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Glama

Openverse

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. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that results include full input schemas with curated examples and are ready to call directly, which is valuable context beyond 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 compact yet comprehensive, with examples and clear guidance. Every sentence adds value, and it is well-structured for quick understanding.

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?

Given the tool's role as a discovery tool, the description covers usage, examples, and output behavior (returns top-N tools with schemas). No output schema is needed, so completeness is high.

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%, so the schema already documents all parameters. The description briefly mentions aliases but does not add significant meaning beyond what the schema provides.

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 the tool's purpose: 'Find tools by describing the data or task.' It lists specific use cases (SEC filings, financials, etc.) and distinguishes from siblings by focusing on tool discovery rather than direct data retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance provided: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also includes examples of when to use, making it easy for the agent to decide.

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

A3.9/5.0
Disambiguation2/5

Several tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions from Pipeworx data, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market tools also heavily overlap in purpose, as do ai_visibility_check and scan_competitor_ai_presence. Only the Openverse media tools and memory/subscription tools are cleanly distinguishable.

Naming Consistency3/5

All names are lowercase snake_case, but the naming conventions are mixed: verb_noun tools like search_images and resolve_entity coexist with bare verbs like remember, recall, forget, and subscribe, plus noun compounds like entity_profile, polymarket_edges, and bet_research. Subfamilies such as polymarket_* and the audio/image tools are internally consistent, but there is no single predictable pattern across the full set.

Tool Count2/5

37 tools exceeds the 25+ threshold and feels inflated for the surface, especially since several could be consolidated: there are three ask_pipeworx variants and five overlapping prediction-market tools. The Openverse-specific core is only 6 tools, with 31 mostly unrelated Pipeworx and utility tools attached, making the server feel like a grab-bag rather than a purpose-built Openverse integration.

Completeness4/5

Each embedded subdomain covers its main lifecycle well: Openverse has search/get/related for images and audio, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and research has ask, grounded, deep_research, entity_profile, compare_entities, resolve_entity, and validate_claim. Minor gaps exist—notably no Openverse video/collection tooling and no explicit memory update—but agents can work around them without hitting dead ends.

Resources