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agentmem

agentmem

by Thezenmonster
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_memory

Search active agent memories by relevance, trust, and recency to find coding context, decisions, or procedures. Supports natural language queries and filters by memory type.

Instructions

Full-text search across all active memories. Returns results ranked by relevance, trust status, and recency. Deprecated and superseded memories are excluded automatically.

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": "search_memory"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSearch query. Supports natural language and keywords.
typeNoFilter results to a specific memory type (bug, decision, setting, procedure, context, feedback, session)
limitNoMaximum number of results to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.1
    • addedInput schema / properties / limit / description
      Added value: +"Maximum number of results to return"
    • addedInput schema / properties / query / description
      Added value: +"Search query. Supports natural language and keywords."
    • addedInput schema / properties / type / description
      Added value: +"Filter results to a specific memory type (bug, decision, setting, procedure, context, feedback, session)"
  2. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it's a read-only search operation (implied by 'search'), specifies result ranking criteria ('relevance, trust status, and recency'), and mentions automatic exclusions ('deprecated and superseded memories are excluded automatically'). This adds valuable context beyond the input schema, though it could detail more on permissions or rate limits.

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 highly concise and well-structured, consisting of two sentences that efficiently convey purpose, behavior, and exclusions without any wasted words. It is front-loaded with the core functionality and follows with important behavioral details, making it easy to parse and understand quickly.

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

Completeness4/5

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

Given the tool's moderate complexity (search operation with 3 parameters), no annotations, and no output schema, the description is largely complete. It covers purpose, behavior, and exclusions adequately for an agent to use the tool correctly. However, it could be more complete by mentioning the output format or any limitations, which would help compensate for the lack of output schema.

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%, so the input schema already documents all parameters thoroughly. The description adds minimal semantic value beyond the schema, as it doesn't explain parameter interactions or provide additional usage context for the parameters. The baseline score of 3 is appropriate since the schema does the heavy lifting, and the description doesn't compensate with extra insights.

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 tool's purpose with specific verbs ('full-text search across all active memories') and distinguishes it from siblings by specifying scope ('active memories', 'deprecated and superseded memories are excluded automatically'). It explicitly identifies the resource being searched (memories) and the operation (search), differentiating from tools like list_memories or recall_memory.

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

Usage Guidelines3/5

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

The description implies usage context by stating it searches 'active memories' and excludes 'deprecated and superseded memories', which suggests when this tool is appropriate versus alternatives like list_memories. However, it doesn't explicitly state when to use this tool versus other search-related siblings or provide clear exclusions or prerequisites, leaving some ambiguity.

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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