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Glama

ToolRouter

by io.github.Humanleap

Search brain

brain_query
Read-onlyIdempotent

Search your knowledge brain for relevant information. Returns pages ranked by relevance. Optional wing/hall narrow the search to a slice of the brain (e.g. wing="engineering", hall="preference").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hallNoOptional. Restrict to a hall: "fact", "event", "discovery", "preference", or "advice".
wingNoOptional. Restrict to a specific wing (top-level area like "engineering", "family").
as_ofNoOptional. ISO-date string (e.g. "2026-01-15"). When set, returns pages that were valid at that point in time — including superseded versions. Useful for "what did we know in January?" queries.
limitNoMax results. Defaults to 10.
queryNoSearch query. Omit to list all pages by recency.
scopeNoWhich brain scope to search. Defaults to all.
include_drawer_previewsNoOptional. When true, each result includes the first ~400 chars of its linked drawer (raw source text) if one exists. Useful to decide whether to call brain_expand for the full text.

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description's main job is to add behavioral context. It adds 'returns pages ranked by relevance' and the narrowing semantics, but it does not disclose details such as the shape of returned pages or how temporal as_of results behave beyond what the schema already states.

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 description is two sentences with no wasted words: the first states the purpose and result, the second gives the most important optional-filter behavior with a concrete example. It is appropriately sized and front-loaded.

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

Completeness3/5

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

The description gives a usable mental model for a search tool, and annotations plus the detailed input schema cover safety and parameters. However, there is no output schema, and the description leaves the result shape and the relationship to brain_expand implicit, so it is not fully complete on its own.

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%, so the schema carries the parameter documentation burden. The description's concrete wing/hall example is a small addition, but it does not meaningfully explain parameters beyond what the schema already provides.

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 uses a specific verb and resource ('Search your knowledge brain') and states the output ('pages ranked by relevance'). It is clear, but it does not explicitly distinguish itself from sibling search-like tools such as discover or brain_expand.

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 tool's purpose implies when it should be used, and the example of wing/hall filtering gives context for narrowing a search. However, there is no explicit guidance about when to choose this over sibling tools or situations where it should not be used.

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.

Resources