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Glama

Mailchimp

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds value by specifying data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), off-calendar fiscal year handling, result sorting by primary metric, and inclusion of pipeworx:// citation URIs.

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 five sentences, front-loaded with example queries and a clear directive. Every sentence adds value: usage examples, data source details, sorting behavior, and alternative comparison. No filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description covers what data is returned per type and mentions citation URIs. It also notes that results are sorted and that the tool replaces many sequential lookups. The description is mostly complete for a two-parameter tool with full schema coverage, though it could optionally detail the exact output structure.

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 enriches each parameter: for 'type', it explains what data is pulled per entity type (e.g., 'latest 10-K revenue + net income + cash + long-term debt' for company); for 'values', it clarifies required format (tickers/CIKs for company, drug names) and provides examples. This is significant added meaning beyond the basic schema descriptions.

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's purpose: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies the verb 'compare' and the resources (companies or drugs), and distinguishes from sibling tools like entity_profile which focus on single entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises 'ALWAYS PREFER over sequential single-pack lookups' and provides example queries like 'which is bigger', indicating when to use this tool. It partially addresses when not to use by contrasting with sequential lookups, but does not explicitly name alternatives like entity_profile.

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