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Jambozx

OnlineCyberTools MCP (280+ filterable tools)

conversion_string_number

Read-onlyIdempotent

Convert text between six numeric representations: Unicode code points, integers, floats, scientific notation, English words, and Roman numerals. Ideal for mapping characters to code points or reformatting number lists.

Instructions

String / Number Format Converter. Convert text between six representations of the same underlying numeric values: string (each character as its Unicode code point), integer, float, scientific notation, English number words (zero-twenty, tens, hundred, thousand, million), and Roman numerals. Parsing string reads each character code point; the other types read space/comma/newline-separated tokens. Use this to map characters to/from code points or to reformat a list of numbers; use conversion_roman_numerals for a dedicated Roman converter, and conversion_number_base for ASCII/binary/hex/octal byte encodings. Runs locally on the supplied text: read-only, non-destructive, offline, no auth, default rate limit. Returns the converted string, an analysis block, and reference info for the target format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesValue(s) to convert. For from_type=string the whole text is read character-by-character; for numeric types it is split on spaces, commas, and newlines into separate values. Must be non-empty.
from_typeYesHow to interpret the input. string=Unicode code points per character; integer/float/scientific=numeric tokens; words=English number words; roman=Roman numerals.
to_typeYesOutput format. string emits one character per code point; float fixes 3 decimals; scientific uses 3-digit exponential; roman supports 1-3999; words covers 0-99 then falls back to digits.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoWhether the conversion succeeded.
resultNoThe values rendered in to_type (empty string on error).
errorNoError message; present only when success is false (e.g. empty input, invalid token, unknown number word).
analysisNoConversion stats (null on error): input_length, output_length, values_count, value_statistics{min,max,average,sum}, conversion_type{from,to}, data_type_analysis{integers,floats,negative,zero,positive}, encoding_info{source_format,target_format,reversible,precision_loss}.
format_infoNoReference info for to_type (name, description, example, data_type); present only on success.
Behavior4/5

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

Annotations already declare read-only, non-destructive, idempotent. The description adds concrete behavioral details: runs locally, offline, no auth, default rate limit. It also outlines parsing behavior and return contents, complementing annotations without contradiction.

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

Conciseness4/5

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

The description is concise (4-5 sentences) and well-structured: name, enumeration, parsing details, usage guidance, operational context, return info. Some minor redundancy but overall efficient.

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?

Given the tool's complexity (3 params, 6 representations, parsing rules) and existing output schema, the description covers major aspects: supported types, parsing behavior, conversion constraints, and sibling distinctions. Could clarify multi-value output but schema likely covers.

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?

Despite 100% schema coverage, the description adds significant meaning: parsing rules per from_type (character-by-character vs token splitting), output format specifics (3 decimals for float, exponential for scientific, range limits for roman and words). This goes beyond enum labels.

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 clearly states the tool's purpose as a 'String / Number Format Converter' that converts between six representations. It enumerates the types and differentiates from siblings by naming conversion_roman_numerals and conversion_number_base as alternatives.

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?

Explicitly advises using this tool for mapping characters to/from code points or reformatting number lists, and directs users to specialized tools for Roman numerals and number base conversion. This provides clear when-to and when-not-to guidance.

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

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