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Cachly — AI Cognitive Brain

Cachly — AI Cognitive Brain

by cachly-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

team_confirm

Endorse a team lesson with a senior or peer review to boost its recall rank and mark it with a shield badge. Use in code reviews to canonize the best solution.

Instructions

Endorse (review-confirm) a team lesson so trusted, human-reviewed knowledge ranks above unreviewed auto-learned entries. A senior review weighs more than a peer review; distinct endorsements add a small boost. Confirmed lessons surface higher in smart_recall and team_recall and carry a 🛡️/✔️ badge. Use this in code review or knowledge reviews to bless the canonical solution for a topic.

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>",
      "topic": "<topic>",
      "reviewer": "<reviewer>"
    },
    "name": "team_confirm"
  }
}

Input Schema

NameRequiredDescriptionDefault
noteNoOptional review note (kept in the lesson audit trail)
levelNoReview weight — "senior" ranks higher than "peer" (default: peer)
topicYesTopic slug of the lesson to confirm (e.g. "deploy:api")
reviewerYesYour name or handle (the reviewer endorsing this lesson)
instance_idYesUUID of the shared team brain instance

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.47

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description must carry the burden. It explains the ranking effect, badge, and senior vs peer weight, but does not cover reversibility, idempotency, permissions, or the immediate return result. This leaves gaps for a full behavioral understanding.

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?

Four sentences, front-loaded with purpose, no redundancy. Every sentence earns its place by explaining effect, usage, or parameter behavior.

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 covers the tool's purpose and effect on recall, but lacks information about return values, success/failure indicators, or prerequisites. Given no output schema, the description should at least hint at the response.

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 the nuance that 'distinct endorsements add a small boost' for the 'level' parameter, which is not in schema. No other parameters gain new meaning, so the added value is marginal.

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 verb (endorse/confirm) and resource (team lesson), and distinguishes it from auto-learned entries. It explains the ranking effect and badge, which sets it apart from other tools like brain_confirm_ci or recall tools.

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 explicitly recommends use in 'code review or knowledge reviews' and describes the outcome (higher ranking, badge). It does not list when not to use or name alternatives directly, but the context is clear enough for an agent to decide.

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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