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Algenta MCP Server

by io.github.thyn-ai
OverviewDiscussionsAdmin

chat_completions

Read-only

Run one ordered chat transcript through an Algenta model and return the assistant message plus token usage. The default text.tokenizer model is a deterministic tokenizer-backed utility route whose assistant message is a JSON tokenization summary of the user messages — not a generative LLM; provider-backed chat models advertised by list_models are routed through the configured provider service. Use responses for independent single-string utility calls. This tool does not stream and does not expose function/tool calling, and nothing is persisted. An unsupported model id fails with model_not_supported.

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": "chat_completions"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoChat-capable model id from list_models.text.tokenizer
messagesYesOrdered conversation transcript; the last user message is the prompt.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / messages / description
      Added value: +"Ordered conversation transcript; the last user message is the prompt."
    • addedInput schema / properties / messages / items / properties / content / description
      Added value: +"Text content of this transcript turn."
    • addedInput schema / properties / messages / items / properties / role / description
      Added value: +"Speaker role for this transcript turn."
    • addedInput schema / properties / model / description
      Added value: +"Chat-capable model id from list_models."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already note readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds substantial behavioral context: the default text.tokenizer is deterministic and non-generative, nothing is persisted, unsupported model ids fail with model_not_supported, and the tool lacks streaming and function-calling capabilities. This goes well beyond what annotations provide.

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 dense but front-loaded, with the core purpose in the first sentence, model routing and limitations in the second, and alternative tool guidance plus an error case in the third. Every sentence adds distinct value, and there is no repetition of schema or annotation details.

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

Completeness5/5

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

Given the small parameter surface, rich schema documentation, and no output schema, the description covers what an agent needs: the return shape, default model behavior, provider-backed model routing, non-features, persistence behavior, and the failure mode. Nothing critical is missing for selecting and invoking the tool correctly.

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?

Schema coverage is 100%, so the baseline is 3. The description enriches the model parameter by explaining that text.tokenizer is a deterministic tokenizer-backed route returning a JSON tokenization summary, not a generative LLM. This directly informs parameter choice and output interpretation. The messages parameter is already well documented in the schema.

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 states a specific verb ('Run'), a resource (one ordered chat transcript through an Algenta model), and the returned output (assistant message plus token usage). It also distinguishes this tool from sibling tools like responses and the tokenizer-backed default model.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use responses for independent single-string utility calls, explains that provider-backed chat models are routed through list_models, and clarifies what this tool does not do (no streaming, no function/tool calling). This gives an agent clear guidance on when to select this tool over alternatives.

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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