eLabFTW MCP

update_entity_fields

Update an existing eLabFTW experiment or item's title, status, or category. Requires write scope and at least one field; returns applied values and read-back verification.

Instructions

Update top-level scalar fields (title, status, category) on an existing eLabFTW experiment or item. WRITE OPERATION — requires the update write scope. At least one of title, status_id, category_id must be given. Returns the applied fields and a read-back verification.

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>"
    },
    "name": "update_entity_fields"
  }
}

Input Schema

Name	Required	Description	Default
`id`	Yes
`title`	No
`trace_id`	No
`status_id`	No
`category_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

A4.2/5.0

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 flags a WRITE OPERATION, the required write scope, and the at-least-one-field constraint. It stops short of saying whether unspecified fields are left untouched or that the read-back is the verification mechanism's output.

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?

Three tight sentences, front-loaded with the purpose and then the operational caveats. No filler; each sentence adds information.

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 details needn't be spelled out, and the write scope plus validation rule are covered. The main gap is that entity_type and id are undocumented and the valid values for entity_type (experiment vs item) are not stated.

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% across 6 params. The description covers title, status_id, and category_id (including that at least one is required), but leaves id, entity_type, and trace_id unexplained — notably entity_type, which likely drives behavior. Partial compensation, so a mid score.

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 (update), the exact resource and scope (top-level scalar fields on an existing eLabFTW experiment or item), and enumerates the affected fields. This clearly separates it from siblings like update_entity_metadata and update_experiment_body.

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 a clear precondition ('at least one of title, status_id, category_id must be given') and the required permission scope. It does not explicitly name alternatives for editing non-scalar fields, but 'top-level scalar fields' implicitly positions it against update_entity_metadata.

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