eLabFTW MCP

add_ai_review_comment

Attach an AI-generated review comment to an eLabFTW experiment or item with automatic provenance header and trace ID; requires comment write scope.

Instructions

Attach an AI-generated review comment to an eLabFTW experiment or item. The provenance header ('[AI-GENERATED] | | trace: …') is added automatically. WRITE OPERATION — requires the comment write scope.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_type": "<entity_type>",
      "id": "<id>",
      "comment": "<comment>"
    },
    "name": "add_ai_review_comment"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes
`comment`	Yes
`trace_id`	No
`entity_type`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 7, 2026

TDQS

A3.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does a fair job: it discloses that the tool is a WRITE OPERATION, that the provenance header is added automatically (so the agent should not include it), and that a comment write scope is required. It stops short of describing append-vs-overwrite behavior or failure modes.

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 tight sentences with the operation and its automatic header behavior front-loaded, and no filler. Slightly compressed given the undocumented parameters, but efficient per sentence.

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?

An output schema exists, so return values need not be explained, and the description covers the essentials: write classification, required scope, and the auto-generated header. The main gap is entity_type semantics and valid values, which are undocumented in both schema and description.

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

Parameters2/5

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

Schema description coverage is 0% for all four parameters, so the description must compensate and largely does not. Only trace_id gets indirect meaning via the 'trace: …' header notation; entity_type, id, and comment are left entirely to the bare schema titles.

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 gives a specific verb (attach) and resource (AI-generated review comment) and names the target domain (eLabFTW experiment or item), which is clear and concrete. It does not distinguish itself from the sibling review_experiment tool, which an agent might reasonably confuse it with.

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?

It states the write scope requirement ('requires the comment write scope') and flags the operation as a write, giving some context for use. However, there is no guidance on when to reach for this versus review_experiment or how it fits into a review workflow.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.