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 XAIHT/Tlamatini --skill create_new_mcpgit clone --depth 1 https://github.com/XAIHT/TlamatiniWrote 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/xaiht/tlamatini/create_new_mcp)<a href="https://agentmods.dev/skills/xaiht/tlamatini/create_new_mcp"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/create_new_mcp/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/xaiht/tlamatini/create_new_mcp"><img src="https://agentmods.dev/badge/skills/xaiht/tlamatini/create_new_mcp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 107 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
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 | $0.00147 | $0.01969 |
| Opus 5 | $0.00073 | $0.00984 |
| Sonnet 5 | $0.00029 | $0.00394 |
| Haiku 4.5 | $0.00015 | $0.00197 |
Grade A, and why
create-new-mcp 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 9d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create New MCP / Tool — canonical reference
The full, ground-truth procedure for adding a tool, an MCP context provider, or both lives at:
Tlamatini/.mcps/create_new_mcp.md
First decision — classify the request before writing anything
| Type | The request looks like... | Path to follow |
|---|---|---|
| Tool only | "let the LLM do X on demand" — run a command, start an agent, unzip, decompile, summarize. | Tool-only workflow (below). |
| MCP-backed context provider only | "every chat turn, prefetch X and inject it as context" — system metrics, files search, repo inventory. | MCP context-provider workflow (below). |
| Both | Pre-fetched context and a separate action tool. Rare; most are one or the other. | Do both halves. |
Tool-only workflow (most common)
- Implement an
@toolfunction inagent/tools.py(sync, returns strings). - Resolve any bundled paths for both source and frozen modes (
os.path.dirname(sys.executable)vsos.path.dirname(os.path.abspath(__file__))). - Register the tool in
get_mcp_tools()under aglobal_stategate. - Seed a
Toolrow via a new migration so the UI toggle row exists. - Frontend usually needs NO changes — the tool checkbox UI is dynamic.
- The tool is only usable when the unified-agent / Multi-Turn chain is selected.
MCP context-provider workflow
- Create
mcp_<name>_server.py+mcp_<name>_client.py. - Create a sidecar chain
chain_<name>_lcel.py. - Wire startup in
apps.py+management/commands/startserver.py. - Extend
rag/factory.py: import the chain, add the sync wrapper, map the status key, patchinvoke()to inject the new payload field. - Choose a payload field and update all main chains (basic / history-aware / unified).
- Seed an
Mcprow and update the frontend MCP checkboxes (they are hardcoded, NOT dynamic — unlike Tool checkboxes). - Verify persistence and reconnect behavior end-to-end.
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.
- 9d ago First seen · 124 lines · 147 tokens per session scan A a40449f7987f
create-new-mcp is a skill published in the GitHub repository XAIHT/Tlamatini (16 stars, last pushed today), licensed MIT. It adds 147 tokens to every session and 1,969 once invoked, about $0.0007 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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