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IranTools (paid)

fnv_hash

Origin URL: https://irantools.johnaio281.workers.dev Origin hostname: irantools.johnaio281.workers.dev Origin brand: IranTools - paid MCP server for AI agents Provider aliases: FNV-1a hasher, Content fingerprint Family key: irantools.johnaio281.workers.dev::text Endpoint: POST /api/iran/fnv_hash Summary: Compute a stable FNV-1a content fingerprint (32 and 64 bit). Keyword: fnv1a Keyword: fnv hash Keyword: content fingerprint Keyword: non cryptographic hash Keyword: stable hash Use case: compute a stable fingerprint for cache keys Use case: hash content without pulling in a crypto dependency Use case: compare two payloads by a 32-bit fingerprint Source description: Compute a stable FNV-1a content fingerprint (32 and 64 bit). Auth mode: paid

Price: $0.003 per call (USDC on Base via x402, or USDC on Tempo via MPP).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose genuinely useful behavioral facts – auth mode is paid and each call costs $0.003 via x402/MPP – but never states the return format (hex string? both bit widths? how a width is selected) or any limits, which matters for a tool with no output schema.

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

Conciseness2/5

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

Badly bloated: origin URL, hostname, brand, aliases, family key, endpoint, five keyword lines, and three use-case lines precede/pad the one substantive sentence. The lead is provider boilerplate instead of the tool's purpose, so the reader must scan to find value.

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

Completeness3/5

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

For a one-parameter hash tool this is only partially complete: cost and auth are covered, but with no output schema and no annotations the description should explain what is returned and how 32- vs 64-bit output is chosen. It leaves those gaps.

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 'text' parameter has 0% schema description coverage, so the description must compensate. It implies the input is the content to fingerprint, but it adds no format detail, and the '(32 and 64 bit)' claim references a choice that no parameter exposes, creating ambiguity rather than clarity.

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 core line 'Compute a stable FNV-1a content fingerprint (32 and 64 bit)' gives a specific verb and resource, and 'fnv_hash' is clearly distinguishable from siblings like entropy or detect_format. However, the purpose is buried under provider metadata and SEO keywords rather than stated up front.

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?

Three use cases are listed (cache keys, hashing without a crypto dependency, comparing payloads by 32-bit fingerprint), which implies when to reach for it. But there are no exclusions and no routing against siblings such as entropy or detect_format, so an agent still has to infer the boundary.

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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