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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.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, non-destructive. The description adds that results include full schemas and are ready to call directly, which is beyond the annotations and helpful.

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?

The description is front-loaded with the core purpose and provides domain examples and benefits without being overly verbose. It is well-structured for quick comprehension.

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 clearly explains the return value (tools with names, descriptions, schemas, examples) and covers the purpose, usage, and behavior comprehensively.

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 all parameters described. The description provides natural language examples and explains aliases, adding value over the schema alone.

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 finds tools by describing data or tasks, lists many domains, and explicitly says 'Call this FIRST' for browsing, distinguishing it from sibling tools that perform specific functions.

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?

The description explicitly tells when to use it ('when you need to browse, search, look up, or discover what tools exist for' various domains) and advises to call it first. It does not explicitly state when not to use, but the guidance is strong.

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
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx family (standard, beta, grounded) and validate_claim vs ask_pipeworx_grounded may cause some confusion for agents despite detailed descriptions.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, and most are verb_noun structures (e.g., ask_pipeworx, compare_entities, resolve_entity), making them predictable and easy to distinguish.

Tool Count4/5

With 32 tools, the set is larger than ideal but well-justified by the broad scope of data sources and functionalities (email verification, SEC/FDA lookups, prediction market analysis, memory, subscriptions).

Completeness5/5

The tool set covers the full pipeline from data discovery (discover_tools, suggest_questions) to retrieval, analysis, comparison, verification, and monitoring, with no obvious gaps for its intended use cases.

Resources