generate_personGenerate fictional person record(s) — name, email, phone, address, company, job title, birthdate, uuid — for testing/seeding (keyless, offline). All data is FAKE.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | How many (1-100, default 1). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "count": 1
+ },
+ {
+ "count": 5
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive. The description adds behavioral context: data is fake, keyless, and offline, which goes beyond annotations. No contradiction noted.
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?
One concise sentence that front-loads the action and includes a comprehensive field list and key constraints. No redundant information.
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?
With a single parameter and no output schema, the description compensates by listing output fields and clarifying the offline/fake nature. Fully sufficient 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers the single 'count' parameter with description and range (1-100, default 1), so description adds no additional parameter semantics. Baseline 3 is appropriate when schema coverage is 100%.
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 uses the verb 'Generate' with a specific resource 'fictional person record(s)' and enumerates fields (name, email, phone, address, company, job title, birthdate, uuid), clearly distinguishing it from sibling 'lorem' which generates generic placeholder text.
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?
Explicitly states 'for testing/seeding (keyless, offline)' and 'All data is FAKE,' indicating it's not for real-world use. It doesn't mention alternatives, but the context is clear and excludes production usage.
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.