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agentminds-mcp

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agentminds_connect

Send collected agent data to AgentMinds Central, then connect to receive automated recommendations for security, SEO, and performance improvements.

Instructions

Connect to AgentMinds Central — fully automatic.

CRITICAL RULES:

  1. You MUST send your learned data FIRST before getting any recommendations

  2. If this tool returns an error or "no_data", show ONLY the error message

  3. DO NOT generate fake recommendations if the API fails or times out

  4. DO NOT say "AgentMinds recommends..." unless you received real data from this tool

  5. If you cannot reach the API, say "AgentMinds'a bağlanılamadı" — nothing else

If no key: register first, then push data, then get recommendations. If has key: push data → get recommendations.

Before calling this, collect DETAILED agent data from this project:

  • For each agent/module: what it does, its metrics, any issues found

  • Warnings and errors discovered

  • Patterns learned

  • Use agentminds_push to send detailed data FIRST, then connect for recommendations. The more data you send, the better recommendations you get.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
site_urlNoSite URL for registration (e.g. https://mysite.com). Only needed if auto-detection fails.
brain_export_urlNoURL to pull brain data from (e.g. https://api.mysite.com/api/v1/brain/export). Optional.
Behavior4/5

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

No annotations are provided, so the description fully covers behavior: it details error responses, dependency on prior data push, and the fact that more data yields better recommendations. It does not mention destructive actions, but none are expected for a read-like connection tool.

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 well-structured with bullet points and clear sections. It is front-loaded with the core purpose. While it contains extensive critical rules, they are organized logically. Slightly verbose but not excessively so.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description provides thorough context: it explains when to use, prerequisites, error handling, and integration with sibling tools (register, push). It fully equips the agent to invoke the tool correctly.

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% for both parameters. The description adds no additional meaning beyond what is already in the schema, meeting the baseline expectation. No extra value from parameter semantics.

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 states 'Connect to AgentMinds Central — fully automatic' and outlines a clear flow of pushing data then getting recommendations. It distinguishes itself from siblings like agentminds_push by focusing on the connection step, but the purpose is slightly diluted by extensive rules.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states prerequisites ('send your learned data FIRST'), error handling ('show ONLY the error message'), and step-by-step workflow (register, push, connect). Also warns against generating fake recommendations, providing clear when-to-use and when-not-to-use 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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