Cachly — AI Cognitive Brain

fedbrain_search

Search shared knowledge across tech stacks, ranking results by contextual relevance to your stack. Shows certificate provenance, confirm_count, and Gold Standard badges.

Instructions

FedBrain context-weighted search: Search the global commons, weighting results by tech-stack similarity. Brains with matching domain context (Go/Kubernetes/Postgres) rank higher than unrelated stacks. Shows certificate provenance, confirm_count, and Gold Standard badges.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "instance_id": "<instance_id>",
      "query": "<query>"
    },
    "name": "fedbrain_search"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max results (default: 10)
`query`	Yes	What to search for
`instance_id`	Yes	Brain instance ID
`context_hints`	No	Your tech stack, e.g. ["go", "kubernetes", "postgres"]

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.9.9May 6, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions result features (badges, provenance) but lacks details on side effects, permissions, or rate limits.

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?

Two sentences: first states the purpose, second provides critical details on ranking and displayed information. No wasted text.

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?

Covers the search mechanism and output features, but without an output schema, the description should more thoroughly explain return format (e.g., pagination) and domain-specific terms like 'certificate provenance'.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds meaning by explaining context_hints as 'Your tech stack' and how weighting works, going beyond the schema's basic descriptions.

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 it is a 'FedBrain context-weighted search' that searches the global commons and ranks results by tech-stack similarity, distinguishing it from simpler sibling tools like brain_search.

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 implies when to use (context-weighted search) but does not explicitly state when not to use or provide alternative tools for simple searches.

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

Deploy Server