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total-agent-memory

total-agent-memory

by vbcherepanov
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_context_build

Read-onlyIdempotent

Constructs optimal query context by integrating spreading activation, knowledge graphs, episodes, skills, and self-modeling. Enhances memory retrieval for AI agents.

Instructions

Build optimal context for a query. Combines: spreading activation + knowledge graph + episodes + skills + self-model. The 'brain thinking' tool.

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_context_build"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesWhat you need context for
projectNo
max_tokensNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.5/5.0
Behavior3/5

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

Annotations already convey read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds that it combines retrieval mechanisms, but does not explain actual processing steps, output format, or potential costs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded, with only minor filler in the 'brain thinking' metaphor. It could be more informative, but it does not waste 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?

Given the large sibling set and no output schema, the description leaves ambiguity about the return value, the meaning of 'context', parameter details, and how this differs from other memory tools. It is not complete enough for reliable selection.

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

Parameters1/5

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

Schema coverage is only 33% (1 of 3 parameters described). 'query' has a terse description, while 'project' and 'max_tokens' are undocumented, and the description does not compensate for these gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a verb and resource ('build optimal context for a query') and hints at internal mechanisms, but 'optimal context' and 'brain thinking' are vague. With many similar memory/context sibling tools, it does not clearly define what distinct artifact it produces.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus memory_recall, memory_search_by_tag, memory_context, or other siblings. No conditions, alternatives, or situational examples are provided.

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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