subfeed_list_models
List all available LLM models on Subfeed.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Subfeed
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It fails to disclose whether the operation is read-only, if authentication is required, or any side effects, remaining purely declarative.
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 entire description is one sentence of six words, front-loading the action and object with no extraneous detail.
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 simplicity (no parameters, no output schema), the description covers the core purpose. However, it could mention return format or any filtering limitations, so a slight deduction from perfect completeness.
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; the schema is empty. The description adds context about the scope ('all available') but no parameter semantics are needed, so baseline 4 applies.
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 uses the specific verb 'List' and names the resource 'all available LLM models on Subfeed,' clearly distinguishing it from sibling tools like list_addons and list_entities.
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 its use for listing LLM models but offers no explicit guidance on when to choose this over alternatives, such as list_addons or list_entities.
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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TDQS
B3.3/5.0
Disambiguation5/5
Each tool targets a clear, distinct operation: entity CRUD, addon management, discovery, interaction, and registration. While chat, invoke, and webhook all involve communication, their descriptions clearly differentiate entity chat from public invocation and webhook payloads.
Naming Consistency4/5
All tools share the 'subfeed_' prefix and use snake_case, with most following a verb_noun pattern (create_entity, delete_entity, list_addons). A few use bare verbs (chat, invoke, register) or noun-only (webhook), but the pattern is still recognizable and predictable.
Tool Count5/5
15 tools is at the upper edge of the ideal range, but it matches the server's broad scope covering entities, addons, models, discovery, and user registration. Each tool serves a distinct purpose and none feel redundant.
Completeness4/5
The surface covers full entity lifecycle (create, get, update, delete, publish), addon management (enable, disable, list), entity discovery, and user registration. Minor gaps like an explicit unpublish or webhook management can be worked around via update_entity or standalone webhook sending.