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Glama

Crypto Fng

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

A4.7/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context on multi-source fan-out, fallback behavior (GDELT→GNews), soft-fail for USPTO, and return format. However, the core safety traits are covered by 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?

The description is efficient given the tool's complexity, beginning with example queries. Every sentence adds value (sources, fallback, return format, sibling differentiation). Could be slightly trimmed but is well-structured.

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 return structure (changes grouped by source, total count, citation URIs), failure behaviors (patents soft-fail), and data sources. Since there is no output schema, the description fully compensates, making it complete for a complex multi-source tool.

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% but the description adds crucial meaning: explains relative date shorthand with examples, recommends window sizes, and clarifies that 'value' can be ticker or CIK. This significantly aids agent usage.

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 provides a change feed for a company from multiple sources (SEC, GDELT/GNews, USPTO) and explicitly distinguishes from sibling tool 'entity_profile' which is for static profiles.

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', 'latest on Y', 'updates on Acme') and directly contrasts with entity_profile for static profiles. Also gives guidance on using '30d' or '1m' for typical monitoring.

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

All Crypto Fng toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions through the same underlying catalog with only subtle differences. The polymarket family (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) also has fuzzy boundaries. Only the Crypto Fear & Greed tools are clearly distinct.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern, but there are notable deviations: the ask_pipeworx_* family uses object-style names, pipeworx_feedback and polymarket_edges are noun_noun, and current_index vs index_history uses 'index' inconsistently. The polymarket_* family mixes verb and noun styles internally.

Tool Count2/5

33 tools is heavy for a server ostensibly named 'Crypto Fng' — only 2 of the tools relate to that core purpose. The rest constitute a broad generic data-research and prediction-market platform that would be more appropriately scoped as its own server. The count exceeds the comfortable range for an agent to reason over.

Completeness4/5

Within the actual (broad) domain, the surface is fairly complete: discovery (discover_tools, suggest_questions), querying (ask_pipeworx family), grounded verification (validate_claim, ask_pipeworx_grounded), deep research, entity resolution/profile/comparison, memory lifecycle, and subscription lifecycle. The named crypto-sentiment domain is fully covered with current and historical index tools, though the underlying 5,708 pack tools are only reachable indirectly through the router.

Resources