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Glama

deckofcards

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations confirm safe read-only behavior (readOnlyHint, destructiveHint false), and the description adds rich behavioral detail: the fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail due to PatentsView API sunset, and the return structure (changes[], total_changes, citation URIs). This goes well beyond the structured fields.

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 front-loaded with natural language triggers, then efficiently explains the parallel fan-out, source fallback logic, input formats, output structure, and alternative tool. Every sentence provides necessary context, and the formatting makes it scannable.

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 complexity of multiple data sources, fallback behavior, and no output schema, the description fully covers the return format, source behavior, and parameter options. It also provides a pointer to the alternative tool, making it complete and actionable for an agent.

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

Parameters3/5

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

Schema description coverage is 100%, with each parameter already documented in the input schema (e.g., 'since' accepts ISO dates and relative shorthand). The description repeats these formats but adds no new parameter semantics beyond what the schema provides, so the baseline score of 3 applies.

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 identifies the tool as a change feed for companies, with concrete examples of natural language queries ("What's new with X", "updates on Acme"). It distinguishes itself from the sibling entity_profile by specifying when to use the static profile instead.

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?

Explicitly states when to use this tool versus the alternative: 'Use entity_profile instead when you want the static profile...' It also describes the fallback logic between GDELT and GNews, giving the agent clear context on how the tool behaves under rate-limiting.

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

All deckofcards toolsInspect
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TDQS

B3.4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are functionally identical (beta just has experimental routing), and the Polymarket suite (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all revolve around prediction-market signal detection with unclear boundaries. Also ai_visibility_check and scan_competitor_ai_presence overlap heavily.

Naming Consistency2/5

Naming is a mix of verb-first (draw_cards, resolve_entity, discover_tools) and noun-first (entity_profile, new_deck, recent_alerts) styles, with inconsistent prefixes (pipeworx_feedback vs ask_pipeworx) and no unifying convention. Some tools are bare verbs (recall, remember), others are noun phrases (polymarket_edges).

Tool Count2/5

34 tools is excessive for a server named 'deckofcards' — only 3 tools relate to cards. Even as a general-purpose data/research server, 34 is on the high end and includes many near-duplicates (ask_pipeworx variants) and highly specialized tools that could be consolidated.

Completeness3/5

The research side is fairly complete (lookup, grounding, comparison, profiles, claim verification, memory, subscriptions), but the card-deck functionality is minimal (only create, draw, shuffle) and lacks any deck inspection or hand management. The server's scope is unclear, making it hard to assess true coverage.

Resources