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lorem

by io.github.pipeworx-io

Generate With Options

generate_with_options
Read-onlyIdempotent

Generate formatted placeholder text with headers, code blocks, and lists (HTML). Specify which elements to include: headers, code blocks, unordered lists, ordered lists.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoInclude code blocks
countYesNumber of paragraphs to generate (1–10)
lengthYesLength of each paragraph
headersNoInclude random headers (h1–h6)
ordered_listsNoInclude ordered (numbered) lists
unordered_listsNoInclude unordered (bullet) lists

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
htmlYesFormatted HTML output
countYesNumber of paragraphs requested
lengthYesLength of each paragraph
optionsYes
plain_textYesPlain text version with HTML tags stripped

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "Number of paragraphs requested",
+      "type": "number"
+    },
+    "html": {
+      "description": "Formatted HTML output",
+      "type": "string"
+    },
+    "length": {
+      "description": "Length of each paragraph",
+      "enum": [
+        "short",
+        "medium",
+        "long",
+        "verylong"
+      ],
+      "type": "string"
+    },
+    "options": {
+      "properties": {
+        "code": {
+          "description": "Include code blocks",
+          "type": "boolean"
+        },
+        "headers": {
+          "description": "Include random headers",
+          "type": "boolean"
+        },
+        "ordered_lists": {
+          "description": "Include ordered lists",
+          "type": "boolean"
+        },
+        "unordered_lists": {
+          "description": "Include unordered lists",
+          "type": "boolean"
+        }
+      },
+      "required": [
+        "headers",
+        "code",
+        "unordered_lists",
+        "ordered_lists"
+      ],
+      "type": "object"
+    },
+    "plain_text": {
+      "description": "Plain text version with HTML tags stripped",
+      "type": "string"
+    }
+  },
+  "required": [
+    "count",
+    "length",
+    "options",
+    "html",
+    "plain_text"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "code": false,
+    "count": 2,
+    "headers": true,
+    "length": "short",
+    "ordered_lists": false,
+    "unordered_lists": true
+  },
+  {
+    "code": true,
+    "count": 4,
+    "headers": true,
+    "length": "medium",
+    "ordered_lists": true,
+    "unordered_lists": true
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds that the output is HTML and that the text is placeholder content, providing useful context beyond the annotations without contradicting them.

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 sentence that is front-loaded with the main purpose and immediately lists the configurable options. No wasted words, every phrase earns its place.

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?

The tool is simple (generation, no side effects) and benefits from strong annotations, a complete parameter schema, and an output schema. The description fully covers the tool's purpose and key options, making it complete for an agent to invoke correctly.

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?

Input schema has 100% coverage with detailed descriptions for all parameters, including examples. The description only reiterates the element options (headers, code blocks, lists) without adding new semantics beyond the schema, so a baseline of 3 is appropriate.

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 a specific action ('Generate formatted placeholder text') and enumerates the distinct features (headers, code blocks, unordered/ordered lists). This distinguishes it from the sibling generate_paragraphs tool by highlighting the configurable elements.

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

Usage Guidelines4/5

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

The description provides clear usage context (when you need formatted placeholder text with specific elements) and tells the user to specify which elements to include. It does not explicitly mention alternatives or exclusions, but the sibling generate_paragraphs is implicitly the simpler choice.

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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TDQS

A3.6/5.0
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle natural-language data queries, with ask_pipeworx_beta explicitly duplicating ask_pipeworx. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research) also heavily overlaps, and ai_visibility_check is a single-entity version of scan_competitor_ai_presence. An agent would frequently be unable to tell which tool to select.

Naming Consistency2/5

All names are snake_case, but the pattern is inconsistent: some are verb_noun (generate_llms_txt, resolve_entity), some are bare verbs (forget, recall, subscribe), and several are noun-first domain names (polymarket_edges, pipeworx_trending, entity_profile). There is no uniform verb convention, and the mix makes it hard to predict what a tool does from its name.

Tool Count2/5

At 33 tools, this is well above the 'heavy' threshold and includes several near-duplicates: three ask_pipeworx variants and six polymarket_* tools. While the underlying platform is broad, this meta-layer could be consolidated to 15-20 tools without losing capability.

Completeness4/5

The core data-query workflow is well covered: ask, deep research, entity profile, compare, validate, resolve ID, and search inside documents. The memory lifecycle (remember/recall/forget) and subscription lifecycle (subscribe/list/recent_alerts/unsubscribe) are also complete. However, the set includes unrelated utilities (generate_paragraphs, scan_dependency, generate_llms_txt) that don't belong to the main data domain, and there is no direct tool to execute a raw discovered tool by name.

Resources