tentra

record_embedding

Store embedding vectors for files or symbols to populate a vector search index, enabling semantic search and retrieval in AI coding environments.

Instructions

Store an embedding vector for a symbol or file. Call this after generating an embedding with your native capability. Used to populate the vector search index.

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": {
      "entity_type": "<entity_type>",
      "entity_id": "<entity_id>",
      "model": "<model>",
      "vector": "<vector>",
      "source_text": "<source_text>"
    },
    "name": "record_embedding"
  }
}

Input Schema

Name	Required	Description
`entity_type`	Yes	What kind of entity this embedding represents
`entity_id`	Yes	ID of the file or symbol being embedded
`snapshot_id`	No	Snapshot this embedding belongs to
`model`	Yes	Embedding model identifier (e.g. text-embedding-3-small)
`vector`	Yes	The embedding vector produced by the agent
`source_text`	Yes	The text that was embedded (for audit / re-embed on model change)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Apr 18, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool stores embeddings to populate a vector search index, implying a write operation, but lacks details on permissions, rate limits, idempotency, or error handling. For a tool with no annotations and a mutation action, this leaves significant gaps in understanding its behavior and constraints.

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 two sentences, front-loaded with the core purpose and followed by usage timing. Every word earns its place with no redundancy or fluff, making it highly efficient and easy to parse for an AI agent.

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

Completeness3/5

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

Given the tool has no annotations and no output schema, the description is somewhat complete for a basic understanding but lacks depth. It covers the what and when but omits behavioral details like side effects, response format, or error cases. For a mutation tool with 6 parameters, this is a minimal viable description that could benefit from more context.

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 all parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by mentioning the tool is for storing embeddings after generation, but it does not provide additional semantic context for parameters like why snapshot_id might be optional or how vector dimensions relate to the model. Baseline 3 is appropriate as the schema carries the primary burden.

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 clearly states the action ('Store an embedding vector') and the target ('for a symbol or file'), specifying both verb and resource. It also distinguishes from potential siblings by mentioning it's used 'to populate the vector search index,' which sets it apart from other tools like record_contract or record_decision that handle different data types.

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 context on when to use it: 'Call this after generating an embedding with your native capability.' This gives clear timing guidance. However, it does not specify when NOT to use it or mention alternatives among the many sibling tools, such as how it differs from record_semantic_node or other indexing tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.