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Slack_connect

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

TDQS

A4.8/5.0
Behavior5/5

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

Describes fan-out behavior, fallback mechanism (GDELT to GNews), and soft-fail for USPTO due to API sunset. Annotations already declare readOnlyHint, idempotentHint, etc., and description adds valuable behavioral context without contradiction.

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?

Packed with information in a single paragraph, front-loaded with example queries. Slightly dense but no wasted words; could be broken into shorter sentences for readability.

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 and complex behavior (multiple sources, fallback, soft-fail), description fully explains what the tool returns (changes grouped by source, total_changes count, URIs) and handles edge cases (USPTO soft-fail). No gaps.

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%. Description adds context for `since` (ISO/relative shorthand, examples) and `value` (ticker or CIK), going beyond schema descriptions. Could provide slight more detail on `type` but it's minimal.

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?

Description starts with example user queries and clearly states the tool provides a change feed for a company in a time window, fanning out to multiple sources. It distinguishes from sibling entity_profile by specifying what entity_profile is for.

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 provides usage examples ('What's new with X') and directly tells when to use the alternative entity_profile tool ('Use entity_profile instead when you want the static profile... regardless of window'). This gives clear guidance on tool selection.

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

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