Skip to main content
Glama
tetracoralla

math-anchor

by tetracoralla

Run mathematical operations in a batch

math.batch
Read-onlyIdempotent

Run 1-32 independent math operations in a single ordered batch, setting per-item memory, timeout, output, and precision limits.

Instructions

Run 1 to 32 independent operations in order with per-item limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
timeoutMsNoCumulative deadline for the complete batch, including queued items.
maxOutputBytesNoStrict UTF-8 byte budget for the complete ordered batch result.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.4.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds genuinely useful behavioral context beyond that: operations are independent, executed in order, and subject to per-item limits. It does not describe partial-failure behavior, but that gap is mitigated by the output schema and annotations.

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?

A single front-loaded sentence contains only high-signal details: count range, independence, ordering, and per-item limits. There is no fluff or repetition of schema defaults.

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

Completeness4/5

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

Together with a detailed input schema, an output schema, and read-only/idempotent annotations, the description is nearly complete for selecting and invoking the tool. The main omissions are explicit routing to math.run for single operations and any note about how item failures affect the batch, but neither blocks correct basic use.

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

Parameters3/5

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

Schema description coverage is 67% and the schema carries detailed descriptions for timeoutMs, maxOutputBytes, and resultMode. The description contributes only the general notion of per-item limits and the 1-32 item count, which mostly restates schema constraints. This matches the high-coverage baseline of 3.

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

Purpose4/5

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

The description names a specific action ('Run'), a clear resource ('1 to 32 independent operations'), and an ordering constraint ('in order'), so an agent can tell it is a batch-execution tool. It does not explicitly name math.run or another sibling to differentiate, so it misses the top score.

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

Usage Guidelines3/5

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

The phrase 'independent operations' and the 1-32 range imply this tool is for multiple independent computations, while the sibling math.run suggests single-operation use. However, the description never explicitly says when to prefer this over math.run or warns against dependent operations, leaving the guidance implicit.

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

Install Server

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/tetracoralla/math-anchor'

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