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Entso E

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
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it reveals parallel fan-out to multiple APIs, fallback logic, soft-fail for USPTO, and return structure (changes[] grouped by source). This fully informs the agent of the tool's behavior.

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?

The description is well-organized: begins with example queries, then explains the tool's behavior, parameter details, return format, and alternative tool. While slightly verbose, every sentence adds value and there is no extraneous information.

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 the tool's complexity, no output schema, and multiple data sources, the description covers all essential aspects: acceptable inputs, data sources, fallback behavior, return format, and relationship to sibling tool. It is complete enough for confident tool selection.

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?

The input schema already describes all parameters (100% coverage), but the description adds valuable semantics: examples of 'since' formats (ISO date and relative shorthand with monitoring suggestion), clarification that 'value' accepts ticker or CIK, and guidance to use '30d' or '1m' for typical monitoring.

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's purpose: a change feed for a company over a specified time window, aggregating SEC, GDELT/GNews, and USPTO data in one parallel call. It also distinguishes itself from the sibling 'entity_profile' tool by specifying when to use the 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 provides explicit example queries ('What's new with X', 'latest on Y') and explains when to use the sibling 'entity_profile' instead. It details fallback behavior (GDELT->GNews) and acceptable input formats, but does not explicitly list scenarios where the tool should not be used.

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

All Entso E toolsInspect
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TDQS

B3.3/5.0
Disambiguation2/5

The ENTSO-E energy tools are clearly distinct, but the Pipeworx half contains overlapping query modes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx/ask_pipeworx_grounded/deep_research/validate_claim all route natural-language questions to the same underlying catalog. The Polymarket tools also blur edge detection, arbitrage, fill-risk, and persistence tracking, so an agent can easily select the wrong one.

Naming Consistency2/5

The five ENTSO-E tools use a clean snake_case noun pattern, but the rest mix brand verbs (ask_pipeworx, bet_research), bare memory verbs (remember, recall, forget), and polymorphic prefixes (polymarket_*), with inconsistent suffixes like beta, grounded, and kalshi_spread. There is no server-wide predictable naming convention.

Tool Count2/5

36 tools is too many for the apparent scope, and the server name promises ENTSO-E while only 5 of 36 tools serve that domain. Even viewed as a general data utility, the count is heavy and includes duplicate query modes, though individual clusters do have some purpose.

Completeness3/5

For an ENTSO-E server, the five energy tools cover the basics (generation, load, price, flow, capacity) but omit common datasets like generation forecasts, balancing/imbalance prices, and outages. The unrelated Pipeworx tools add broad research, memory, and subscription coverage, but the overall surface feels like a general-purpose assistant with an energy add-on rather than a complete energy domain.

Resources