Skip to main content
Glama
cachly-dev

Cachly — AI Cognitive Brain

brain_seed_starter

Seeds a fresh Brain with curated engineering lessons covering Docker, JWT, Postgres, K8s, and more, enabling the first smart recall to return useful hits instead of nothing.

Instructions

Seed a fresh Brain with a curated set of universal, high-value engineering lessons (Docker layer cache, JWT clock skew, Postgres migration locks, K8s OOM limits, N+1 queries, cache stampede, CORS preflight, and more). Makes the very first smart_recall return a useful hit instead of nothing — ideal right after setup or in a fresh repo with no git history to learn from. Starter lessons are tagged source:"starter", never override your own lessons, and are idempotent (won't double-seed). Example: brain_seed_starter(instance_id="...") · brain_seed_starter(instance_id="...", topic_filter=["docker","redis"])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoRe-seed even if already seeded; also overwrites same-topic lessons. Default false.
dry_runNoPreview which starter lessons would be seeded without writing.
instance_idYesUUID of the Brain instance to seed.
topic_filterNoOnly seed lessons whose topic or tags match one of these strings (e.g. ["docker", "auth"]). Omit to seed all.
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that lessons are tagged source:'starter', never override existing lessons, and are idempotent. However, it omits details about potential side effects (e.g., database writes, permission requirements, or performance impact), leaving some behavioral aspects unclear.

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 a single paragraph of 4 sentences, front-loaded with the core action and followed by usage context, properties, and an example. It is efficient but could be trimmed slightly (e.g., 'universal, high-value' is slightly verbose). Still well-structured.

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?

Given 4 parameters (1 required), no output schema, and no annotations, the description covers the tool's purpose, usage context, idempotency, tagging behavior, and example calls. The connection to smart_recall is explained. It is complete enough for an agent to decide when and how to invoke this tool.

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% (all 4 parameters have descriptions). The description adds value by showing example invocations and explaining topic_filter in context, but the schema already defines each parameter adequately. Baseline 3 is appropriate.

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 defines a specific verb (seed) and resource (Brain) with clear content (curated engineering lessons). It distinguishes from siblings by referencing smart_recall and positioning itself for fresh setups, avoiding overlap with tools like brain_import or brain_discover.

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 gives explicit context ('ideal right after setup or in a fresh repo') and idempotency guarantees, but does not mention when not to use the tool or name alternative siblings beyond smart_recall. The usage is clear but lacks exclusionary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/cachly-dev/cachly-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server