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Glama

Mailchimp

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?

Beyond annotations (readOnly, idempotent), the description discloses the parallel fan-out to multiple sources, fallback mechanism, soft-failure of the patents API, and the output structure (changes[] grouped by source, total_changes count, citation URIs). No contradictions 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.

Conciseness4/5

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

The description is well-structured with examples upfront, functional details, and a clear sibling differentiation at the end. While slightly long, every sentence adds value; could be marginally more concise but front-loads key information effectively.

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, input formats) and lack of output schema, the description thoroughly explains behavior, output structure, and constraints. Agent has enough context to use the tool correctly.

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?

With 100% schema coverage, the description adds substantial value: examples for `since` (ISO date and relative shorthand like '30d'), explains that `value` accepts ticker or CIK, and notes that `type` only supports 'company'. Provides monitoring recommendation for `since`.

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 starts with example queries ('What's new with X', 'latest on Y') and explicitly states it's a change feed for a company in the last N days. It lists the exact sources (SEC EDGAR, GDELT→GNews, USPTO) and distinguishes itself from the sibling tool entity_profile, making the purpose crystal clear.

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 scenarios (e.g., 'updates on Acme') and when-not-to-use (pointing to entity_profile for static profiles). Also explains fallback behavior (GDELT preferred, GNews when rate-limited) and gives parameter usage examples for `since` and `value`.

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

B3.4/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx and ask_pipeworx_beta are currently identical, and discover_tools overlaps heavily with suggest_questions. The six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, etc.) have fuzzy boundaries that will mislead an agent choosing among them.

Naming Consistency3/5

Most names are snake_case, but patterns vary: verb_noun (list_subscriptions, validate_claim), adjective_noun (recent_alerts, recent_changes), bare verbs (remember, recall, forget), and domain-prefixed tools (ask_pipeworx, polymarket_*, mailchimp_*). No camelCase mixing, but the inconsistency across styles makes prediction of names harder.

Tool Count1/5

The server is named Mailchimp but only 5 of 36 tools are Mailchimp-related; the remaining 31 tools cover unrelated domains (Pipeworx data lookup, prediction markets, memory, subscriptions). This extreme mismatch means the count is wildly inappropriate for the apparent purpose.

Completeness1/5

For a Mailchimp server, the surface is severely incomplete: only read operations exist (list/get audiences, campaigns, members) with no create, update, delete, send, or automation tools. The Pipeworx tools are comparatively rich but their presence does not fix the fact that the Mailchimp domain itself is a dead end.

Resources