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Glama

Comtrade

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe read operations. The description adds context: parallel calls, fallback mechanism, and return structure (changes[] grouped by source, total_changes, citation URIs). No contradictions.

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 front-loaded with query examples and structured logically. While dense, every sentence adds value. Slightly long but appropriate for the complexity.

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 no output schema and multi-source complexity, the description is thorough: explains each source, fallback behavior, return format, and when to use alternative. Covers all necessary context.

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 covers all parameters with descriptions. The description adds usage context: since accepts ISO or relative shorthand with recommendation, value accepts ticker or CIK, type only 'company'. This enriches understanding beyond 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' with specific examples like 'What's new with X'. It distinguishes itself from the 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?

Explicitly tells when to use this tool versus entity_profile. Provides detailed guidance on date format (ISO or relative), fallback behavior (GDELT→GNews), and soft-fail for patents. Also recommends 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.

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TDQS

A3.8/5.0
Disambiguation3/5

Some tools have near-identical purposes (ask_pipeworx vs ask_pipeworx_beta are currently identical; polymarket_arbitrage vs polymarket_edges both find opportunities), but detailed descriptions and distinct input patterns mostly help an agent choose. A few discovery/verification tools (suggest_questions vs discover_tools, validate_claim vs ask_pipeworx_grounded) also overlap, creating residual ambiguity.

Naming Consistency3/5

All names are snake_case and many use domain prefixes (comtrade_, polymarket_, pipeworx_), but the set mixes verb_noun (compare_entities, discover_tools), bare verbs (remember, forget, subscribe), and noun phrases (entity_profile, recent_changes). This inconsistency, plus the use of domain-specific prefixes as a substitute for a uniform verb_noun style, makes the naming pattern only moderately predictable.

Tool Count2/5

35 tools is well beyond the typical well-scoped range, and the vast majority (prediction markets, memory, subscriptions, feedback, npm dependency checks) have nothing to do with the server's apparent Comtrade trade-data purpose. This bloat makes the set feel unfocused, even though some meta-tools serve a general data-access mission.

Completeness4/5

For the Comtrade trade-data domain, the four comtrade_* tools cover country codes, top commodities, top partners, and detailed bilateral trade values — enough for most queries, with minor gaps like time-series trends or tariff lookups. For the broader Pipeworx data-access scope, the set is extensive (ask_pipeworx, deep_research, entity_profile, validate_claim, subscriptions), so no severe dead ends are apparent.

Resources