list-modelsList available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.
| Name | Required | Description | Default |
|---|---|---|---|
| thinking_level | No | Filter to a specific thinking level |
| Name | Required | Description | Default |
|---|---|---|---|
| levels | Yes | Available thinking level tiers |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only and idempotent. The description adds valuable context by explaining the grouping by thinking level and the type of information returned (credit costs, capabilities), which goes beyond the structured annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action and followed by key output details and a usage pointer. Every word adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and an output schema, the description fully covers purpose, content, and usage context. It sufficiently complements the schema and annotations, making it complete for an agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage for the sole parameter is 100%, so the schema documents it fully. The description mentions 'thinking level' but does not add new semantic details beyond what the schema and enum already provide, which aligns with the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists available AI models grouped by thinking level, with specific details on what is shown (default models, credit costs, capabilities). It distinguishes itself from all sibling tools by focusing on model listing rather than sessions or code operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises using this tool 'before consult', providing a clear context for when to invoke it. It does not enumerate exclusions or alternatives, but the single usage hint is actionable and sufficient for this simple tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set separates general consultation (consult-council) from specialized councils for tradeoffs, debugging, architecture, implementation planning, and code review. Session management and utility tools (logs, usage, models, sessions) are clearly distinct. Only minor potential overlap exists between consult-council and the specialized councils, but descriptions clarify the purpose of each.
All tools follow a consistent verb_noun pattern in snake_case (e.g., consult-council, get-session, list-models). Verbs are varied but predictable, and compound nouns are hyphenated consistently. No mixed conventions.
13 tools is appropriate for a platform that offers multiple council workflows plus session, log, and usage management. Each tool addresses a distinct need without unnecessary bloat.
The server covers the full lifecycle of a council-based workflow: model discovery (list-models), execution (consult-council and specialized councils), session retrieval (list-sessions, get-session), sharing (get-thread-link, set-thread-visibility), and operational support (check-usage, get-logs). Missing delete/update session tools are minor and not essential for the core purpose.