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Tanod MCP server

utilpeek: generate lorem ipsum placeholder text

text_lorem
Read-onlyIdempotent

Generate reproducible lorem ipsum or English placeholder text for design mockups and tests; the same seed returns identical filler.

Instructions

utilpeek: Generate seeded lorem ipsum or English placeholder text. For random values use generate_random. Returns placeholder text: classic lorem ipsum or English filler, in words (at most 10,000), sentences (1,000) or paragraphs (100), deterministic for a seed (echoed back). Input: optional kind, unit, count, seed and start_with_lorem; an empty body gives 3 paragraphs. Typically under 0.3 s. Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNolorem (Latin-like lorem ipsum, default) or english (common English words).lorem
seedNoRandom seed (default 0): the same seed and settings give the same text.
unitNoWhat `count` counts: words, sentences or paragraphs (default).paragraphs
countNoAt most 10,000 words, 1,000 sentences or 100 paragraphs.
start_with_loremNokind=lorem: begin with "Lorem ipsum dolor sit amet" (default true).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
seedNo
textNo
unitNo
countNo
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
wordsNo
sourceNo
charactersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description layers on traits they do not carry: determinism tied to `seed` (echoed back), typical latency (<0.3 s), price (USD 0.001) and a free quota (10 calls per IP per UTC day). Those operational constraints are exactly the kind of context annotations cannot express.

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?

Front-loaded with purpose, then alternative, return shape, inputs, latency, price and quota in a compact block where each sentence is short. Minor redundancy in re-listing the count caps already present in the schema, but nothing is padded.

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?

An output schema exists, so return values need not be explained, yet the description still characterizes them (lorem vs English filler, echo of `seed`). Combined with defaults, limits, latency, cost and quota, an agent has everything needed to invoke it correctly and decide whether it can afford to.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description goes one step beyond by stating the empty-body consequence ('an empty body gives 3 paragraphs'), which is derived behavior rather than a restatement of the schema defaults, and by summarizing what `count` is bounded by. It does not add format details for `seed` or `start_with_lorem` beyond the schema.

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?

Specific verb (Generate) plus resource (seeded lorem ipsum / English placeholder text) stated in the first sentence. It explicitly disambiguates from the sibling it could be confused with, generate_random, so an agent can route without opening either schema.

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?

Names the alternative tool and the condition that selects it: 'For random values use generate_random.' It also states the default-call behavior ('an empty body gives 3 paragraphs'), so an agent knows both when to use this and what happens with no arguments.

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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