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Glama

Energi Data Dk

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe read-only operation; description adds valuable behavioral details such as fan-out to multiple sources, rate-limit handling via fallback, and structural output (changes grouped by source, total_changes count, citation URIs). 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.

Conciseness4/5

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

Description is dense but each sentence earns its place. Front-loaded with example queries. Slightly long but efficient for the breadth of information conveyed.

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 (multiple sources, fallback, error handling), the description covers all necessary aspects: inputs, behavior, output structure, and alternative tool. No output schema but return format is described.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters, but description adds meaningful context: explains that 'since' accepts ISO date or relative shorthand with examples, 'value' can be ticker or CIK, and 'type' is limited to 'company'. This goes beyond schema descriptions.

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 it's a 'change feed for a company' with specific data sources (SEC EDGAR, GDELT→GNews, USPTO) and explicitly distinguishes from sibling tool 'entity_profile'.

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?

Provides explicit when-to-use examples ('What's new with X', 'latest on Y') and when-not-to-use ('Use entity_profile instead for static profile regardless of window'). Also explains fallback behavior and soft-fail conditions.

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

A3.9/5.0
Disambiguation2/5

Several tools form overlapping clusters that are hard for an agent to distinguish: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all cover factual/research lookups, and the five Polymarket tools also overlap heavily. ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, making misselection essentially guaranteed in that pair. The descriptions are detailed, but the boundaries between many tools remain unclear at the set level.

Naming Consistency4/5

Almost all tool names are snake_case and mostly follow readable verb_noun or domain-specific patterns, such as ask_pipeworx*, resolve_entity, validate_claim, and polymarket_*. Minor deviations exist — bare verbs like remember/recall/forget/subscribe and noun-style names like spot_prices/co2_intensity — but there is no mixed casing and the overall pattern is predictable.

Tool Count2/5

34 tools is already over the 25+ threshold for a well-scoped server, and the mismatch is much worse because only three tools (co2_intensity, spot_prices, query_dataset) relate to the advertised Energi Data DK domain. The other 31 tools appear to belong to an unrelated general-purpose Pipeworx platform, so the count is not appropriate for the server's stated purpose.

Completeness3/5

For the stated Energi Data DK domain, energy data access is partially covered: two dedicated tools plus query_dataset as a generic escape hatch for all ~100 datasets prevents hard dead ends, but there are no typed tools for most of those datasets and no energy-specific monitoring/alerting. If the real intended domain is the broader Pipeworx platform, coverage is much stronger, but then the server name is misleading.

Resources