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Cachly — AI Cognitive Brain

Cachly — AI Cognitive Brain

by cachly-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

brain_marketplace

Browse curated brain packs grouped by domain, built from community-confirmed lessons. Install one to bootstrap knowledge in unfamiliar areas like Kubernetes, Auth, or React.

Instructions

Browse the Domain Brain marketplace — curated, installable packs of high-trust community lessons, grouped by domain (Kubernetes, Auth, Database, React, Payments, …). Each brain is built from verified, community-confirmed lessons in the global Knowledge Commons. Use at onboarding or when starting work in an unfamiliar domain to bootstrap your Brain instantly. Install one with brain_install(slug="..."). Example: brain_marketplace() → "☸️ Kubernetes Incident Brain · 42 lessons · install: brain_install(slug="k8s")".

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "brain_marketplace"
  }
}

Input Schema

NameRequiredDescriptionDefault
min_confirmsNoOnly count lessons with at least this many community confirmations (default: 1)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.47

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. The description implies a read-only operation ('Browse') and shows an example output without side effects. It does not explicitly state that this is a non-destructive, safe-to-call operation, but the context is strong enough to infer. The description adds value by explaining the nature of the marketplace (curated, community-verified lessons), but a more explicit statement about no state changes would elevate it.

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 concise and well-structured: a clear opening sentence, contextual background, usage guidance, and an example. Every sentence serves a purpose without redundancy. It is front-loaded with the core action and then provides supporting details.

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?

The tool has no output schema, so the description must explain the return value. It provides an example output ('☸️ Kubernetes Incident Brain · 42 lessons · install: brain_install(slug="k8s")'), which gives a good idea of the format. However, it does not explicitly state that the function returns a list of such strings (or a single string), which would improve completeness. Given the simplicity of the tool (1 optional parameter), the description is very close to complete.

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?

The input schema has 100% coverage with a description for the only parameter (min_confirms: 'Only count lessons with at least this many community confirmations (default: 1)'). The main description does not mention the parameter at all. Since the schema already provides full meaning, the description adds no additional value beyond the schema, meeting the baseline of 3.

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 clearly states the tool's purpose: 'Browse the Domain Brain marketplace' and lists it as a listing of curated, installable packs. The verb 'browse' specifies the action, and the resource 'marketplace' is distinct from sibling tools like brain_install (which installs) and brain_search (which searches specific brains). The example output further clarifies the tool's behavior.

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

Usage Guidelines5/5

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

The description explicitly recommends when to use the tool: 'Use at onboarding or when starting work in an unfamiliar domain.' It also provides an alternative by mentioning brain_install, stating 'Install one with brain_install(slug="...")'. This gives clear guidance on when to use this tool versus a sibling.

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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