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Glama

Barcelona Events

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

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. The description goes beyond by detailing the fan-out to specific data sources, fallback logic (GDELT to GNews on rate limits/5xx), soft-failure of USPTO due to API sunset, and the return structure (changes[] grouped by source, total_changes, 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.

Conciseness5/5

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

The description is information-dense yet well-organized. It opens with query examples, then explains data sources and fallback, parameter details, return format, and comparison with a sibling tool. Every sentence adds value without redundancy.

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, API sunset, no output schema), the description covers all necessary aspects: what it does, how it handles failures, parameter formats, return structure, and when to use an alternative. No gaps remain.

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 coverage is 100%, establishing a baseline of 3. The description adds significant meaning: for 'since' it explains ISO date vs. relative shorthand with examples, for 'value' it specifies ticker or CIK, and for 'type' it notes only 'company' is supported. This enhances understanding beyond the schema.

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 time window, fanning out to multiple sources (SEC, GDELT, GNews, USPTO). It explicitly distinguishes from the sibling tool 'entity_profile' by stating when to use that instead, meeting the high bar of specificity and differentiation.

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 gives explicit examples of queries and provides clear context for when to use this tool ('What's new with X') vs. alternatives ('Use entity_profile instead'). It also offers parameter guidance (e.g., 'Use "30d" or "1m" for typical monitoring') and explains fallback behavior.

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

A3.7/5.0
Disambiguation2/5

There is heavy overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the five polymarket_* tools plus bet_research all target prediction-market analysis with unclear boundaries. Many other tools (entity_profile, compare_entities, recent_changes, resolve_entity) also partially cover company lookups, making it hard to pick the right one.

Naming Consistency4/5

The vast majority follow a clear snake_case verb_noun pattern (compare_entities, resolve_entity, generate_llms_txt, validate_claim, unsubscribe). Minor deviations exist (entity_profile, events, pipeworx_trending are noun-first), but the pattern is predictable and readable overall.

Tool Count2/5

32 tools is far too many for a server ostensibly about Barcelona events—only one tool (events) is actually on-topic. The count would be heavy even for a general-purpose data/research server, and for the stated purpose it is extreme scope creep.

Completeness1/5

Relative to the server's stated purpose (Barcelona events), the surface is severely incomplete: only a single read-only lookup tool exists, with no management, modification, or richer event coverage. While the unrelated Pipeworx/prediction-market tools have broad coverage individually, that does not serve the Barcelona Events domain at all.

Resources