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Linear

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, etc.), the description explains multi-source fan-out (SEC, GDELT/GNews, USPTO), fallback behavior (GDELT→GNews), and soft-fail for USPTO. 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 a single, well-structured paragraph front-loaded with user-intent examples. Every sentence adds meaningful detail without redundancy. Approximately 100 words, efficient.

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?

Despite no output schema, the description specifies return format (structured changes[] grouped by source, total_changes count, citation URIs). It addresses error handling (soft-fail) and provides context on data sources. Complete for a tool of this complexity.

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 covers all parameters with descriptions (100% coverage). The description adds practical examples for 'since' (e.g., '7d', '30d') and clarifies 'value' accepts ticker or CIK. This improves usability beyond 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 the tool provides a change feed for a company over a time window, with examples of queries. It distinguishes from entity_profile by explicitly contrasting static profiles vs. change feeds.

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 provides clear guidelines on when to use this tool (dynamic change feed) vs. entity_profile (static profile), and specifies input formats for 'since'. However, it doesn't exhaustively list all alternative tools or cases when not to use.

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 within their domains (e.g., Polymarket tools are well-separated). However, a few tools like ask_pipeworx, deep_research, and suggest_questions could cause minor confusion, as they all deal with querying data.

Naming Consistency3/5

Tools from the same service use consistent prefixes (linear_, polymarket_, pipeworx_), but the overall naming style is mixed: some are verb_noun (linear_create_issue), some are noun_verb (bet_research), and some are single words (remember). This inconsistency reduces predictability.

Tool Count3/5

With 35 tools, the server covers a broad range of functionality (data query, prediction markets, memory, etc.). While not excessive, the count is on the higher side, and the server name 'Linear' suggests a narrower focus, which may mislead expectations.

Completeness4/5

The tool set covers core data querying, research, entity profiles, prediction market analysis, and memory operations comprehensively. Minor gaps exist (e.g., limited Linear CRUD), but the overall surface feels complete for its intended use as a data assistant.

Resources