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Ajisai

by masamoto1982version 0.9.0i★ –iRuns on your computer
Registry nameio.github.masamoto1982/ajisai

Install

Overview

Description from the maker (original English)

Exact rational and algebraic computation, with machine-readable Word contracts, in Ajisai.

PopNx MCP’s three pillars

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

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: Docs, sheets & office work

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

  • npmNode.js installed (for the npx command)

Which app do you use?

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": {
    "ajisai": {
      "command": "npx",
      "args": [
        "-y",
        "ajisai-mcp-server"
      ]
    }
  }
}

Run this in your terminal

claude mcp add ajisai -- npx -y ajisai-mcp-server

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "ajisai": {
      "command": "npx",
      "args": [
        "-y",
        "ajisai-mcp-server"
      ]
    }
  }
}

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": {
    "ajisai": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "ajisai-mcp-server"
      ]
    }
  }
}

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 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account masamoto1982, 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 Docs, sheets & office worki

  • More stars than 0% of the other 3572 servers in this category
  • Published to the registry more recently than 55% of the other 3572 servers in this category
  • Basic checks equal to or better than 100% of the other 3572 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 Ajisai 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 (0.9.0) reached the registry 25 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

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

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Runs in the cloud

Excel MCP Server

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by haris-musaWidely used↓ 49.6K/wk★ 4.3KUpdated 7 days ago

Runs on your computer

MCP Server for Excel

Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

by sbroenneGrowing↓ 4.3K/wk★ 847Updated 5 days ago

Runs on your computer

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.

Ajisai MCP server

Ajisai's MCP surface makes the language useful as a bounded, deterministic and diagnostic computation kernel for AI agents. The server remains a thin adapter: the Rust CLI owns language semantics and generated artifacts own vocabulary.

Ajisai promises exactness in its supported numeric domain, rather than unqualified “no rounding errors”. Operations such as explicit rounding and functions outside that domain retain their documented semantics.

This README is the server's own record of what it does and how it is verified; the readiness tracker and the hand-off memo that once accompanied it in docs/dev/ were retired when their exit criteria were met. Host-by-host resource ceilings are compared in docs/dev/mcp-host-profiles.md.

Install and connect

Requirements: Node 20 or newer. Nothing else — no build step, no cargo, no native binary: the package carries its WASM backend.

The server is published on npm as ajisai-mcp-server and listed in the official MCP Registry as io.github.masamoto1982/ajisai. Most MCP clients take a JSON server entry. Claude Desktop (claude_desktop_config.json), Claude Code (.mcp.json) and Cursor (.cursor/mcp.json) all use this shape:

{

"mcpServers": { "ajisai": { "command": "npx", "args": ["-y", "ajisai-mcp-server"] } } }

npx -y ajisai-mcp-server --doctor exits 0 when the installed copy can actually compute.

From a checkout

A clone runs the same server without npm, which is how the repository's own tests and an unreleased engine are reached. The WASM backend is committed under wasm/generated/, so a fresh clone computes immediately.

git clone https://github.com/masamoto1982/Ajisai.git

cd Ajisai/tools/mcp-server npm install node index.js --doctor # exits 0 when this copy can actually compute

The server entry then names the checkout's index.js:

{

"mcpServers": { "ajisai": { "command": "node", "args": ["/path/to/Ajisai/tools/mcp-server/index.js"] } } }

That is the whole configuration. No env block is needed, and pointing AJISAI_BIN at a native binary — as an earlier version of this file did in its only working example — is an optional override, not a prerequisite:

  • AJISAI_BIN selects a native ajisai binary instead of the packaged WASM backend, which is how a Docker image that builds one in should be wired.
  • AJISAI_REPO is a development-only fallback for discovering a locally built binary without naming it.

Without either, a native binary is discovered only when this package is run from an Ajisai checkout (tools/mcp-server beside rust/Cargo.toml), and then the more recently built of rust/target/release and rust/target/debug is used. An installed copy under node_modules never looks outside itself: it runs the packaged WASM backend.

The published package holds only what the server runs — the adapter, its two backends, the packaged assets and WASM module, README.md, CHANGELOG.md and LICENSE. The evaluation harness, its corpora and the tests stay in the repository (npm run test:pack fails if one ships).

Both backends answer identically (see Backends and provenance); the override is about deployment, not about results.

Checking an installation

The server is silent when it is healthy, which makes it indistinguishable from one that is wedged. The same executable answers for itself:

node index.js --version    # adapter, engine and specification versions, registry digest

node index.js --doctor # Node, assets, backend and two real computations node index.js --help

--doctor exits 0 when every check passes and 1 when any fails, so it can gate a container start or a support request. It computes 2 3 DIV 1 3 DIV ADD and 2 SQRT through the selected backend: a server that starts and loads its assets but answers wrongly is still broken, and only running something proves otherwise. With no arguments the process speaks MCP on stdin/stdout and writes nothing else there.

Agent surface

toolpurpose
computeexecute source with time, source, output and step limits
checkparse, resolve and conservatively verify #:contract declarations without execution
infer_contractsinfer contracts for user-defined Words without execution
word_contractquery the complete canonical spec/words.json contract registry

Execution tools accept source text only. Deliberately omitting file-path input prevents an AI tool call from becoming an arbitrary local-file reader.

check answers about the *form* of a program, never about running it: it tokenizes, parses, resolves names and verifies #:contract declarations (#:contract DOUBLE inputs=1 outputs=1 field=closed, one line per Word, in the keys CONTRACT answers in — inputs, outputs and field, and no other: any other term makes the directive malformed), all without execution. compute runs the same declaration check before executing anything, and a declaration the body contradicts, or a malformed one, stops the run: both tools answer it as an ordinary error — error.category: contractViolation and a message quoting the finding — with every finding in contractDecls.findings ({ severity, message }) and a verdict per Word in contractDecls.declarations, and compute names it outcome: error:contractViolation. A source without a directive carries no contractDecls. So a check that returns status: ok says the program is well-formed and its names resolve — it does not say the program will succeed, stay inside a ceiling, or produce a value rather than a NIL. Nothing that only a run can decide is decided here. Read it as "this will get as far as executing", and read compute for what executing it does.

The three source tools answer one program consistently, and tool-consistency.test.js (part of npm run selftest) holds them to it over every golden and corpus source:

  • An error's outcome and its error.category name one category.
  • Source that does not parse is malformedSource to all three — infer_contracts included.
  • check ok means the run does not fail on the program's form or its names.
  • Every suggested line infer_contracts answers, pasted back above the source, verifies under check.
  • check is stricter than a run: it rejects a name nothing defines even inside a Word that is never called, which a run never reaches.
Three outcomes, kept distinct
Ajisai outcomestatusoutcome (compute)isError
a valueokvalue—
NIL(reason)ok, with the absence reasonnil: —
language ERRORerror, with message and errorerror: —
a failure of the *host*hostError—yes

outcome uses the ids of spec/outcomes.json, so a reasoned absence is told apart from a value without reading the top stack node.

All four tools answer with the same envelope (result.schema.json, also served as ajisai://schema/result), so one schema describes every result a caller can receive and there is no second contract to keep in step.

What a result costs

Every result arrives twice: as structuredContent, which a caller branches on, and serialized into a text content block, because MCP asks a tool with an output schema to also return the serialized JSON that way — a text-only client has no other route to any of it. That mirror stays. Replacing it with a prose summary is the one compaction that would actually lose information, and the self-test pins that a text-only client can still tell a value, a reason-carrying NIL, a language error and a host failure apart from the text alone.

What was removed is everything constant. 1 2 ADD answered 3,732 bytes, of which the answer — stack and stackDisplay — was 85: the rest was the twelve-entry limit table on every result (mcp.limits), and an execution receipt and observation digest no host verified. Five calls in one turn cost 5,000 tokens of that. Now:

  • mcp.limitProfile names the profile (mcp-local-stdio); the ceilings are the ajisai://limits resource, read once. A ceiling that fires still names itself in error.resource and its value in message, and a host failure about one in error.limit, so no result needs the table to be acted on.
  • resourceUsage — what the run spent against each ceiling — is sent; the engine reports no optimizer counters at all.
  • The engine sends no receipt and no observation digest (envelope schema 5).
  • An optional field carrying no value is absent, not null — message and error on a plain success, contractDecls on a source that declares nothing. Test for presence, not for `null`.

1 2 ADD is about 1 KB as sent, and what remains constant is its provenance. npm run eval:performance measures the whole response and the text block alone over the seven benchmark cases and fails against a committed medianResponseBytesBudget, so none of it can come back unnoticed. The budget is a ceiling to lower when a response genuinely shrinks, never one to raise so a regression passes.

A host failure is machine-readable: error.code is a stable identifier (invalidRequest, unknownTool, sourceTooLarge, backendUnavailable, capacityExhausted, timeout, responseTooLarge, malformedBackendResponse, backendFailure, registryUnavailable), error.retryable says whether trying again can help, and error.limit names the declared ceiling when the failure is about one. error.message is written for a model and carries no host paths, environment-variable names or spawn diagnostics; that detail goes to the server's stderr, where an operator is looking.

Saturating concurrentExecutions queues the caller for up to a second before answering capacityExhausted, so an ordinary burst becomes back-pressure rather than a retry loop the caller has to write.

Reading an algebraic value

2 SQRT answers with the value, a rendering of it, and an approximation. On the stack node, read:

fieldwhat it is
stackDisplaythe value written as one token: "sqrt(2)", "2/1*sqrt(2)", "sqrt(2)-sqrt(3)", exact and never truncated
semantics.exactTermsthe value itself: Σ (numerator/denominator)·√radicand, arbitrary-precision integers as strings

The display renders exactly the terms beside it. Compute with exactTerms; the display is meant to be read rather than parsed.

The one that misleads is the node's own value: a rational approximation flagged semantics.approximate, so it looks exact and is not.

The display writes the canonical normal form, so equal values are written the same way: 8 SQRT and 2 SQRT 2 SQRT ADD both give 2/1*sqrt(2), and EQ decides they are the same number. Comparison decides equality here; string comparison does not — the string is display text, not a value. exactTerms does not appear on a rational or a vector of rationals, whose stackDisplay is already the whole value.

Errors

A language error is message — one line of text — and one object, error: { category, resource?, word?, line?, column? }. category is the spec/outcomes.json error category, the same id as in outcome, and is what to branch on; word is the Word the failure is attributed to, line and column where it was written, and resource, for resourceLimitExceeded, the entry of ajisai://limits that fired. An unknown Word is category: unknownWord with the name as written in word; word_contract answers that name with the closest known Words in suggestions.

responseBytes bounds the response as sent: the structured result, its serialized text mirror and provenance together. A *successful* result is kept under it by the engine rather than refused: under the agent profile a stack is sent whole up to 440 KiB (a 5,000-element vector of small integers), and past that

Only the beginning is shown; read the rest in the repo

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