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AppFlowy MCP Server and API by Cotlet

List database rows in AppFlowy

appflowy_list_database_rows
Read-onlyIdempotent

List database rows in the user's own AppFlowy account from ChatGPT, Claude or any AI agent. Returns how to do it in AppFlowy step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's AppFlowy Cloud Browser. AppFlowy's official API: AppFlowy Cloud has a documented REST API for workspaces, the folder tree and database rows, but no hosted MCP server (checked 25 Oct 2026).

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds meaningful non-obvious context beyond that: the call does not touch the account at all ('opens nothing by itself'), it returns a step-by-step UI procedure, and no hosted MCP server exists. That corrects a likely wrong expectation from the name, which is exactly what this dimension rewards.

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 sentences, front-loaded with the purpose and the critical 'opens nothing by itself' caveat. The closing sentence about AppFlowy's REST API and the absence of a hosted MCP server is somewhat tangential, though it does justify why browser automation is the fallback; it is the weakest part of an otherwise tight definition.

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

Completeness3/5

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

With zero parameters and no output schema, the description must carry the flow, and it does outline what is returned and what to do next. What is missing is any indication of how complete or structured that returned procedure is, and there is no guidance on what the agent should do if the returned steps do not match the live UI.

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?

The tool takes zero parameters (empty object schema, additionalProperties=false), so there is nothing for the description to disambiguate. Baseline applies.

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?

The description names a specific verb and resource ('List database rows in the user's own AppFlowy account') and then clarifies the actual mechanic: it 'Returns how to do it in AppFlowy step by step (screens, buttons, fields) and opens nothing by itself.' This is important because the name suggests a data fetch while the tool actually yields a procedure. The one gap is that it never distinguishes itself from sibling readers like appflowy_list_workspaces or appflowy_read_page_tree.

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

Usage Guidelines3/5

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

There is real workflow guidance: run this first, then 'do it with the cotlet_browser_* tools in the user's AppFlowy Cloud Browser.' However, the description gives no when-not conditions and no explicit rule for choosing between this and other appflowy_* read tools, so the agent must infer selection logic.

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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