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Glama

Scryfall

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 already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds substantial behavioral detail: multi-source fan-out (SEC EDGAR, GDELT→GNews, USPTO), fallback mechanism, parameter shorthand support, and return structure (changes[] grouped by source + URI citations).

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 paragraph that front-loads example queries. It is informative without being verbose, but could benefit from structured sections for parameters or data sources. Every sentence contributes meaning.

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, parameter options, no output schema), the description covers essential behavior, parameter details, return structure, and sibling differentiation. It leaves no critical gaps for agent invocation.

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 the description adds value by explaining accepted formats for 'since' (ISO date and relative shorthand with examples), the fact that 'value' accepts ticker or CIK, and a usage recommendation for 'since' (typical monitoring: '30d' or '1m').

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 recent time window, with specific example queries. It explicitly distinguishes from sibling tool entity_profile by contrasting 'change feed' vs 'static 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?

The description provides explicit usage contexts ('What's new with X' queries) and guidance on when to use entity_profile instead. It also explains internal fallback logic between GDELT and GNews, and notes a soft fail for USPTO.

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
Disambiguation3/5

Most tools have distinct purposes, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus deep_research creates real boundary confusion, and the six polymarket_* tools overlap enough to require careful reading. The few Scryfall card tools are clearly distinct from the Pipeworx bulk, but the name mismatch adds selection friction.

Naming Consistency4/5

All tool names use snake_case and most follow a verb_noun pattern (get_card, search_cards, resolve_entity, validate_claim). There are deviations like entity_profile, deep_research, and pipeworx_trending, but the nested families (ask_pipeworx*, polymarket_*) are internally consistent and predictable.

Tool Count2/5

35 tools is above the 25+ threshold and is especially mismatched with the server name 'Scryfall', which implies a focused MTG card server. Only 4 of 35 tools relate to Scryfall; the remaining 31 form a sprawling data-research platform that would be more appropriately split into separate servers.

Completeness4/5

The dominant Pipeworx data-research surface is remarkably complete: universal lookup, grounded answers, deep research, entity profiles, comparisons, entity resolution, claim validation, change feeds, subscriptions, memory, and discovery. The Scryfall subset covers core card lookup (search, get by name, random, list sets) but lacks rulings, set details, and card-by-ID lookups, which is a minor gap relative to the server's stated name.

Resources