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Robly MCP by usefulapi

List contacts

robly_list_contacts
Read-only

List contacts (members) of the Robly account, newest first as Robly orders them: id, email, subscribed/deleted flags, subscription date, sign-up source, timestamps. Filter by status, by list, or by last-updated time; optionally include each contact's lists and data fields. Pages with limit (max 100) and offset: pass next_offset from the previous reply. Robly does not return a total. GET /api/v1/contacts/show.

Input Schema

NameRequiredDescriptionDefault
limitNoContacts per page, 1-100 (default 25).
offsetNoContacts to skip (default 0).
statusNoOnly contacts with this status (default all).
list_idNoOnly contacts on this list (sub list id from robly_list_sub_lists).
updated_sinceNoOnly contacts changed since this ISO 8601 date-time with a timezone, e.g. 2026-10-01T00:00:00Z.
include_fieldsNoInclude each contact's data fields (name, custom fields).
include_sub_listsNoInclude the lists each contact is on.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnly/non-destructive/openWorld, and the description adds genuinely useful behavior beyond them: newest-first ordering, the hard page cap of 100, the absence of a total count, and the offset-chaining pattern. It does not discuss error behavior or rate limits, keeping it out of the top band.

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?

A single dense paragraph, front-loaded with verb, resource and ordering, then filters, then paging caveats. Every clause carries information an agent needs; nothing is repeated from the schema.

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?

With no output schema, the description compensates by enumerating returned fields (id, email, flags, dates, source, timestamps) and disclosing the missing total. For a 7-parameter read-only list tool this is sufficient to call correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real semantics the schema cannot: what the include_* flags yield in the response, how the page chain works across calls, and that no total is available to compute remaining pages. That raises it above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("List contacts (members) of the Robly account") and enumerates the returned fields, so an agent can tell it apart from the single-record robly_find_contact. It stops short of naming sibling tools explicitly, so it falls just short of a 5.

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?

Gives clear usage context: default newest-first ordering, the three filter axes (status, list, updated_since), optional expansion via include_fields/include_sub_lists, and paging instructions ("pass next_offset from the previous reply"). No explicit when-not-to-use or named alternative is given, but the operating context is unambiguous.

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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