Ankra

Update AI model

update_ai_model

Change a model in the organisation's AI model catalog (the twin of ankra ai models update): only the fields passed change, the rest keep their values. model is the catalog row id or key (list_ai_models). Disabling a model a lane selected returns that lane to its default. Needs ai.manage (organisation owners and admins). Never takes or returns a provider key.

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": {
      "model": "<model>"
    },
    "name": "update_ai_model"
  }
}

Input Schema

Name	Required	Description
`key`	No	The catalog key: lowercase letters, digits and hyphens, unique in the organisation's catalog.
`tier`	No	The tier the model fills (quick, standard or deep); empty for none.
`model`	Yes	The model's catalog row id or its catalog key, as list_ai_models shows them.
`model_id`	No	The provider's model id, e.g. claude-opus-5, or the model a custom endpoint serves.
`is_enabled`	No	Whether the picker offers the model; default true.
`sort_order`	No	Position in the picker; lower first.
`description`	No	A short description shown beside the name.
`endpoint_id`	No	The id of the organisation's OpenAI-compatible endpoint that serves the model (list_ai_endpoints names them); omit or null for an Ankra-managed model.
`display_name`	No	The name the chat model picker shows.
`supports_tools`	No	Whether the model can call tools; default true.
`supports_images`	No	Whether the model reads images; default false.
`max_output_tokens`	No	The most tokens one answer may carry; 0 for the default.
`supports_thinking`	No	Whether the model supports extended thinking; default false.
`context_window_tokens`	No	The model's context window in tokens; 0 for the default.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 8, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Goes well beyond the annotations: it discloses the merge/patch semantics, a real downstream side effect ("Disabling a model a lane selected returns that lane to its default"), the required permission scope, and a security constraint ("Never takes or returns a provider key"). With destructiveHint=false, the lane-reset warning is exactly the kind of consequence an agent would otherwise miss.

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?

Four short sentences, front-loaded with the action and the partial-update rule, with side effects and permissions after. The "twin of `ankra ai models update`" clause is slightly incidental but cheap and useful for CLI-literate callers; nothing is redundant.

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?

For a 14-parameter mutation with no output schema and thin annotations, the description covers identity, semantics, permission, and the main side effect. The remaining gap is the response shape — with no output schema, the agent gets no statement of what a successful update returns beyond the negative "never returns a provider key".

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?

Schema coverage is 100%, so per-parameter meaning is already handled and baseline 3 applies. The description earns above baseline by clarifying that all 13 non-required fields are optional and left untouched when omitted, and by restating that `model` accepts either a row id or a catalog key sourced from list_ai_models.

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 ("Change a model in the organisation's AI model catalog") and pins the exact object via the `model` identifier. It is clearly separable from create_ai_model, delete_ai_model, set_ai_lane_model and list_ai_models named in the sibling set, and the CLI twin reference anchors it for operators who know the command line.

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 concrete context: use list_ai_models to obtain the row id or key, and ai.manage is required (owners and admins). The partial-update rule ("only the fields passed change") tells the agent how to call it rather than when to prefer it, and no sibling is explicitly excluded — e.g. nothing says when to use set_ai_lane_model instead.

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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