eLabFTW MCP

review_experiment

Generate an AI review summary for an eLabFTW experiment: assess documentation quality, risks, suggestions, and completeness score. Read-only; requires an AI endpoint.

Instructions

Generate an AI review summary for an eLabFTW experiment (documentation quality, risks, suggestions, completeness score). Read-only — writes nothing. Needs an AI endpoint; without one the response carries the upstream placeholder marker.

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": {
      "id": "<id>"
    },
    "name": "review_experiment"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 7, 2026

TDQS

A4/5.0

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 well: it discloses that the operation is read-only ('writes nothing') and that it depends on an AI endpoint, with a defined fallback placeholder behavior. It does not cover auth requirements or rate limits, but the core behavioral profile is clear.

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?

Two tight sentences, front-loaded with the purpose followed by the safety and dependency caveats. Every clause earns its place.

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 primary operational caveat (AI endpoint dependency). For a single-parameter read-only tool this is nearly complete, with only minor gaps around the id parameter.

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 coverage is 0% and the sole 'id' parameter is undocumented in the schema. The description implicitly clarifies that the id refers to an eLabFTW experiment, adding marginal meaning, but does not specify format or behavior beyond that.

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 (Generate) and resource (AI review summary for an eLabFTW experiment) and even enumerates the output components (documentation quality, risks, suggestions, completeness score). This clearly distinguishes it from read-only siblings like get_experiment and from the related add_ai_review_comment.

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 a key prerequisite ('Needs an AI endpoint; without one the response carries the upstream placeholder marker'), which tells the agent when the tool is useful. However, it never explicitly contrasts this with alternatives such as get_experiment or add_ai_review_comment, leaving the choice to inference.

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