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Mailchimp

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,801 across 1517 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

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?

Description goes beyond readOnlyHint/openWorldHint annotations by specifying the exact behavior: refuses with categorized refusal_reason values when data doesn't directly answer, and promises verbatim evidence. No contradiction 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?

Structured and skimmable but slightly verbose with repeated 'picks the right tool... fetches the data' and em-dash asides. Still every sentence carries useful information; only mild redundancy prevents a 5.

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 purpose, usage boundaries, return shape (success and refusal), cost comparison, and relationship to sibling tools. No output schema is provided, but the description compensates by outlining the exact return structure and refusal enums.

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 describes all six aliases with 100% coverage. The description adds natural-language semantics and explicitly clarifies mutual aliasing, which helps an agent construct a valid request without ambiguity.

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?

Clearly states the tool's purpose: a grounded answer mode that extracts answers only from tool results, avoiding hallucination. It differentiates from sibling ask_pipeworx by highlighting the extra LLM call and grounded nature, so an agent can select it without inspecting the schema.

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 explains when to use ('whenever an answer will be quoted, cited, or acted on') and when not to ('prefer ask_pipeworx for casual lookups') due to the extra LLM cost. This is direct, actionable guidance that mentions the alternative by name.

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