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by The-40-Thieves
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Python
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Float Repr

float_repr
Read-onlyIdempotent

Inspect any number's binary64 representation to see its exact stored value, raw bits, ULP, neighboring floats, and whether the literal is representable or rounded.

Instructions

What binary64 actually stores for X: exact value, raw bits, ULP, both neighbours, and whether the literal is representable. float_repr(0.1) shows 0.1000000000000000055511151231257827...; float_repr(0.25) says EXACT. Above 2^53 warns consecutive integers are indistinguishable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xYesValue to inspect as binary64: exact stored value, raw bits, ULP, neighbours, and representability

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.0
    • addedInput schema / properties / x / description
      Added value: +"Value to inspect as binary64: exact stored value, raw bits, ULP, neighbours, and representability"
  2. Changed1 schema field changedv0.11.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "float_reprDictOutput",
+  "type": "object"
+}
  3. Changed4 schema fields changedv0.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / x / title
      Added value: +"X"
    • addedInput schema / title
      Added value: +"float_reprArguments"
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  4. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it reports ULP, neighbors, representability, and warns about the ≥2^53 limit where consecutive integers become indistinguishable. This goes beyond the annotation flags.

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 compact and front-loaded with the core purpose, followed by concrete examples and an edge-case warning. Every sentence adds value, and the structure is easy for an agent to parse quickly.

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?

With a single parameter and an output schema present, the description covers the purpose, behavior, and edge cases. It lacks an explicit note on error handling (e.g., non-finite values), but is otherwise complete for the tool's simplicity.

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?

The single parameter x is fully described in the schema (coverage 100%), with the same explanation in the tool description. The description re-emphasizes the meaning and provides examples, but does not add new parameter semantics beyond what the schema already states.

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 states precisely what the tool does: it shows the exact binary64 stored value, raw bits, ULP, neighboring values, and representability. It is clearly distinguishable from siblings like bits, radix_convert, or calc_exact by focusing on float representation. The example cases reinforce the purpose.

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 description gives a usage example but does not explicitly state when to use this tool versus alternatives such as bits or radix_convert. It implies the tool is for inspecting float representation, but there is no guidance on when not to use it or which sibling to prefer in other cases.

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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