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Glama

ureweries

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds valuable behavioral context: it returns 'top-N most relevant tools with names, descriptions, and full input schemas (with curated examples)' and emphasizes 'no second schema lookup needed.' This explains the output format and the tool's efficiency, going beyond the annotations without contradicting them.

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: first sentence defines purpose, second gives usage context, third explains return value, and fourth provides a strategic directive. Each sentence earns its place, and the long list of domains is informative rather than filler. It is front-loaded with the essential purpose and remains concise despite the rich content.

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?

Despite lacking an output schema, the description explicitly explains what the tool returns (top-N tools with schemas and examples) and how to use it. The annotations cover safety, and the schema covers parameters. For a tool discovery tool, this description is complete: it provides the necessary context for an agent to invoke it correctly and understand the result format.

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 detailed descriptions for query and limit, including aliases. The description's mention of 'top-N' aligns with the limit parameter but doesn't add new parameter-level detail beyond the schema. Since the schema fully documents all parameters, the description provides no additional semantic value, warranting the baseline score of 3.

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 with a specific verb ('Find tools') and resource (tools by describing data or task). It distinguishes itself from sibling tools by being a meta-tool for discovery, while siblings are domain-specific (e.g., search_breweries, deep_research). The scope of domains listed further clarifies what it covers.

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?

The description explicitly states when to use the tool ('when you need to browse, search, look up, or discover what tools exist') and provides a strong directive: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This gives clear usage context and a hint about alternatives (other tools that might be selected after discovery).

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
Disambiguation4/5

Most tools have clear distinct purposes, but ask_pipeworx, ask_pipeworx_grounded, and deep_research overlap in querying Pipeworx data sources. Entity_profile and recent_changes also share some coverage. Overall, agents can differentiate, but a few pairs may cause confusion.

Naming Consistency3/5

Tool names use snake_case for multi-word (e.g., ai_visibility_check) but also single-word verbs (forget, recall, remember). The pattern is not uniform: some are verb_noun, some are just noun or verb. Mixed conventions reduce predictability.

Tool Count3/5

At 33 tools, the set is on the high side for an MCP server. The server covers a broad data-query domain, which justifies many specialized tools, but the count is borderline heavy and includes several meta-tools (discover_tools, suggest_questions). Could be streamlined without losing core functionality.

Completeness4/5

The tool surface is comprehensive for data retrieval across financial, economic, pharmaceutical, real estate, weather, and prediction markets. It includes both single-lookup and comparative tools, plus monitoring via subscriptions. Minor gaps exist (e.g., no direct update/delete for user memory beyond forget), but the core domain is well-covered.

Resources