brain_settingsView and toggle your brain settings. Currently supports: drawers_enabled (store raw tool output alongside summaries for richer recall).
| Name | Required | Description | Default |
|---|---|---|---|
| value | No | New value (required for "set"). | |
| action | Yes | "get" lists current settings; "set" toggles a named setting. | |
| setting | No | Setting name (required for "set"). Currently: "drawers_enabled". |
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?
The description adds meaningful behavioral context beyond the annotations by explaining that drawers_enabled stores raw tool output alongside summaries for richer recall. It aligns with readOnlyHint=false and destructiveHint=false, and while it doesn't mention side effects or persistence, the annotation coverage lowers the burden.
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?
A single front-loaded sentence with one useful parenthetical; no filler or repetition of schema details. Every part contributes to understanding the tool.
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 two-action tool with full schema coverage and annotations, the description is largely complete: it defines the settings scope and explains the supported setting. Minor gaps are the lack of return-value hints for 'get' and no explicit usage guidance, but these are not critical for a tool this small.
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 is 100%, so the baseline is 3, but the description adds semantic value for the setting parameter by explaining the purpose of drawers_enabled. It does not add detail for action or value, but the schema already documents those sufficiently.
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 states a clear verb and resource: 'View and toggle your brain settings,' and names the one supported setting with its effect. It does not explicitly contrast with sibling tools such as brain_status or brain_update, so it stops short of a full 5.
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?
The description gives no guidance about when to prefer this tool over the many brain_* siblings, and it does not state exclusions or conditions for using get versus set. The schema covers mechanics, but the description leaves alternative-selection to inference.
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.
Tools are grouped by clear resource prefixes (account_, brain_, connector_, credential_, file_, job_, key_), and most actions have distinct purposes. A few boundaries overlap—brain_admin's lint action duplicates brain_lint, and account_preferences/setup/switch could momentarily confuse—but the descriptions resolve most ambiguity.
The dominant pattern is resource_verb for actions (file_read, job_cancel, key_create) and resource_noun for state views (credits_balance, brain_settings, account_preferences), which is readable. However, exceptions like discover, use_tool, top_up_credits, and feedback_request_tool break the pattern, and the set is not consistently verb_noun.
47 tools is well beyond the comfortable range; even though prefixes organize them, the agent faces a large selection surface with many narrowly scoped tools. A more consolidated set with action-based subcommands would be easier to navigate.
Core workflows are covered end-to-end: account setup and billing, connector and credential management, file CRUD, job polling, key lifecycle, brain knowledge management, and catalogue discovery/execution. Gaps are minor—outfit/persona/product/scene are list-only, connectors lack an update operation, and there is no explicit single-page brain get—but agents can generally work around them.