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praxeo

OpenRouter MCP Server

by praxeo

chat_with_model

Send messages to any OpenRouter model with customizable parameters. Control response length, randomness, and system instructions for precise AI output.

Instructions

Send a message to a specific OpenRouter model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesOpenRouter model ID (e.g., 'openai/gpt-4')
messageYesMessage to send to the model
max_tokensNoMaximum tokens in response
temperatureNoTemperature for response randomness
system_promptNoSystem prompt for the conversation
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for disclosing behavior. It only states that the tool sends a message, without explaining whether the operation is idempotent, what side effects exist, rate limits, or the nature of the response. For a tool with no annotations, this is insufficient.

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 a single concise sentence with no wasted words. However, it is overly brief and could benefit from a brief note on usage or behavior. Still, it achieves clarity without verbosity.

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?

Given that there is no output schema, the description should explain what the tool returns (e.g., the model's response). It also fails to address the complexity of 5 parameters, such as how system_prompt interacts with message. The description is incomplete for a tool of this nature.

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%, meaning each parameter is already described in the schema. The description does not add extra meaning beyond the schema. Baseline score of 3 is appropriate because the schema handles documentation, but the description contributes no additional parameter context.

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 'Send a message to a specific OpenRouter model' uses a specific verb (send) and identifies the resource (message to model). It clearly distinguishes from sibling tools like analyze_document, compare_models, get_model_info, and list_models.

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. The description lacks context about prerequisites, when to choose chat_with_model over other tools, or any exclusions.

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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