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Memex

Memex

by narphorium
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

search_knowledge_base

Find relevant information from your stored content by searching your knowledge base with natural language queries.

Instructions

Search through previously analyzed content in the knowledge base using natural language. This tool helps you find relevant information from your stored content.

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_knowledge_base"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesWhat information you're looking for in your knowledge base

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations provided, so description must carry the full burden. It suggests a read operation ('search through') but does not explicitly confirm non-destructive behavior or disclose limitations like search scope or result format.

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?

Two sentences with no redundant information. Front-loaded with the action and resource, efficient and clear.

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?

No output schema exists, yet description does not describe return values (e.g., snippets, relevance scores). Lacks details on search behavior, pagination, or result scope, making it incomplete for a search tool.

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% with one parameter described as 'What information you're looking for in your knowledge base'. Description adds 'using natural language', which provides minor extra context beyond the schema, but not significant value.

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?

Description clearly states 'Search through previously analyzed content in the knowledge base using natural language', with a specific verb and resource. It distinguishes from sibling tools like 'analyze_web_content' and 'save_artifact' which have different purposes.

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 for finding stored content but does not explicitly state when to use it over siblings or provide exclusions. No alternatives or conditions mentioned.

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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