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responses

Read-only

Process independent strings via the unified Algenta response envelope to return token counts, embeddings, or generated text per selected model. Stateless single-turn requests with no conversation storage.

Instructions

Run the unified Algenta response envelope over one string or a list of independent strings, each processed as its own single-turn request. The output item per input depends on the model: tokenization models (default text.tokenizer) return the input's tokens and token_count; embedding models return a deterministic vector of dimensions length; provider-backed chat models advertised by list_models return generated text. Use chat_completions for an ordered multi-role transcript and embeddings when you specifically need vectors. Stateless and non-destructive: no conversation state is created, continued, or stored by this tool. An unsupported model id fails with model_not_supported.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesOne string, or a list of independent strings each processed as its own single-turn request.
modelNoModel id from list_models; selects the output item type.text.tokenizer
dimensionsNoEmbedding vector length when the selected model produces embeddings.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.3
    • addedInput schema / properties / dimensions / description
      Added value: +"Embedding vector length when the selected model produces embeddings."
    • addedInput schema / properties / input / description
      Added value: +"One string, or a list of independent strings each processed as its own single-turn request."
    • addedInput schema / properties / model / description
      Added value: +"Model id from list_models; selects the output item type."
  2. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds value by stating statelessness and non-destructiveness. It also mentions the model_not_supported error, which is useful. But it doesn't detail output format or error handling beyond that.

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 compact and dense, with every sentence adding essential information. It front-loads the core action and then provides model-specific behavior and alternatives. No fluff.

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?

For a tool with no output schema, the description provides enough to know what to expect per model type. It covers statelessness, error case, and parameter effects. It doesn't detail exact response format, but given the tool's flexibility, it's acceptable. A bit more on embedding vector specifics could be added.

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 coverage is 100%, so the schema already describes all parameters. The description adds that 'dimensions' is the embedding vector length and that 'model' selects output type, but these are minor additions over the schema descriptions. Some redundancy exists, but baseline 3 is appropriate.

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 tool's purpose: running a unified response envelope over one or more strings, with per-model behavior. It distinguishes itself from chat_completions and embeddings by specifying alternatives. However, it could be clearer about what 'response envelope' means.

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 description provides explicit guidance: use chat_completions for multi-role transcripts and embeddings for vectors. It lacks explicit 'when not to use' but covers key alternatives. The model selection guidance (list_models) is helpful.

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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