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total-agent-memory

total-agent-memory

by vbcherepanov
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Python
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memory_rebuild_embeddings

DestructiveIdempotent

Rebuild vector embeddings for stored memory records, optionally filtering by embedding space, to refresh outdated representations after switching embedders. Idempotent operation returns counts of rebuilt and skipped records.

Instructions

v11.0: re-encode every record (or every record in a given embedding space) and update the binary + float32 vectors. Idempotent. Pass embedding_space='code' to refresh only code rows after switching the code embedder. Returns {rebuilt: int, skipped: int}.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "memory_rebuild_embeddings"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
projectNo
batch_sizeNo
embedding_spaceNoOptional: only re-encode rows in these spaces.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

Description says 'update' and 're-encode', clearly indicating mutation, and adds idempotency info. Annotations already mark it destructive and not read-only, so no contradiction; slight lack of detail on potential data loss or performance impact.

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?

Very concise: two sentences covering functionality, idempotency, an example, and the return shape. No superfluous words.

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?

Mentions the return shape ({rebuilt, skipped}) but leaves significant gaps: meaning of limit, project, batch_size is unexplained, and there is no warning about side effects or performance. Given the tool mutates records, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only embedding_space has a description in the schema, and the description adds meaning for it. The other three parameters (limit, project, batch_size) remain undocumented, and the description does not compensate for the low 25% coverage.

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?

Clearly states the action (re-encode and update vectors), the resource (records in an embedding space), and provides a concrete usage example (embedding_space='code').

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?

Gives an explicit scenario for using the tool (refreshing code rows after switching the embedder) and mentions idempotency, but does not contrast with alternative tools or state when not to use it.

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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