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MCP-Saptiva

MCP-Saptiva

by LaraArias
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

saptiva_chat

Send chat completion requests to AI models for generating responses, reasoning tasks, and workflow automation through the MCP-Saptiva server.

Instructions

Send a chat completion request to Saptiva AI models. Supports multiple models including Saptiva Turbo (fast), Cortex (reasoning), Legacy (tool-compatible), and more.

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": {
      "messages": "<messages>"
    },
    "name": "saptiva_chat"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoModel to use. Options: Saptiva Turbo, Saptiva Cortex, Saptiva Ops, Saptiva Legacy, Saptiva KALSaptiva Turbo
messagesYesArray of message objects with role and content
max_tokensNoMaximum tokens to generate
temperatureNoSampling temperature (0.0 to 1.0)
top_pNoTop-p sampling parameter (0.0 to 1.0)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Removedv1.0.0
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions model capabilities but doesn't describe authentication requirements, rate limits, costs, response formats, error handling, or whether this is a read/write operation. For a chat completion tool with zero annotation coverage, this leaves significant behavioral gaps.

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 appropriately sized with two sentences that efficiently convey core functionality and model options. It's front-loaded with the primary purpose and avoids unnecessary elaboration. Every sentence contributes value, though it could be slightly more structured with clearer separation of concepts.

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

Completeness2/5

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

For a chat completion tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what a 'chat completion request' entails, what the response format looks like, authentication requirements, or error conditions. The agent lacks crucial context for proper tool invocation despite good schema coverage.

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?

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema by mentioning model capabilities (fast, reasoning, tool-compatible) which slightly enhances understanding of the 'model' parameter options. This meets the baseline for high schema coverage.

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 ('Send a chat completion request') and target resource ('to Saptiva AI models'), providing a specific verb+resource combination. It distinguishes from some siblings by focusing on chat completion rather than embedding, OCR, or model listing, though it doesn't explicitly differentiate from saptiva_reason which might have overlapping functionality.

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 about when to use this tool versus alternatives. The description mentions model capabilities (fast, reasoning, tool-compatible) but doesn't specify use cases, prerequisites, or when to choose this over sibling tools like saptiva_reason. The agent receives no explicit when/when-not/alternatives information.

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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