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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_search_fast

Read-onlyIdempotent

Retrieve relevant memories deterministically using FastEmbed. Bypasses LLM reranking for fast, consistent results.

Instructions

v11.0: like memory_recall but with rerank=False, diverse=False forced. Deterministic fast path — zero LLM, FastEmbed-only.

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": {
      "query": "<query>"
    },
    "name": "memory_search_fast"
  }
}

Input Schema

NameRequiredDescriptionDefault
typeNoall
limitNo
queryYes
branchNo
detailNofull
fusionNorrf
projectNo
embedding_spaceNoFilter to one or more embedding spaces (text|code|log|config).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Discloses that it forces rerank=False and diverse=False, uses no LLM, and is deterministic, which is consistent with readOnly and idempotent annotations. It does not mention output shape or side effects, but the read-only behavior is already covered.

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 short and front-loaded; two sentences convey the key distinction without padding. The version prefix is minor but does not detract from clarity.

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?

As a standalone description, it depends heavily on the unnamed memory_recall tool and omits return shape, result ordering, and parameter semantics. Given no output schema and low schema coverage, an agent would need additional context to use it confidently.

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 is described in the schema; query, limit, type, branch, detail, fusion, and project have no parameter-level explanation and the description does not clarify them beyond the memory_recall reference. With 8 parameters and 13% schema coverage, most parameter semantics remain implicit.

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?

Clearly identifies a memory search tool and distinguishes it from memory_recall by forcing rerank=False and diverse=False for a deterministic fast path. The exact search semantics rely on familiarity with memory_recall, but the name and 'like memory_recall' anchor the purpose.

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?

Explicitly says when to use it: deterministic fast path, zero LLM, FastEmbed-only, in contrast to memory_recall. It does not spell out all trade-offs or when not to use, but the deterministic/no-LLM cues are practical guidance.

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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