loremGenerate lorem-ipsum placeholder text (keyless, offline). unit = words | sentences | paragraphs (default sentences); count = how many.
| Name | Required | Description | Default |
|---|---|---|---|
| unit | No | words | sentences | paragraphs (default sentences). | |
| count | No | How many units (default 3). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "count": 3,
+ "unit": "sentences"
+ },
+ {
+ "count": 2,
+ "unit": "paragraphs"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds useful context beyond these by noting 'keyless, offline' and specifying that 'unit' defaults to 'sentences,' which enriches the agent's understanding of the tool's operation without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core purpose ('Generate lorem-ipsum placeholder text') and then pragmatically lists the key parameters and defaults. Every word earns its place, and it is appropriately concise for such a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, low-risk tool with full schema coverage and clear annotations, the description covers all necessary aspects: purpose, keyless/offline nature, parameter meanings, and defaults. No output schema is present, but the return value (generated text) is self-evident from the tool's name and description, making this description complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides full descriptions for both parameters ('unit' and 'count') with examples and defaults, achieving 100% schema coverage. The description merely repeats the options and default for 'unit' but adds no additional semantic value beyond what the schema already offers, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb+resource: 'Generate lorem-ipsum placeholder text.' It also distinguishes itself from all sibling tools (which are focused on research, entities, or subscriptions) by being a simple utility for placeholder content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it's a placeholder text generator that is 'keyless, offline,' but it does not explicitly state when to use it or mention any alternatives. Since no similar sibling tool exists, the lack of explicit 'when not to use' is not critical, but the guidance is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but there is overlap within the ask_pipeworx family (beta, grounded) and Polymarket tools (arbitrage, edges, fill_risk), which could cause confusion. Detailed descriptions mitigate this, but the boundaries are not always clear.
Tool names use snake_case but lack a consistent verb_noun pattern. Some are imperative (discover_tools), others are descriptive (ask_pipeworx, bet_research), and some are noun phrases (entity_profile, recent_alerts). This inconsistency makes it harder to predict tool names.
33 tools is on the higher side for a single server, but the broad scope (company data, prediction markets, memory, etc.) partially justifies the count. However, many tools are variations of core functionality, suggesting possible consolidation.
The tool set covers a wide range of data sources and tasks, including company profiles, comparisons, economic data, and prediction markets. The universal ask_pipeworx router fills most gaps, though some niche data sources might not be directly accessible.