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lorem

by io.github.pipeworx-io

Generate Paragraphs

generate_paragraphs
Read-onlyIdempotent

Generate placeholder text paragraphs for mockups and layout testing. Returns plain text without formatting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of paragraphs to generate (1–10)
lengthYesLength of each paragraph: short, medium, long, or verylong

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesFull plain text output
countYesNumber of paragraphs generated
lengthYesLength of each paragraph
paragraphsYesArray of individual paragraphs

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 generated",
+      "type": "number"
+    },
+    "length": {
+      "description": "Length of each paragraph",
+      "enum": [
+        "short",
+        "medium",
+        "long",
+        "verylong"
+      ],
+      "type": "string"
+    },
+    "paragraphs": {
+      "description": "Array of individual paragraphs",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "text": {
+      "description": "Full plain text output",
+      "type": "string"
+    }
+  },
+  "required": [
+    "count",
+    "length",
+    "text",
+    "paragraphs"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "count": 3,
+    "length": "medium"
+  },
+  {
+    "count": 5,
+    "length": "long"
+  }
+]
  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 valuable context that the output is plain text without formatting, which is not captured by annotations. This goes beyond the baseline safety profile.

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 a single sentence that front-loads the verb, resource, and use case, and adds the return format. Every word earns its place; there is no filler or redundancy.

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 with only two parameters and an output schema. The description covers the core purpose, use case, and return format, while the schema handles parameter details and examples. No significant information gaps remain.

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?

Schema description coverage is 100% for both parameters ('count' and 'length'), including types, ranges, and enum values. The description adds no new parameter-level information, so the baseline of 3 applies.

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 uses a specific verb ('Generate') and resource ('placeholder text paragraphs'), with an explicit purpose ('for mockups and layout testing') and return type ('plain text without formatting'). This clearly distinguishes it from sibling tools like generate_with_options or generate_llms_txt.

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?

It provides clear context for when to use the tool ('for mockups and layout testing'), but does not explicitly mention alternatives or when not to use it. No exclusions are given, but the purpose is specific enough to guide selection.

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