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Glama

Eurostat

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

A4.8/5.0
Behavior5/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent with description. Description adds details: parallel calls, source-specific behavior (USPTO soft-fail), and output structure with 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.

Conciseness4/5

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

Description is dense but well-organized: starts with example queries, then details sources, parameters, and output. Each sentence adds value, though slightly verbose.

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?

Covers complexity of multi-source tool with fallback, parameter formats, and output structure. No output schema, but description details return fields. Also references sibling for static profile, making it complete.

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%, but description adds value with exact usage examples (ISO date syntax, relative shorthands like '7d', '30d') and explains value can be ticker or CIK. Exceeds baseline of 3.

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 uses specific verbs like 'change feed' and lists example queries, clearly stating it returns changes from multiple sources in one parallel call. It distinguishes from sibling tool entity_profile by explicitly noting 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 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'), fallback behavior (GDELT→GNews), and directs to entity_profile for static profile. Also gives guidance on since parameter format.

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

All Eurostat toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

The set is heavily overlapped: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all serve similar factual-lookup purposes with fuzzy boundaries. The Polymarket tools and visibility tools also overlap substantially, making tool selection genuinely ambiguous despite long descriptions.

Naming Consistency3/5

All names are lowercase snake_case, which is internally consistent, but there is no predictable verb_noun pattern: verb styles vary wildly (ask, get, list, search, recall, remember, forget, subscribe). The 'ask_pipeworx_beta' suffix also breaks naming convention.

Tool Count1/5

34 tools is far too many for a server named Eurostat, and only 3 of them (get_dataset, list_datasets, search_datasets) actually serve Eurostat data. The rest is a sprawling generic Pipeworx utility surface including prediction markets, memory, subscriptions, visibility checks, and dependency scanning, which is a severe scope mismatch.

Completeness2/5

For the Eurostat-specific portion, search/list/get covers basic dataset retrieval, but the server's broader surface is a grab-bag of unrelated capabilities with no coherent domain. The true domain is unclear, and the Eurostat side lacks deeper operations like metadata lookup or bulk/time-series expansion.

Resources