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u2-mcp

u2-mcp

by bpamiri
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_knowledge

Lists all saved knowledge topics with summaries, allowing you to identify and retrieve specific content from your database.

Instructions

List all saved knowledge topics.

Returns a list of topics that have been saved about this database. Use get_knowledge_topic to retrieve the full content of a specific topic, or get_all_knowledge to retrieve everything at once.

Returns: Dictionary containing list of topics with summaries.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_knowledge"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, but the description clearly indicates the tool is a read-only list operation. It specifies the return type is a dictionary containing a list of topics with summaries, and notes the relationship to other tools. However, it does not mention performance or authorization requirements.

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 three concise sentences. It front-loads the primary purpose, then explains the return value, and ends with usage guidance for alternatives. Every sentence is useful with no redundancy.

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

Completeness5/5

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

Given no parameters, an output schema (presumably detailed), and a set of sibling tools, the description is complete. It states the tool's behavior, output format, and points to relevant alternatives. There are no significant gaps.

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

Parameters4/5

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

The tool has zero parameters, so the baseline is 4. The description adds no parameter-specific information, which is acceptable. It does describe the return format, adding value beyond the input schema.

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 lists all saved knowledge topics. It explicitly differentiates from siblings by recommending get_knowledge_topic for full content and get_all_knowledge for retrieving everything at once.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool versus alternatives: use get_knowledge_topic for a specific topic's full content, or get_all_knowledge to retrieve everything at once.

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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