Skip to main content
Glama

Elexon

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").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds details about data sources (SEC, GDELT/GNews, USPTO), fallback logic, date formats, and return structure. 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.

Conciseness4/5

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

The description is a single dense paragraph but efficiently packs all necessary information. It is front-loaded with examples, though could be slightly more structured with bullet points.

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 explains the return structure (changes[], total_changes, citation URIs). It covers sources, fallbacks, and date handling comprehensively, making the tool's behavior fully understandable.

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%, and the description adds value by explaining 'since' formats with examples, noting only 'company' is supported for type, and providing CIK/ticker examples for value. Not critical but helpful.

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, covering SEC filings, news, and patents. It gives example queries and distinguishes from entity_profile, making the purpose unambiguous.

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 guidance on when to use this tool vs. entity_profile, and gives example queries. However, it does not contrast with other sibling tools like deep_research or recall.

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

All Elexon toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx_grounded/deep_research heavily overlap as high-level routing entry points, and the five Polymarket tools (edges, arbitrage, edge_tracker, fill_risk, bet_research) cover closely related concerns. The descriptions are detailed, but an agent can easily select the wrong entry point.

Naming Consistency3/5

All names are snake_case and readable, but conventions are mixed: verb-first names (query_dataset, validate_claim, discover_tools) coexist with noun-phrase names (system_demand, entity_profile, recent_changes), and prefix families are applied inconsistently (elexon_* and polymarket_* exist, but bet_research, generation_by_fuel, and system_demand have no prefix). The pattern is understandable but not predictable enough to be considered consistent.

Tool Count2/5

36 tools is well above the 25-tool threshold for a heavy surface, and many tools are orthogonal to the nominal Elexon scope: memory (remember/recall/forget), subscriptions, npm dependency scanning, and llms.txt generation. The count forces significant discovery overhead and makes the set feel bloated rather than well-scoped.

Completeness4/5

The Elexon core is solid: elexon_list_datasets plus query_dataset covers all 84 BMRS datasets, with direct shortcuts for system prices, generation by fuel, and system demand. The broader Pipeworx side also covers research, entity resolution, prediction-market analysis, memory, and subscriptions without obvious dead ends, though a few minor gaps exist such as limited non-npm dependency scanning and no direct Elexon-specific tools for every dataset family.

Resources