Skip to main content
Glama

list_facts

Retrieve structured facts as lightweight summaries, with optional filters for subject type, object type, and predicate.

Instructions

List facts as lightweight summaries.

Args: limit: Maximum results (1-100, default 50) subject_type: Filter by subject type object_type: Filter by object type predicate: Filter by predicate

Returns: List of fact summaries

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
predicateNo
object_typeNo
subject_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as potential side effects, authentication requirements, or rate limits. The phrase 'lightweight summaries' hints at limited data but is insufficient for full transparency.

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 extremely concise: a one-sentence purpose followed by a structured list of parameters. Every element is necessary, and the most important information (purpose) is front-loaded.

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

Completeness3/5

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

Given the presence of similar sibling tools (query_facts, search_facts), the description lacks differentiation. It does not explain what 'lightweight summaries' means or contrast with other listing/search tools. An output schema exists but is not described.

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?

The description adds meaning to parameters beyond the input schema, e.g., 'limit: Maximum results (1-100, default 50)' and clarifies that subject_type, object_type, and predicate are filters. However, the schema had 0% description coverage, so the baseline is lower, but the description partially compensates.

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

Purpose4/5

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

The description clearly states the action ('List') and the resource ('facts'), and specifies the output as 'lightweight summaries'. It is specific and distinct from generic verbs, but does not differentiate from similar sibling tools like 'query_facts' or 'search_facts'.

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 is provided on when to use this tool versus alternatives (e.g., 'query_facts', 'search_facts') or when not to use it. The description only lists parameters without usage context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/michaelkrauty/mcp-notes'

If you have feedback or need assistance with the MCP directory API, please join our Discord server