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Twenty MCP by usefulapi

List notes

twenty_list_notes
Read-only

List/filter notes. Supports cursor pagination, raw filter/order_by, and relation depth. Twenty REST: GET /rest/notes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow deep to hydrate related records: 0, 1, or 2.
limitNoResults per page (1-60, default 20 — Twenty caps at 60).
filterNoRaw Twenty filter string, passed verbatim as ?filter=. Syntax: field[comparator]:value, e.g. `employees[gte]:100` or `stage[eq]:"MEETING"`. Comparators: eq, neq, gt, gte, lt, lte, in, is, like, ilike, startsWith.
order_byNoRaw Twenty ordering string, passed verbatim as ?order_by=, e.g. `createdAt[DescNullsLast]`.
ending_beforeNoPagination cursor — return records before this cursor (from a previous page's pageInfo).
starting_afterNoPagination cursor — return records after this cursor (from a previous page's pageInfo).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds that results are cursor-paginated and can be hydrated to a relation depth, which is genuine behavioral context, but it omits the 60-item cap behavior and what pageInfo/return shape the caller receives.

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?

Three short sentences, front-loaded with the core action and then the notable capabilities and endpoint. No filler, though the REST endpoint reference is mildly redundant with the tool name.

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?

For a read-only list tool with a fully documented schema and no output schema, the description covers scope, pagination, filtering, and ordering adequately. The only real gap is that result-shape and default-paging behavior are left to the schema annotations.

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

Parameters3/5

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

Schema description coverage is 100%, so every parameter (depth, limit, filter, order_by, both cursors) is already documented with syntax examples. The description's mention of "raw filter/order_by" and "relation depth" merely mirrors the schema and adds no new semantics, making the baseline 3 appropriate.

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/filter notes") and discloses the underlying REST operation, which cleanly separates it from twenty_get_note and the create/update siblings. It is clear without explicitly naming an alternative tool.

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

Usage Guidelines2/5

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

The description only asserts capability (pagination, raw filter/order_by, depth) and never says when to reach for this list tool versus twenty_get_note or how to narrow results. No prerequisites or exclusions are given, leaving usage entirely to inference from the 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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