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atom-mcp-server

atom-mcp-server

by AtticStandard
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Get Model Details

get_model_detail
Read-onlyIdempotent

Retrieve complete technical specifications and cross-vendor pricing for specific AI models.

Instructions

Deep dive on a single AI model: technical specs + pricing across all vendors.

Returns model_registry data (context window, parameters, open-source status, training cutoff, model family) plus all SKU pricing across every vendor that offers this model.

Examples:

  • "Tell me everything about GPT-4o" → model_name="GPT-4o"

  • "Claude Sonnet 4.5 specs and pricing" → model_name="Claude Sonnet 4.5"

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_name": "<model_name>"
    },
    "name": "get_model_detail"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYesModel name to look up, e.g. 'GPT-4o', 'Claude Sonnet 4.5', 'Llama 3.1 70B'
_atom_api_keyNoYour ATOM API key for full access. Omit for free tier (redacted data).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.2

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnly/idempotent safety, while the description adds substantial behavioral context: it details the exact data structure returned (model_registry data with context window, parameters, open-source status, training cutoff, model family) and explains the pricing scope (all SKU pricing across every vendor). It also implies tiered access behavior via the API key parameter description.

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 tightly structured with zero waste: first sentence establishes purpose and scope, second sentence details return payload structure, followed by concrete usage examples. Every sentence earns its place with specific technical details rather than generic filler.

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?

Given the absence of an output schema, the description adequately compensates by enumerating the specific fields and data categories returned (registry fields, pricing SKUs). It covers the complexity of the 2-parameter input well and explains the access tier behavior, though it omits error handling scenarios (e.g., model not found).

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?

With 100% schema description coverage, the baseline is 3. The description elevates this by providing concrete examples of model_name values ('GPT-4o', 'Claude Sonnet 4.5') that clarify expected input formats and naming conventions beyond the generic schema description.

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 explicitly states 'Deep dive on a single AI model: technical specs + pricing across all vendors,' providing a specific verb (deep dive), resource (AI model), and scope (technical specs + pricing across vendors). It clearly distinguishes from siblings like search_models (implied by 'single' vs search) and compare_prices (comprehensive detail vs comparison).

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?

The examples ('Tell me everything about GPT-4o', 'Claude Sonnet 4.5 specs and pricing') provide clear context for when to use this tool—when seeking comprehensive details about a specific known model. However, it does not explicitly name alternatives like search_models for when the exact model name is unknown.

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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