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Slack_connect

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark it as read-only, open-world, idempotent, and non-destructive. Description adds behavioral details: returns live catalog examples with tool shapes, and mentions optional topic filtering. 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.

Conciseness5/5

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

Description is well-structured: starts with a clear summary, lists categories, and ends with usage advice. Every sentence is meaningful, no redundancy.

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?

For an onboarding tool, the description fully explains what it returns (category-bucketed examples with tool shapes), how to customize (topic parameter), and when to use (first interaction). No output schema is needed given the clear description.

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 has 100% coverage for the 'topic' parameter. Description adds value by listing example topic values (finance, pharma, etc.) and explaining the behavior when omitted (full spread).

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 is for onboarding, returning example questions and tool arguments. It specifies the resource (Pipeworx) and the action (suggest questions), and distinguishes it from siblings by being the entry point.

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?

Explicitly states 'Use this FIRST' when unsure what Pipeworx can do, and gives context about returning category-bucketed examples. No explicit when-not-to-use, but the guidance is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Slack_connect toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes, particularly the multiple 'ask_pipeworx' variants and various Polymarket tools that serve similar functions. The lack of clear boundaries between data retrieval tools makes it difficult for an agent to choose the right one.

Naming Consistency2/5

Naming patterns are inconsistent: Slack tools use a 'slack_' prefix, while Pipeworx tools use a mix of verbs (ask_, validate_, resolve_) and nouns (entity_profile, bet_research). No uniform convention is applied across the set.

Tool Count3/5

With 36 tools, the count is high but not unreasonable for a comprehensive data platform. However, the inclusion of only 5 Slack tools in a server named 'Slack_connect' indicates a mismatch between tool count and intended purpose.

Completeness1/5

For a Slack integration, the tool surface is severely incomplete—missing core operations like creating channels, archiving, reactions, or message threading. The Pipeworx tools are extensive but unrelated to the server's stated purpose.

Resources