Brain status
brain_statusView brain page counts and recent pages for your personal and team brain.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
brain_statusView brain page counts and recent pages for your personal and team brain.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 establish read-only, idempotent, non-destructive behavior, so the description only needs to add context. It adds the scope ('personal and team brain') but does not disclose return shape, pagination, or any other behavioral details.
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 sentence front-loads the action and object, then narrows scope. No filler or 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 zero-parameter, read-only status tool, the description covers the essential information an agent needs. It could be more explicit about what a 'recent pages' response contains, but the annotations and simple profile keep the gap minor.
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?
The tool has zero parameters and the schema description coverage is 100%, so the description bears no parameter-documentation burden. It clearly conveys what the operation reports.
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 uses a specific verb ('View') and identifies clear resources: 'brain page counts and recent pages' for personal and team brain. It is distinct enough from siblings like brain_query, though it does not explicitly name an alternative.
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 purpose implies when to use it — when the user needs brain page counts or recent pages as status information. It does not explicitly state when not to use it or compare it with related brain tools like brain_query or brain_settings.
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.