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Productboard MCP Server and API by Cotlet

Mark feedback processed in Productboard

productboard_mark_feedback_processed
Read-onlyIdempotent

Mark feedback processed in the user's own Productboard account from ChatGPT, Claude or any AI agent. Returns how to do it in Productboard step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Productboard Cloud Browser. Productboard's official API: Productboard has a full REST API v2 for notes, entities, teams, members, webhooks and analytics, and a beta MCP server at mcp.productboard.com that works on specs (checked 22 Oct 2026).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "productboard_mark_feedback_processed"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety bar is lower; the description still adds real value by saying the tool opens nothing by itself and that the response is step-by-step UI instructions. Worth noting the name's 'mark processed' wording superficially resembles a write, but the description explicitly reconciles this with the read-only annotation rather than contradicting it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The operational content is front-loaded and tight, but the final sentence about Productboard's REST API v2 and beta MCP server at mcp.productboard.com does not help an agent select or invoke this tool, and the 'checked 22 Oct 2026' stamp reads as filler. Roughly a third of the text is tangential.

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 parameters and no output schema, the description carries the burden of explaining the return value, and it does: step-by-step screens, buttons and fields. It also tells the agent what to do next, so nothing needed to call it correctly is missing.

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, so the baseline is 4 and there is nothing for the description to clarify. No missing parameter meaning is possible here.

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?

States a specific verb and resource ('Mark feedback processed') and immediately resolves the ambiguity that the name creates: it 'Returns how to do it in Productboard step by step (screens, buttons, fields) and opens nothing by itself'. An agent can tell from the first sentence that this is an instruction-generating tool, not a mutation.

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 workflow context: call this to get the steps, 'then do it with the cotlet_browser_* tools in the user's Productboard Cloud Browser'. That is explicit sequencing guidance and names the follow-on tools. It does not, however, say when to prefer this over the sibling Productboard tools (e.g. productboard_add_feedback), so there is no exclusion guidance.

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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