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 nimadorostkar/Claude-Skills-collection --skill mcp-servergit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/mcp-server)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/mcp-server"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/mcp-server/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/nimadorostkar/claude-skills-collection/mcp-server"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/mcp-server.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.01521 |
| Opus 5 | $0.00020 | $0.00760 |
| Sonnet 5 | $0.00008 | $0.00304 |
| Haiku 4.5 | $0.00004 | $0.00152 |
Grade A, and why
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 12d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MCP Server
Purpose
Build an MCP server that a model can use correctly. The protocol is straightforward; the difficulty is designing a tool surface that a language model uses well, which is a different problem from designing an API for a programmer.
When to Use
- Exposing a system's capabilities to an LLM client.
- Wrapping an internal API for agent use.
- Improving an MCP server whose tools the model uses incorrectly.
Capabilities
- Tool design: granularity, naming, schema, and descriptions.
- Resources for read-only context.
- Prompts as reusable, parameterized templates.
- Transports: stdio for local, HTTP/SSE for remote.
- Authentication and authorization.
- Error returns that let a model recover.
Inputs
- The underlying system and its API.
- The tasks a model should be able to accomplish.
- The security boundary: what the model may do, and on whose behalf.
Outputs
- A server whose tool descriptions unambiguously state when each tool applies.
- Errors that are instructive rather than merely accurate.
- A test suite exercising the tools as a model would call them.
Workflow
- Design around tasks, not endpoints — Do not mirror your REST API one-to-one. A model needs
find_order_by_customer, notGET /orderswith fourteen optional query parameters. - Write the description as if it were the prompt — Because it is. State what the tool does, when to use it, when not to use it, and what it returns. This text determines whether the model uses the tool correctly.
- Constrain the inputs — Enums, not free strings. Required parameters, not optional ones the model must guess.
- Return errors a model can act on — What went wrong, and what to do instead. An error that names the valid values lets the model self-correct in one step.
- Keep responses compact — Return what the model needs. A tool returning a 40 KB JSON blob spends the model's context on noise.
- Test as a client — Not with unit tests on the handlers. Connect a real client and watch which tools the model picks and how it uses them. It will surprise you.
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.
- 12d ago First seen · 148 lines · 40 tokens per session scan A 47942eb12235
mcp-server is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 24d ago), licensed MIT. It adds 40 tokens to every session and 1,521 once invoked, about $0.0002 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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