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Anam MCP Server

Anam MCP Server

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by anam-org
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Python
Remote

delete_llm

Delete a large language model by its unique ID. Permanently removes the specified LLM from the system.

Instructions

Delete an LLM by ID.

Args: llm_id: The UUID of the LLM to delete

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": {
      "llm_id": "<llm_id>"
    },
    "name": "delete_llm"
  }
}

Input Schema

NameRequiredDescriptionDefault
llm_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.13

TDQS

B3.4/5.0
Behavior2/5

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

No annotations provided, so the description carries full burden. It does not disclose whether the deletion is permanent, whether it cascades to related entities, or any authentication/authorization requirements. Critical behavioral details are omitted.

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?

The description is extremely concise and front-loaded with the purpose. It could benefit from a brief note on irreversibility or side effects, but it is efficient for a simple delete.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single parameter and an output schema present, the description covers the basic operation but lacks insights into return values or error conditions. It is minimally adequate but leaves behavioral gaps.

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 description adds meaning by specifying that llm_id is a UUID and that it identifies the LLM to delete. With 0% schema parameter descriptions, this clarification is valuable.

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?

The description clearly states the action (Delete) and the resource (LLM), and specifies the required parameter llm_id. It effectively distinguishes from siblings like create_llm, get_llm, list_llms, and update_llm.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., deleting via a batch operation or soft delete options). No context about prerequisites or prohibitions.

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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