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Glama

Visualcrossing

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that results are ready to call directly with full schemas and curated examples, no second lookup needed. No contradictions.

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?

Front-loaded with main verb. Lists many domains which could be condensed, but remains informative. Not overly verbose, but could be more concise.

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?

No output schema, but description compensates by detailing what the tool returns: names, descriptions, full schemas with examples. Covers purpose, usage, and behavior completely for a discovery tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and description adds meaning: explains aliases for query, default/max limit, and purpose of query parameter. Provides context beyond schema definitions.

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?

Description clearly states the tool's purpose: 'Find tools by describing the data or task.' It lists specific domains and explains that it returns top-N relevant tools with full schemas, distinguishing it from sibling tools which are specific data actions.

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?

Explicit guidance: 'Call this FIRST when you have many tools available and want to see the option set.' Provides examples of when to use (browse, search, look up). Lacks explicit 'when not to use', but context is clear.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Even tools with overlapping domains (e.g., ask_pipeworx and deep_research) are differentiated by use case: single lookups vs multi-faceted research. Weather, Polymarket, memory, and subscription tools are completely separate, and descriptions clarify any potential confusion.

Naming Consistency4/5

Tool names mostly follow a verb_noun pattern, but some are single verbs (forget, recall) or noun_noun (entity_profile, weather_timeline). The mix is noticeable but still predictable and readable, with consistent snake_case formatting throughout.

Tool Count4/5

With 34 tools, the server is large but each tool serves a specific function within the broad data-access domain. The count is justified given the wide range of domains (weather, company data, prediction markets, memory, subscriptions, etc.), though it is on the higher end for typical MCP servers.

Completeness4/5

The tool surface covers a wide range of operations: data retrieval, comparison, fact-checking, weather, prediction markets, memory, subscriptions, and meta-tools. There are no obvious gaps for the intended use of a unified data gateway, though some niche data sources might not be directly addressed.

Resources