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Glama

ToolRouter

Brain Admin

brain_admin

Brain admin operations. Pass action: "lint" | "link" | "promote" | "rebuild_index" | "team_sleep". team_sleep enables/disables/runs the nightly team consolidation cycle — pass sub_action "enable" (with optional timezone), "disable", "status", or "run_now". Team admin role required for team_sleep actions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNoHuman-readable relationship label (for link).
actionYesAdmin operation to perform.
page_idNoSource brain page ID (for link/promote).
team_idNoTeam to promote to (for promote). Auto-resolved if on one team.
timezoneNoIANA timezone for team_sleep enable (e.g. "Europe/London"). Defaults to UTC. Sleep fires at 3 AM local time.
sub_actionNoRequired for action="team_sleep". "enable" turns on nightly sleep for the team; "disable" turns it off; "status" shows last run and schedule; "run_now" triggers an immediate sleep cycle.
to_page_idNoTarget page ID (for link).
relationshipNoRelationship type (for link, e.g. relates_to, derived_from).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

All annotations are false, so the description carries the full burden of behavioral disclosure. It does mention the team_sleep lifecycle and its admin requirement, but it does not explain the side effects or risks of link, promote, lint, or rebuild_index, whether operations are reversible, or what success/failure looks like. This is a significant gap for a mutating admin tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: scope, action list, then team_sleep-specific instructions. Each sentence contributes useful information without filler. The team_sleep sentence is longer than the others, but that complexity is inherent to the subcommand structure.

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

Completeness2/5

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

This is an eight-parameter administrative tool with no output schema and no informative annotations, yet the description only fully explains the team_sleep branch. The semantics, side effects, and result behavior for link, promote, lint, and rebuild_index are left almost entirely to parameter names and schema descriptions. That is insufficient for safe and correct use of the non-team_sleep actions.

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?

Schema description coverage is 100%, and every parameter already has a meaningful description and enum where appropriate. The description adds useful cross-parameter context for team_sleep (sub_action values, optional timezone, default UTC, 3 AM behavior), but much of that is also present in the schema, so the value added is incremental rather than transformative.

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 identifies an administrative resource ('Brain admin operations') and enumerates five specific executable actions: lint, link, promote, rebuild_index, team_sleep. This goes well beyond a tautology and tells an agent what the tool can do, though it does not explicitly distinguish the tool from siblings like brain_lint or brain_settings.

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 provides explicit usage guidance for the team_sleep subcommand: which sub_action values to pass, that timezone is optional, and that a team admin role is required. However, it gives no guidance on when to prefer brain_admin over the dedicated sibling tools, nor when individual actions like link vs promote vs rebuild_index should be chosen.

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

B3.4/5.0
Disambiguation4/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness4/5

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.