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List Packs

list_packs
Read-onlyIdempotent

Browse all available Pipeworx packs. Returns pack names, categories, tool counts, and gateway URLs. Use to discover data sources or explore what's available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoFree-text filter matched against pack slugs, descriptions, and tool names/descriptions — not a fixed taxonomy (e.g. "weather", "pesticide", "sec filings"). Omit to list everything.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
packsYes
gatewayYesGateway base URL
total_packsYes

TDQS

A4.2/5.0
Behavior4/5

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

Given annotations already declare readOnlyHint=true and destructiveHint=false, the description adds behavioral value by disclosing the return fields (pack names, categories, tool counts, gateway URLs) and the exploratory use case. It does not describe any limitations (e.g., pagination) but the provided annotations and return-field disclosure are sufficient context. No contradiction with annotations.

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 two sentences, front-loaded with the main action and purpose, followed by a concise usage note. Every sentence provides value with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple listing tool with one optional parameter and an output schema, the description covers the main purpose, return contents, and usage intent. The schema fills in the parameter details. It is not overly verbose but sufficient for an agent to understand the tool's scope. It could have hinted at the optional category filtering in the description, but the schema covers this, so completeness is high.

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?

The schema has 100% coverage for the single optional 'category' parameter, with a detailed description explaining it is a free-text filter and not a fixed taxonomy. The description itself does not mention this parameter, but because the schema fully documents it, the baseline score of 3 is appropriate.

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 uses a specific verb ('Browse') and resource ('Pipeworx packs'), and further specifies the return contents (pack names, categories, tool counts, gateway URLs). The phrase 'all available' distinguishes it from sibling tools like search_packs, which implies a search-oriented alternative.

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 clearly states when to use the tool ('Use to discover data sources or explore what's available'), providing suitable context. It does not explicitly mention exclusions or alternatives, but the 'all available' wording implies a browse-all scenario, making it distinct from search tools. This is clear context but lacks explicit when-not guidance.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the central data access tools like ask_pipeworx, deep_research, entity_profile, and compare_entities. However, the multiple polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) and the similar ask_pipeworx variants could cause confusion, especially for an agent quickly scanning options.

Naming Consistency4/5

Tool names are mostly snake_case and follow a verb_noun pattern (e.g., compare_entities, search_packs, resolve_entity). Some deviations exist, such as pipeworx_feedback, polymarket_arbitrage (starting with a noun), and single-word names like forget and remember, but overall the style is readable and consistent.

Tool Count2/5

With 36 tools, the server feels overly heavy. While the broad domain (structured data across many sources) justifies a large number, the count exceeds the recommended 15–25 range, making it unwieldy for agents to navigate efficiently without extensive discovery.

Completeness4/5

The tool set covers a wide range of domains: company financials, drugs, economics, prediction markets, weather, and even MCP discovery. There are few obvious gaps given the stated purpose, though some areas like social media or international data could be added. Overall, the surface is well-rounded.