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Cachly — AI Cognitive Brain

Cachly — AI Cognitive Brain

by cachly-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

brain_from_ci

Ingest CI run outcomes into a persistent memory to track job status transitions—learning which jobs are fixed, broken, or stable from historical logs.

Instructions

Bulk-ingest CI run outcomes into the Brain — the brain_from_git equivalent for CI history. Feed it an array of {job, status, prev_status} objects from your CI system and it will learn which jobs have been fixed, broken, or are stable. Use it to bootstrap the Brain from historical CI logs.

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>",
      "outcomes": "<outcomes>"
    },
    "name": "brain_from_ci"
  }
}

Input Schema

NameRequiredDescriptionDefault
outcomesYesList of CI run outcomes to ingest
instance_idYesBrain instance ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.47

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states the tool will 'learn which jobs have been fixed, broken, or are stable' which implies internal state change. However, it does not disclose whether the operation is idempotent, destructive (overwrites existing data), or what happens on duplicate entries. This is acceptable for an ingest tool but could be more transparent.

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 concise sentences, each adding value: purpose, input format, and intended use case. No wasted words, front-loaded with the core action.

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 the tool has 2 parameters, no enums, no output schema, and a clear ingest purpose, the description is mostly complete. It explains what the tool does, what it expects, and when to use it. It could briefly mention if the operation is synchronous or batched, but for the complexity level, this is sufficient.

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 description coverage is 100%, so the schema already documents both parameters and their subfields. The description adds meaning beyond the schema by explaining the purpose of 'prev_status' ('needed to detect transitions') and the overall learning effect. This is adequate, though the description does not enumerate every subfield.

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: 'Bulk-ingest CI run outcomes into the Brain'. It specifies the action (ingest), resource (Brain), and the specific domain (CI history). The description also distinguishes the tool from its sibling 'brain_from_git' by explicitly mentioning equivalence, which helps differentiate.

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 usage guidance: 'Feed it an array of {job, status, prev_status} objects from your CI system' and 'Use it to bootstrap the Brain from historical CI logs.' While it does not explicitly list when not to use it or compare alternatives among siblings, the context is clear enough for a bulk-ingest operation.

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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