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Duffel

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

A4.6/5.0
Behavior5/5

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

Annotations already include readOnlyHint, idempotentHint, destructiveHint. The description adds significant behavioral context: it returns top-N most relevant tools with full schemas and curated examples, ready to call directly. This goes beyond what annotations provide and enhances transparency.

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 informative and front-loaded with purpose. It efficiently covers usage, input, and output in a few sentences. While slightly verbose with example lists, every sentence adds value. Could be slightly more concise, but overall well-structured.

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?

The description fully explains what the tool does, when to use it, and what it returns (top-N tools with names, descriptions, full schemas, examples). No output schema exists, but the description compensates by detailing the return format. The context is complete for an agent to use the tool correctly.

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 baseline is 3. The description groups aliases for the query parameter but adds no new semantic meaning beyond what the schema provides. The description focuses on overall tool behavior rather than parameter specifics.

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 many example tasks and data sources, making the functionality explicit. It distinguishes itself from sibling tools by being the discovery tool that helps navigate the toolset.

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 provides explicit when-to-use guidance: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' It also describes scenarios like browsing, searching, or looking up tools, which is precise and directive.

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

A3.6/5.0
Disambiguation3/5

The set is organized into clusters (Pipeworx querying, Polymarket analysis, entity research, subscriptions, memory), but several tools within a cluster have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, deep_research, and validate_claim all answer questions, and polymarket_edges, bet_research, and polymarket_arbitrage all surface trading opportunities. The very detailed descriptions help an agent choose correctly, but the boundaries are not crisp enough for a 4.

Naming Consistency3/5

Most names are lowercase snake_case and there are consistent prefixes like polymarket_ and pipeworx_, which aids predictability. However, the verb/noun ordering is inconsistent across the set (ask_pipeworx, bet_research, entity_profile, duffel_flight_search, ai_visibility_check), and some names are noun-heavy while others are verb-first. It is readable but not a uniform convention.

Tool Count2/5

At 32 tools the surface feels heavy for a coherent server; the rubric treats 25+ as too many. Several tools are wrappers or variants of the same underlying capability (ask_pipeworx_beta, polymarket_edges vs bet_research, ai_visibility_check vs scan_competitor_ai_presence), so the count overstates real functional breadth.

Completeness3/5

The research/data side is very complete: querying, grounded verification, deep research, entity resolution, comparison, profiles, monitoring, memory, and feedback are all covered. However, the Duffel flight tool only searches and never books, so if the server is meant to be a flight agent there is a notable dead end; the broader toolkit also lacks direct CRUD for most resources beyond subscriptions.

Resources