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MCP-Saptiva

MCP-Saptiva

by LaraArias
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

saptiva_list_models

Discover available Saptiva AI models with their capabilities, descriptions, and pricing information to select the right model for your AI workflow needs.

Instructions

List all available Saptiva AI models with their capabilities, descriptions, and pricing.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "saptiva_list_models"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Removedv1.0.0
  2. First observed

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden. It states the tool lists models but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns paginated results, or if the data is cached. This leaves significant gaps for an agent to understand operational constraints.

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 a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse.

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 (0 parameters, no output schema), the description is adequate but incomplete. It lacks context on behavioral aspects like authentication or rate limits, which are important for operational use, especially with no annotations to fill those 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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to add parameter information, and it appropriately doesn't mention any parameters, maintaining focus on the tool's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List') and resource ('all available Saptiva AI models'), specifying what information is included (capabilities, descriptions, pricing). It doesn't differentiate from siblings like 'saptiva_help' which might also provide model information, so it falls short of a perfect 5.

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 is provided on when to use this tool versus alternatives. It doesn't mention if this is for discovery, comparison, or selection purposes, nor does it reference sibling tools like 'saptiva_chat' or 'saptiva_embed' that might require model selection.

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

Deploy Server

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