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Cachly — AI Cognitive Brain

Cachly — AI Cognitive Brain

by cachly-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

ckg_inspect

Inspect the causal knowledge graph for any concept, revealing typed relationships like fixes, requires, and causes with Bayesian confidence scores to identify high-confidence fixes and related concepts.

Instructions

Inspect the Causal Knowledge Graph (CKG) for a concept. Shows all typed edges (fixes, requires, co-occurs, causes) with Bayesian confidence scores. Use to understand what the brain knows about a topic and which fixes have the highest confidence. Also shows related concepts via graph traversal.

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>",
      "concept": "<concept>"
    },
    "name": "ckg_inspect"
  }
}

Input Schema

NameRequiredDescriptionDefault
conceptYesConcept to inspect, e.g. "fix:clickhouse-ipv6" or "docker"
max_hopsNoTraversal depth (default: 2)
instance_idYesBrain instance ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.9

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description must fully disclose behavior. It mentions showing typed edges and confidence scores but does not explicitly state that the tool is read-only (no mutations), nor does it discuss side effects, authentication needs, or rate limits. Score 3 for partial disclosure.

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 three sentences, front-loaded with the core action, and every sentence contributes to understanding. It is efficient and well-structured.

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 explains the tool's output (typed edges, confidence scores, related concepts) but lacks details on output format, pagination, or traversal specifics. Given the absence of an output schema, the description could be more comprehensive for a knowledge graph tool with three parameters.

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 coverage is 100%, so baseline is 3. The description adds a small amount of value by giving example values for the 'concept' parameter (e.g., 'fix:clickhouse-ipv6') but does not elaborate on 'max_hops' beyond the schema or explain defaults. Score 3 for marginal added meaning.

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 clearly states the verb 'inspect' and the resource 'Causal Knowledge Graph' for a concept. It lists the edge types shown, but does not explicitly differentiate from siblings like causal_trace or brain_search, capping the score at 4.

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 says 'Use to understand what the brain knows about a topic and which fixes have the highest confidence.' This implies appropriate context but provides no explicit exclusions or comparisons to other tools (e.g., when to use causal_trace instead). Score 3 reflects moderate guidance.

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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