LLM utility tools

MCP reference for Algenta's deterministic utility-model tools — list models, tokenize, count tokens, chat, responses, embeddings, similarity, and rerank.

These tools expose Algenta's deterministic utility-model surface over MCP. They are reproducible, tokenizer- and lexical-embedding-backed routes — not provider-backed generative models — so the same input always yields the same output. Use them to size prompts, produce stable embeddings, and rerank candidates without spending on a generative provider. Each tool wraps a single authenticated endpoint.

Configure algenta-mcp first so the algenta tools appear in your client — see MCP server. Always call list_models before the others: it reports which models the instance can actually execute and their routing, failover, timeout, and auth-header readiness.

chat_completions and responses are the deterministic utility chat and response surfaces, backed by tokenization or lexical embeddings. They are not generative chat models. To route a real generative objective, use the capability plane.

Tools

Tool	What it does	Key inputs
list_models	List the model catalog with routing, failover, timeout, and auth-header readiness.	(none)
resolve_artifact_bridge	Resolve one Hugging Face artifact path through the cache-safe compatibility bridge.	repo_id, filename (required); revision, local_files_only (default true)
tokenize	Tokenize UTF-8 text with a deterministic tokenizer model.	input (required); model (default text.tokenizer)
count_tokens	Count tokens with a deterministic tokenizer model.	input (required); model (default text.tokenizer)
chat_completions	Run the deterministic utility chat surface (tokenizer-backed).	messages (required; role+content); model (default text.tokenizer)
responses	Run the unified utility response surface over tokenization or lexical embeddings.	input (required; string or array); model (default text.tokenizer), dimensions (default 64, 1–4096)
embeddings	Generate deterministic lexical embeddings.	input (required; string or array); model (default text.hash_embedding_v1), dimensions (default 64, 1–4096)
embedding_similarity	Score two caller-supplied vectors with a similarity model.	left, right (required; number arrays); model (default embeddings.cosine_similarity)
rerank	Rank caller-supplied document embeddings deterministically.	query_embedding, documents (required; each id+embedding); model (default embeddings.cosine_similarity), top_n

Endpoint mapping

Tool	Method	Endpoint
list_models	GET	/v1/models
resolve_artifact_bridge	POST	/v1/artifacts/resolve
tokenize	POST	/v1/tokenize
count_tokens	POST	/v1/count_tokens
chat_completions	POST	/v1/chat/completions
responses	POST	/v1/responses
embeddings	POST	/v1/embeddings
embedding_similarity	POST	/v1/embeddings/similarity
rerank	POST	/v1/rerank

Worked examples

Read the model catalog

list_models takes no arguments and returns the truthful catalog for this instance. Call it before routing to any other tool.

list_models (tool call)

{ "name": "list_models", "arguments": {} }

curl -sS "$ALGENTA_BASE_URL/v1/models" \
  -H "Authorization: Bearer $ALGENTA_API_KEY" | jq '.data[].id'

Count tokens before a call

Size any text against the deterministic tokenizer. The result is stable across calls, so you can budget prompts reliably.

count_tokens (tool call)

{
  "name": "count_tokens",
  "arguments": {
    "input": "the quick brown fox",
    "model": "text.tokenizer"
  }
}

curl -sS "$ALGENTA_BASE_URL/v1/count_tokens" \
  -H "Authorization: Bearer $ALGENTA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "input": "the quick brown fox", "model": "text.tokenizer" }'

Embed text, then rerank candidates

embeddings accepts a string or an array of strings and returns fixed-dimension lexical vectors. Feed those vectors into rerank to order documents against a query embedding.

embeddings (tool call)

{
  "name": "embeddings",
  "arguments": {
    "input": ["renewals dashboard", "quarterly churn report"],
    "model": "text.hash_embedding_v1",
    "dimensions": 64
  }
}

rerank (tool call)

{
  "name": "rerank",
  "arguments": {
    "query_embedding": [0.12, 0.04, 0.88],
    "documents": [
      { "id": "doc-1", "embedding": [0.10, 0.05, 0.90], "text": "renewals dashboard" },
      { "id": "doc-2", "embedding": [0.80, 0.10, 0.02], "text": "payroll export" }
    ],
    "top_n": 1
  }
}

resolve_artifact_bridge defaults to local_files_only: true and never downloads. Set it to false explicitly if you want the bridge to fetch an artifact that is not already cached.

Related pages

MCP serverCapability plane toolsAPI endpoint reference