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Glama

randomuser

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

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

  1. Changed5 schema fields changed
    • addedInput schema / properties / description
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / q
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • changedInput schema / properties / query / description
      Previous value: -"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\")"New value: +"Natural language description of what you want to do (e.g., \"analyze housing market trends\", \"look up FDA drug approvals\", \"find trade data between countries\"). Accepts task, q, description, search as aliases."
    • addedInput schema / properties / search
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
    • addedInput schema / properties / task
      Added value: +{
+  "description": "Alias for query.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "look up FDA drug approvals"
+  },
+  {
+    "query": "analyze housing market trends"
+  }
+]
  3. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context: it returns top-N tools with names, descriptions, and full input schemas with curated examples, ready to call directly without a second schema lookup, and it emphasizes a first-step workflow. It does not address rate limits or edge cases, but goes beyond the annotation baseline.

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?

The description is four sentences, front-loaded with the primary purpose, and each sentence earns its place: use-case list, return behavior, and workflow guidance. The domain list is long but directly relevant; there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the burden of explaining return values—it does so clearly: top-N relevant tools with names, descriptions, full schemas, and examples. The compound parameters are fully covered by the schema, and annotations handle safety. It lacks only explicit ranking/sorting details, but overall it is complete enough for an agent to select and 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?

Schema description coverage is 100%, with all params clearly documented including aliases and defaults. The description confirms that 'query' is a natural-language task description but does not add meaning beyond the schema, 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 clearly states a specific verb-resource relationship: 'Find tools by describing the data or task.' It enumerates the domains it covers and explicitly frames the tool as a discovery/browse/search mechanism, distinguishing it from domain-specific sibling tools like search_within or entity_profile.

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 explicit use cases ('browse, search, look up, or discover') and a directive to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It does not name specific alternative tools or offer a formal when-not-to-use list, so it falls short of a 5.

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 and ask_pipeworx_beta are explicitly described as identical, and ask_pipeworx_grounded differs only in grounding. generate_users and generate_by_gender overlap, as do ai_visibility_check/scan_competitor_ai_presence and the several polymarket_* tools that all target edge detection and arbitrage. An agent would struggle to select the correct tool.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some start with verbs (generate_users, resolve_entity, validate_claim), some are nouns (entity_profile, deep_research, recent_changes), and some are compound noun phrases (ai_visibility_check, pipeworx_feedback, polymarket_arbitrage). There is no predictable verb_noun structure across the set.

Tool Count2/5

33 tools is well above the 'too many' threshold, and the set includes many meta-tools, memory helpers, and niche prediction-market tools. While the broad domain might justify some diversity, the count feels bloated and dilutes the server's focus, especially given the server name suggests only random user generation.

Completeness4/5

For the apparent core domain (data lookups, research, prediction market analysis, subscriptions), the tool surface is quite comprehensive: it covers direct queries, grounded answers, deep research, entity profiles, comparisons, claim verification, discovery, trends, memory, and subscription management. Minor gaps exist (e.g., no direct CRUD for user-generated profiles beyond creation), but overall the feature set feels well covered.

Resources