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SecondBrain MCP

SecondBrain MCP

by vladilenm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

add_agent_memory

Log structured memory entries (rule, mistake, example, project, style, role) with optional tags and source notes to build an AI-system knowledge base.

Instructions

Append a rule, mistake, example, project, style, or role memory entry under 00_Meta/AI-System.

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": {
      "type": "<type>",
      "content": "<content>"
    },
    "name": "add_agent_memory"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNoOptional memory tags
typeYesMemory entry type
contentYesMemory entry content
source_pathNoOptional source note path

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description must carry full weight. It only states the basic action without disclosing side effects, permissions, duplicate handling, or return behavior. For a mutation tool, this is insufficient.

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 a single sentence, very concise, and front-loaded with the verb. It wastes no words but could benefit from a bit more detail. Still good structure.

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 tool has 4 parameters (2 required) and no output schema, the description is too brief. It lacks return value info, behavioral details, and comparison to sibling tools, leaving the agent underinformed.

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 coverage is 100%, so parameters are described in the schema. The tool description mentions the allowed types but does not add further meaning beyond what the schema provides. Baseline score of 3 is appropriate.

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 ('Append'), the resource ('memory entry'), specifies the allowed types ('rule, mistake, example, project, style, or role'), and the location ('under 00_Meta/AI-System'). It is specific and distinguishes from sibling tools like 'read_agent_memory' (read vs write) and note tools.

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?

The description provides no guidance on when to use this tool versus alternatives (e.g., 'append_to_note' or 'create_note'). There is no mention of prerequisites, exclusions, or context for appropriate usage.

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

Deploy Server

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