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Glama

Newsdata

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

A4.6/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint=false. Description adds that results are ready to call without second schema lookup, which is valuable behavioral 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is fairly comprehensive but not overly long. It front-loads purpose and usage, though some sentences could be tighter. Still efficient.

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 no output schema, the description adequately explains return structure (names, descriptions, schemas). It covers input, purpose, and usage context completely for this tool.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for each parameter, including aliases. The description adds overall context but does not significantly extend parameter 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 it's for finding tools by describing data or task, enumerates many domains, and specifies it returns top-N tools with full schemas. It distinguishes from sibling tools by focusing on discovery.

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?

Explicitly advises 'Call this FIRST when you have many tools available and want to see the option set', which provides clear usage context and when-not-to-use (direct queries).

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
Disambiguation3/5

Most tools have detailed usage guidance, but there are several overlapping entry points: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research; discover_tools vs suggest_questions; entity_profile vs recent_changes; and ai_visibility_check vs scan_competitor_ai_presence. The descriptions help, but the boundaries are not always crisp enough to prevent misselection.

Naming Consistency3/5

Names are mostly lower_snake_case, but conventions vary widely: some are verb-first (list_subscriptions, resolve_entity), some are noun/adjective phrases (recent_changes, entity_profile), and several use brand prefixes (pipeworx_trending, polymarket_arbitrage). The naming is readable but lacks a single predictable pattern.

Tool Count2/5

34 tools is well past the 25+ threshold and the scope sprawls beyond news/data into prediction-market arbitrage, npm dependency scanning, AI visibility audits, memory, subscriptions, and llms.txt generation. Each tool may be useful, but the set feels like several different servers merged into one, making it oversized and harder to navigate.

Completeness4/5

The research workflow is well covered: universal routing, grounded verification, deep research, entity resolution/profiles/comparisons, change feeds, discovery/onboarding, subscriptions, and memory all exist. Minor gaps include no direct pipeworx:// citation fetch tool and no broader account/profile management, but agents can work around these.

Resources