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Glama

emdly

by com.emdly

Brain Search

brain_search

Use this BEFORE answering anything about the user's own context — their projects, conventions, decisions, people, infrastructure, past work. Searches the user's Brain files (their brains plus those shared with them); every word of the query is matched on its own, case-insensitively, and files are ranked by how many words hit. Returns the matching files with the surrounding lines so you can decide what to brain_get. Needs a signed-in user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brainNoOptional brain to search (owner-handle/name for shared brains). Omit to search all.
queryYesKeywords to look for; each word is matched on its own, case-insensitively.

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / query / description
      Previous value: -"Case-insensitive phrase to look for."New value: +"Keywords to look for; each word is matched on its own, case-insensitively."
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and it does well: it discloses word-by-word case-insensitive matching, ranking by hit count, return of surrounding lines, scope over owned/shared brains, and a signed-in user requirement. It omits failure modes and limits, but the behavioral detail is well above average.

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 front-loaded with the critical 'use before' guidance, then gives matching semantics, result shape, and a dependency in three compact sentences. Every sentence earns its place with no filler or repetition.

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

Completeness4/5

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

For a two-parameter search tool with no output schema, the description covers the trigger condition, scope, ranking behavior, return format, and authentication prerequisite. It does not mention pagination or limits, but nothing essential is missing for a simple search call.

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 baseline is 3. The description reinforces the query matching behavior but adds little beyond the schema's own parameter descriptions; the brain parameter also repeats what the schema already states. No harmful gaps, but no notable value added beyond the structured data.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action and resource: it searches the user's Brain files, including shared brains. It also distinguishes itself from brain_get by positioning search as the step that lets the agent decide what to retrieve. The resource is clear enough to separate it from sibling tools like search_skills.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use this 'BEFORE answering anything about the user's own context' and lists concrete categories such as projects, conventions, decisions, people, infrastructure, and past work. It does not name alternative tools for exclusions, but the when-to-use guidance is strong and actionable.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: brain_get/brain_list/brain_search/brain_write cover memory operations, while get_collection/get_skill/install_skill/search_skills/submit_skill handle skill discovery, retrieval, installation, and submission. No two tools overlap in function; even the search vs get pairs are distinct.

Naming Consistency3/5

The brain_* prefix provides a consistent pattern for memory tools, but the skill tools use mixed conventions: get_collection, get_skill, install_skill, search_skills, submit_skill — no shared prefix or uniform verb_noun structure. This inconsistency is noticeable but each name is still readable.

Tool Count5/5

With 9 tools covering two domains (memory and skills), the count is well-scoped. Each tool earns its place, and the number is within the ideal range for a focused server.

Completeness4/5

The brain tools cover list, get, search, and write, but lack an explicit delete operation. The skill tools cover search, get, install, submit, and collection listing, but no uninstall. These are minor gaps that agents can work around; the core workflows are complete.

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