to conflicts, or…
workflow-debugger
Use this agent when you need to debug Output SDK workflows in local development. Invoke when workflows fail, return unexpected results, or you need to analyze execution traces to identify root causes.
project-structure
Airbroke uses the Next.js App Router. Most feature code lives under app, components, lib, prisma, and tests.
praman-sap-planner-cli
SAP UI5 test planner via Playwright CLI. Token-efficient alternative to MCP planner. Generates test plan + gold-standard spec using CLI commands.
agent-name
Agent name must be lowercase-with-hyphens, under 64 characters, with no XML tags.
forge
FORGE — Software engineering. Production code, tests, refactors, bug fixes after the cause is known. Use when the design and architecture are set and the job is to make it real. Use proactively for implementation once Axis/Form have spoken.