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genderize

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

A4.2/5.0
Behavior4/5

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

Annotations already establish it as a safe, read-only, idempotent operation. The description adds meaningful behavioral detail about the return format (top-N tools, names, descriptions, full schemas, curated examples) and that results are directly callable. No contradiction with annotations.

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 well-structured and front-loaded with the core purpose, followed by usage contexts, return behavior, and a clear recommendation to call first. Every sentence adds value, and the domain list is relevant but not redundant. It is appropriately sized for the information conveyed.

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?

Despite lacking an output schema, the description compensates by explaining what will be returned (top-N tools with names, descriptions, and full input schemas) and how to use the results directly. It covers the essential context for a meta-tool with six parameters, though it could briefly mention the response structure more explicitly.

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?

The input schema has 100% description coverage, including aliases and constraints for 'limit'. The description itself adds little beyond the schema's already thorough parameter documentation, so it meets the baseline but does not elevate it.

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 explicitly states 'Find tools by describing the data or task' with a specific verb and resource, and lists many example domains. It clearly distinguishes itself from its research-oriented siblings by positioning itself as the meta-tool for discovering tools, not performing the tasks directly.

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?

Provides explicit when-to-use guidance: 'Use when you need to browse, search, look up, or discover what tools exist...' and 'Call this FIRST when you have many tools available and want to see the option set.' It stops short of naming alternative tools or explicit when-not-to-use scenarios, hence not 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

A3.8/5.0
Disambiguation2/5

Several tools route the same style of query to the same Pipeworx catalog: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in purpose, and ask_pipeworx_beta is explicitly identical to ask_pipeworx. The prediction-market tools also blur together, with bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all covering overlapping analysis territory.

Naming Consistency3/5

The tools are consistently lowercase snake_case, but the naming convention is mixed: some are verb_noun (predict_gender, generate_llms_txt), some are noun phrases (entity_profile, recent_alerts), some are bare verbs (remember, forget), and many share domain prefixes like ask_pipeworx or polymarket_. It is readable, but there is no single predictable pattern across the set.

Tool Count2/5

At 33 tools, this exceeds the 25+ threshold where the surface becomes hard to navigate. More importantly, the count does not match the server's apparent genderize identity: the vast majority of tools are unrelated Pipeworx research, prediction-market, memory, and subscription utilities bolted onto a two-tool gender-prediction core.

Completeness3/5

As a broad research assistant, the set is substantial: it covers question routing, grounded verification, deep research, entity profiles, comparisons, change feeds, memory, and subscriptions. However, the actual genderize domain is thin—just two prediction tools with no batch, supported-country, or accuracy endpoints—and several unrelated capabilities feel bolted on, making coverage uneven.

Resources