gnomon

Name: gnomon
Author: lihtness

by lihtnessversion 0.1.2i★ –iRuns on your computer

Registry nameio.github.lihtness/gnomon-mcp

Install

Overview

Description from the maker (original English)

Deterministic batch tools so LLM agents stop guessing dates and math.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "gnomon": {
      "command": "uvx",
      "args": [
        "gnomon-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add gnomon -- uvx gnomon-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "gnomon": {
      "command": "uvx",
      "args": [
        "gnomon-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "gnomon": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "gnomon-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account lihtness, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5659 servers in this category
Published to the registry more recently than 14% of the other 5659 servers in this category
Basic checks equal to or better than 100% of the other 5659 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is gnomon safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

gnomon-mcp

The pointer on a sundial that turns shadow into time.

A small MCP server for the boring-but-essential utilities every model needs: dates, calendars, arithmetic, unit conversion. Use it so your assistant stops "next-token guessing" math and date math.

Why

LLMs are bad at arithmetic and date math by default. They produce plausible answers that are often wrong by a small amount — exactly the kind of mistake that's hard to notice in a long response. gnomon-mcp exposes deterministic Python implementations through MCP so your model can compute instead of guess.

When an agent should reach for gnomon

Anywhere the next plausible token is not the right answer. Concretely:

Math that matters — anything beyond trivial mental arithmetic, anything with a decimal point, anything that compounds. Call calc.
"What day is it" / "how long until" / "how long since" — the model's training cutoff is not today. Call now for a snapshot; calendar with until/since/diff for elapsed time; parse for natural-language dates ("next thursday").
Date arithmetic across month/year boundaries — adding 30 days, finding a quarter-end, counting business days. Models routinely off-by-one these. Call calendar with add / business_days.
Unit conversion — call calc_convert. Never eyeball "kg → lb" or "°C → °F".
Table-row workloads — when the same kind of computation needs to run on every row of a table, both batch tools (calendar, calc) take a list and return a list in order. One call, N results.

The rule of thumb: if you'd ask a colleague to "just double-check that number," call gnomon instead.

How this compares to other MCP servers

Time and math already have several MCP servers — the official Time reference (timezone-only), mcp-time and mcp-datetime (date formatting / timezone), calculator-server (math + units, no dates), and bundles like agent-utils-mcp (regex / hashing / JWT). gnomon's lane is narrower:

Batch-first. calendar(ops) and calc(expressions) take lists; one tool call covers a whole table column instead of N calls.
A real `now()`. One call returns 18 fields — ISO week, quarter, fiscal year, day-of-year, is_weekend, … — instead of just {iso, tz}.
**Dates *and* math *and* units in one wiring.** No need to compose three separate servers.
Natural-language dates baked in ("next thursday", "in 3 hours") without a separate NLP server.

If you only need timezone conversion, the official Time server is enough. If you want a broad utility bundle (regex, hashing, encoding, JWT), agent-utils-mcp is a better fit. gnomon is for the boring date-arithmetic-and-arithmetic core, batched.

Tools

Calendar

Two tools:

`now(tz?)` — standalone. Returns a rich dict snapshot of the current moment. One call gets you everything about "right now".
`calendar(ops)` — batch dispatcher. Each item picks its own op. Designed for table-row workloads (e.g. one call computes time-elapsed for every row).

`now(tz?)` returns:

"iso": "2026-05-25T14:30:45+00:00", "date": "2026-05-25", "time": "14:30:45", "unix": 1779345045, "tz": "UTC", "year": 2026, "month": 5, "month_name": "May", "day": 25, "weekday": "Monday", "weekday_num": 0, # 0=Monday "day_of_year": 145, "week_of_year": 22, # ISO week "quarter": 2, "fiscal_year_us_gov": 2026, # FY starts Oct 1 "hour": 14, "minute": 30, "second": 45, "is_weekend": False, }

`calendar(ops)` operations:

Op	Params	Returns
`diff`	`start, end, unit`	`end - start` — time elapsed between two known dates
`until`	`target, unit, tz?`	`target - now` — time left to a future point (negative if past)
`since`	`source, unit, tz?`	`now - source` — time elapsed since a past point (negative if future)
`add`	`date, n, unit`	ISO of `date + n units` (`seconds\	...\	weeks`, plus` months\	years` calendar-aware)
`weekday`	`date`	`"Monday"`..`"Sunday"`
`business_days`	`start, end`	count of Mon-Fri days (start inclusive, end exclusive)
`parse`	`natural, tz?`	ISO from natural language ("next thursday", "in 3 hours")
`format`	`date, fmt`	strftime-formatted string

Units for diff/until/since: seconds, minutes, hours, days, weeks.

Example — compute several things in one call:

calendar([

{"op": "until", "target": "2026-12-31", "unit": "days"}, # days left in year {"op": "since", "source": "2026-01-01", "unit": "days"}, # days elapsed in year {"op": "diff", "start": "2026-01-01", "end": "2026-12-31", "unit": "days"}, {"op": "weekday", "date": "2026-05-25"}, # "Monday" {"op": "add", "date": "2026-05-25", "n": 1, "unit": "months"}, {"op": "parse", "natural": "next thursday", "tz": "America/Los_Angeles"}, ])

Calculator

Tool	Purpose
`calc(expressions)`	Evaluate a list of Python expressions and return a list of results. Math (`sqrt`, `sin`, `log`, `pi`, `e`, ...), stats (`mean`, `median`, `stdev`, `variance`), and useful builtins (`abs`, `round`, `min`, `max`, `sum`, `range`, `sorted`, ...) are pre-loaded. Batch in / batch out, order preserved.
`calc_convert(value, from_unit, to_unit)`	Unit conversion via Pint (`meter` → `foot`, `kg` → `lb`, `degC` → `degF`, etc.).

Examples:

calc(["2 + 3 * 4"])                  # [14]

calc(["sqrt(16)", "sin(pi/2)"]) # [4.0, 1.0] calc(["mean([1, 2, 3, 4])"]) # [2.5] calc(["sum(range(101))"]) # [5050] calc(["(25 / 100) * 100"]) # [25.0]

Future tools (sketches)

The same logic — *if the model is likely to bluff it, expose a deterministic version* — points at several more primitives worth building. None of these are implemented yet; they are candidates, listed roughly in order of bang-for-buck:

Text measurement — count(text, unit) for chars / words / lines / sentences / LLM tokens. Agents constantly miscount "how long is this" and "will this fit in the context window."
Regex match / replace — regex_find(pattern, text) and regex_sub(pattern, repl, text). Models hallucinate which substrings match a regex; a real engine ends the argument.
Structured-data extraction — jq(path, json) / jsonpath(path, json). Reading values out of a nested blob by path, without typos.
Hashing & encoding — hash(text, algo) (sha256, md5, blake2), encode(text, scheme) / decode(text, scheme) (base64, hex, url, jwt-payload). All things models confidently invent wrong.
Decimal money math — money(expr) evaluated under Python's Decimal with explicit rounding. calc is float-based and quietly unsafe for currency.
Holiday-aware business days — extend calendar.business_days with a country (or calendar) parameter so US/UK/IN holidays are excluded. The current implementation only knows weekends.
Cron describe / next-fire — cron_describe("0 9 * * 1-5") → human English; cron_next(expr, n) → next N firing times. Models routinely misread cron fields.
Token counting for a target model — count_tokens(text, model) via tiktoken / Anthropic tokenizer. Lets an agent budget its own prompts and outputs instead of guessing.

If you want one of these, open an issue (or a PR — each is a small self-contained module that fits the existing tools/ layout).

Install

Recommended: no install — run on demand via uv:

uvx gnomon-mcp           # serves stdio MCP, ready for any client

uvx gnomon-mcp --demo # call every tool once and print the results (no MCP client needed)

Or install globally:

pip install gnomon-mcp

Wire it into your agent

All recipes assume uvx gnomon-mcp. If you prefer a pinned install, swap the command for gnomon-mcp (with no uvx).

Claude Code

claude mcp add gnomon -- uvx gnomon-mcp

Or edit ~/.claude.json / a project .mcp.json:

"mcpServers": { "gnomon": { "command": "uvx", "args": ["gnomon-mcp"] } } }

Claude Desktop

claude_desktop_config.json:

"mcpServers": { "gnomon": { "command": "uvx", "args": ["gnomon-mcp"] } } }

Cursor

~/.cursor/mcp.json (or .cursor/mcp.json in a project):

"mcpServers": { "gnomon": { "command": "uvx", "args": ["gnomon-mcp"] } } }

Continue

~/.continue/config.yaml:

mcpServers:

name: gnomon command: uvx args: ["gnomon-mcp"]

Any other client (generic stdio)

Spawn uvx gnomon-mcp as a subprocess and speak MCP over stdin/stdout. That is the entire integration.

Hosted / remote (HTTP transport)

For team-shared instances or agents that can't spawn a local subprocess:

uvx gnomon-mcp --transport streamable-http --host 0.0.0.0 --port 8000

also supported: --transport sse

Then point your MCP client at http:// :8000/mcp (or /sse for the SSE transport).

Tell your agent to actually use it

The MCP tool descriptions are intentionally terse to keep persistent context cost minimal (~150 tokens for all four tools). The richer "when to reach for gnomon" guidance lives in a Claude Code skill that loads on demand.

Option A — Claude Code plugin (one command, recommended)

The plugin wires both the MCP server *and* the skill in one shot. Inside Claude Code:

/plugin marketplace add lihtness/gnomon-mcp

/plugin install gnomon@gnomon-mcp

That registers gnomon as an MCP server (auto-starts via uvx) and installs the on-demand skill. Skill body loads only when the task triggers it — persistent context stays ~150 tokens for the four tool descriptions plus ~40 tokens for the skill's name + summary.

Option B — manual skill install (no plugin)

If you've already wired the MCP server with claude mcp add gnomon -- uvx gnomon-mcp and only want the skill:

mkdir -p ~/.claude/skills/gnomon

curl -fsSL https://raw.githubusercontent.com/lihtness/gnomon-mcp/main/skills/gnomon/SKILL.md \ -o ~/.claude/skills/gnomon/SKILL.md

Option C — paste into your system prompt (non-Claude-Code agents)

For agents without skill support, paste this short version into your system prompt or CLAUDE.md:

You have gnomon: deterministic tools for dates and math. Use them instead

of guessing.

now — current moment (your training cutoff isn't today).
calendar(ops) — batch date math: diff/until/since/add/weekday/ business_days/parse (natural language)/format.
calc(expressions) — batch Python eval; math + statistics + common builtins pre-loaded.
calc_convert(value, from, to) — unit conversion via Pint.

Both batch tools take a list and return a list. Prefer one batched call over many small ones.

Development

git clone https://github.com/lihtness/gnomon-mcp

cd gnomon-mcp pip install -e ".[dev]" pytest

License

MIT

gnomon

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

gnomon-mcp

Why

When an agent should reach for gnomon

How this compares to other MCP servers

Tools

Calendar

Calculator

Future tools (sketches)

Install

Wire it into your agent

Claude Code

Claude Desktop

Cursor

Continue

Any other client (generic stdio)

Hosted / remote (HTTP transport)

also supported: --transport sse

Tell your agent to actually use it

Option A — Claude Code plugin (one command, recommended)

Option B — manual skill install (no plugin)

Option C — paste into your system prompt (non-Claude-Code agents)

Development

License