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Tradier

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds behavioral details beyond annotations: fans out to SEC, GDELT/GNews, USPTO with soft-fail, and describes output structure (changes[] grouped by source, total_changes count, citation URIs). No contradiction.

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 concise (about 100 words) and front-loaded with user intents. Every sentence adds value, with no redundancy or wasted words.

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?

Given the tool has 3 parameters with thorough schema descriptions, no output schema, and safety annotations, the description adequately explains the multi-source behavior, fallback, and return structure (changes[] grouped by source, total_changes count, citation URIs). It could mention pagination or limits but is sufficient.

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% with descriptions for all 3 parameters. The description adds value by clarifying that 'since' accepts ISO date or relative shorthand (e.g., '7d', '30d', '1y'), suggests '30d' or '1m' for typical monitoring, and explains that 'value' can be a ticker or zero-padded CIK.

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 recent window, with explicit user intents like 'What's new with X' and 'latest on Y'. It distinguishes from sibling tool 'entity_profile', which is for static profiles.

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 (change feed queries) and when-not ('Use entity_profile instead for static profile'). It also explains the fallback mechanism between GDELT and GNews and gives examples of the 'since' parameter format.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all serve general data querying, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_edge_tracker all target prediction-market opportunities. An agent would struggle to pick the right one consistently despite long descriptions.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case bare verbs (remember, recall, forget), some use a pipeworx_ prefix, some use tradier_ prefix, and some use descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). There is no consistent verb_noun or prefix pattern across the set.

Tool Count2/5

34 tools is on the heavy side, but more importantly the count does not match the server's stated identity. The server is named Tradier, yet only 3 of 34 tools are Tradier-specific market data tools; the rest are Pipeworx research, prediction-market, memory, and subscription utilities. The scope feels bloated and unfocused.

Completeness1/5

For a server named Tradier, the surface is severely incomplete: only quote, option expirations, and option chain are provided. Missing are account info, positions, orders, historical data, watchlists, and other core brokerage/trading operations. The Pipeworx research side is broad, but the apparent trading domain has major gaps that would cause agent failures.

Resources