Skip to main content
Glama

Evaluate & cross-check a math expression

evaluate
Read-only

Check any math expression with eight independent engines and compare results, exposing unreliable values from floating-point error, singularities, or branch cuts. Use it when correctness matters.

Instructions

Verify any numeric result that has to be trusted, via the Ultimath API (https://ultimath.ai). Use this whenever the correctness of a number matters — floating-point-sensitive expressions, catastrophic cancellation, values near singularities or branch cuts, ties, or any computation where a single method could be silently wrong. Prefer it over computing by hand or running code: it evaluates the expression on 8 independent engines (exact symbolic, multiprecision ball, interval arithmetic, three different arbitrary-precision floats, deferred symbolic rewriting, IEEE 754 double) in parallel and returns every result plus per-engine precision diagnostics, so disagreement exposes an unreliable value that a single calculation would hide.

The 8 engines are independent and each computes with its own native primitive — so divergence between them (at poles, singularities, branch cuts, or ties like round(2.5)) is the backends' real behavior, NOT a bug. Agreement across engines is a confidence signal; divergence flags a value that is convention-dependent or near a domain edge — verify it rather than trust a single column.

One engine (calcium) is exact rather than numeric: it decides a value instead of approximating it, so it reaches poles the others can only approach (tan(pi/2) is uinf), and when the printed digits are only a truncation it also returns the closed form in symbolic / symbolic_latex (sqrt(2)+1 carries Add(Sqrt(2), 1)). Prefer that closed form when quoting an exact value.

A comparison does not mean the same thing on every engine. On the enclosure engines (flint, mpfi) !=, < and > return true only once the property is PROVEN, while ==, <= and >= return true as soon as it is not disproven. So a true == there is not a proof of equality — calcium decides that one exactly — but a true != IS a proof of difference. Each pair is complementary: a == b and a != b are never both false.

A value that is real comes back as a plain real number: an engine drops a zero imaginary part only when it PROVES the value real. So a trailing +0i is NOT decoration — it means that engine did not establish realness, and a visible residue (e.g. 4.3e-17i on the double engine) is genuine numerical error. Do not strip either one when reporting a result.

Supports: arithmetic, trigonometry (sin, cos, tan), exponentials and logs (exp, ln, log), roots and powers (sqrt, x^y), factorial, complex numbers (3+2i), arbitrary bases (0xFF, 0b1010), constants (pi, e, the golden ratio PHI). Multiplication must be EXPLICIT: write 2pi, 2(3+4), (a+b)(c+d), 2sin(x) — adjacency is NOT a product (2pi is an error). Precedence, tightest first: postfix factorial ! (2^3! = 2^(3!) = 64), then powers ^, then * / %, then + -. Note: expressions are mathematical only — there are no type casts ((int)x) or constructors (complex(1,2)); write a complex number as 1+2i or (re, im). Call list_functions for the authoritative list of available functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format (default 'fixed')
precisionNoDecimal digits of precision (default 50). The ceiling is the PLAN behind the key — the free beta caps at 50 and refuses more — and 999 above every plan, the length of the built-in constants.
expressionYesMath expression, e.g. '0.1 + 0.2', 'sin(pi/4)', 'sqrt(2)^2 - 2', '2*pi', '2*(3+4)', 'factorial(10)'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
msNo
traceNo
resultNoThe retained reading — read `accuracy` before quoting any of its digits
enginesYesPer-engine results, compare to detect floating-point error
inexactYesTrue if any engine emitted a warning (precision loss or domain issue somewhere)
accuracyNoWhat the reading is worth once the other engines have been read against it. `exact` and `proved` are different KINDS of fact and stand together here
angle_unitNo
expressionYes
Install Server

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains nontrivial behavior far beyond the readOnlyHint annotation: engine divergence is real behavior, not a bug; comparison semantics differ per engine (e.g., `!=` can be a proof of difference while `==` is not a proof of equality); and trailing `+0i` indicates unproven realness rather than decoration. This is precisely the kind of behavioral nuance an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place: purpose, when-to-use, engine behavior, comparison semantics, realness, syntax, and precedence are all distinct and clearly organized. It is front-loaded with the core purpose and usage guidance before diving into technical details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description is unusually complete. It covers return content (per-engine results and precision diagnostics), engine semantics, exact closed-form output, precision limits, supported operations, syntax constraints, and points to list_functions for the authoritative list. Nothing essential is missing for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial meaning beyond the schema: explicit-multiplication rules, precedence order, complex-number syntax, unsupported constructs like type casts, the precision ceiling tied to the plan, and examples. This makes the expression parameter far more usable than the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Verify any numeric result that has to be trusted, via the Ultimath API.' It clearly states what the tool does, names its multi-engine evaluation behavior, and differentiates it from computing by hand, running code, and the sibling list_functions tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance: 'Use this whenever the correctness of a number matters,' and enumerates concrete scenarios such as floating-point-sensitive expressions, cancellation, singularities, branch cuts, and ties. It also says 'Prefer it over computing by hand or running code,' directly routing the agent away from alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Flupke68/ultimath-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server