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 cyanheads/census-mcp-server --skill design-mcp-servergit clone --depth 1 https://github.com/cyanheads/census-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/skills/cyanheads/census-mcp-server/design-mcp-server)<a href="https://agentmods.dev/skills/cyanheads/census-mcp-server/design-mcp-server"><img src="https://agentmods.dev/badge/skills/cyanheads/census-mcp-server/design-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 5.1 | $0.00062 | $0.15665 |
| Opus 5 | $0.00031 | $0.07832 |
| Sonnet 5 | $0.00012 | $0.03133 |
| Haiku 4.5 | $0.00006 | $0.01566 |
Grade A, and why
design-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 8d 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.
This is a copy
100% identical to design-mcp-server — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 714 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
- User says "I want to build a ___ MCP server"
- User has an API, database, or system they want to expose to LLMs
- User wants to plan tools before scaffolding
- Existing server needs a new capability area (design the addition, not just a single tool)
Do NOT use for single-tool additions — use add-tool directly.
Inputs
Gather before designing. Ask the user if not obvious from context:
- Domain — what system, API, or capability is this server wrapping? Or is the server providing internal capability with no external dependency (computation, text/code utilities, in-memory state)?
- Data sources / source of truth — APIs, databases, file systems, external services? Or is the server itself the source (in-memory state, pure computation, local-only utility, embedded model)?
- Target users — what will the LLM (and its human) be trying to accomplish?
- Scope constraints — read-only? write access? admin operations? what's off-limits?
If the domain has a public API, read its docs before designing. For internal-only servers, skip API research and go straight to user goals. Don't design from vibes either way.
Server scope and audience
Before committing to a server boundary, answer: what workflow does this server serve, and who is the audience?
The unit of a server is a user workflow, not an API. A single rich API can earn its own server when the audience is large and the API surface supports a full workflow (PubMed for literature research, SEC EDGAR for financial analysis, Shodan for internet-wide device intelligence). Multiple APIs should collapse into one server when they serve the same workflow from different angles — a "threat intelligence" server that aggregates VirusTotal, AbuseIPDB, and GreyNoise is more useful than three separate servers because the user's goal is "assess this indicator," not "query VirusTotal."
Don't default to one-API-one-server. That's the right call when the API is deep enough and the audience is large enough, but it's not the starting point. The starting point is the workflow:
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.
- 8d ago First seen · 714 lines · 62 tokens per session scan A 27b9a61334d6
design-mcp-server is a skill published in the GitHub repository cyanheads/census-mcp-server (2 stars, last pushed 14d ago), licensed Apache-2.0. It adds 62 tokens to every session and 15,665 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to design-mcp-server, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
paperbanana-diagram
Author publication-quality diagram specifications (method.txt files) from paper prose and draw.io sketches, following federal data visualization standards and Tufte design principles. Use when creating, modifying, or regenerating conceptual diagrams, architecture figures, or visual explanations for academic papers or…
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
colab-finetuning
Fine-tune LLMs on Google Colab GPUs directly from openscience. Connects to Colab runtimes via WebSocket bridge for remote training with Unsloth. Supports SFT, GRPO, DPO, vision, and TTS workflows on free T4 to Pro A100 GPUs.
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…
loop
Full execution protocol for MODE: LOOP — the compound-engineering loop: brainstorm → plan → build → review → improve, iterating under defense-in-depth stop conditions with generator/critic separation, durable resumable state, and mandatory compounding learning capture. Loaded on demand by the architect when the loop…
google-meet
Google Meet via gws: create spaces, fetch join links, list recordings.