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Anam MCP Server

Anam MCP Server

Official
by anam-org
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Python
Remote

get_llm

Retrieve details of a specific LLM by its UUID. Returns full model information from Anam.

Instructions

Get details of a specific LLM by ID.

Args: llm_id: The UUID of the LLM to retrieve

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": "get_llm"
  }
}

Input Schema

NameRequiredDescriptionDefault
llm_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.13

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states 'Get details' but does not disclose that this is a read-only operation, nor does it describe error behavior (e.g., what happens if the ID does not exist). The presence of an output schema partially compensates, but the description itself lacks behavioral context.

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?

The description is exceptionally concise: two sentences and a single argument line. Every part is essential, with no redundant information. It is efficiently front-loaded with the action and resource.

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 the tool's simplicity (single parameter, one required field) and the existence of an output schema, the description is minimally adequate. However, it lacks contextual elements such as error handling, prerequisites, or typical usage patterns, which would be helpful for a complete understanding.

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?

The schema has 0% description coverage, so the description must add meaning. It explains the 'llm_id' parameter as 'The UUID of the LLM to retrieve', which adds context beyond the schema's type-only definition. However, it does not specify format constraints or additional details, making it adequate but not highly informative.

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 tool's action ('Get details') and the resource ('specific LLM by ID'). It directly distinguishes from sibling tools like 'list_llms' (list) and 'create_llm' (create), making its purpose unambiguous.

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?

The description provides no guidance on when to use this tool versus alternatives. It neither mentions when not to use it nor suggests other tools for different scenarios. Given the large set of sibling tools, this omission hinders correct selection.

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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