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agentmindsdev

agentminds-mcp

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agentminds_push

Push detailed agent reports with severity, metrics, warnings, recommendations, and learned patterns to AgentMinds Central to receive improved analysis and insights.

Instructions

Push detailed agent data to AgentMinds Central. IMPORTANT: Send FULL data, not just names and scores.

Each agent report MUST include:

  • severity: "critical" | "warning" | "info"

  • summary: what the agent found (1-2 sentences)

  • metrics: key numbers (e.g. {total_leads: 567, bounce_rate: 3.5, open_rate: 12})

  • warnings: array of issues found [{severity: "warning", message: "..."}]

  • recommendations: array of suggested fixes [{title: "...", priority: "high"}]

  • memory.learned_patterns: what the agent learned [{pattern: "...", category: "...", confidence: 0.9, status: "active", impact: "high"}]

The MORE detail you send, the BETTER recommendations you get back. Empty data = empty recommendations.

Example: {agent: "lead_hunter", report: {severity: "warning", summary: "567 leads found but 0% open rate", metrics: {total_leads: 567, emails_found: 230, open_rate: 0, bounce_rate: 8.3}, warnings: [{severity: "critical", message: "0% email open rate - emails likely going to spam"}], recommendations: [{title: "Warm up email domain before bulk sending", priority: "critical"}]}, memory: {learned_patterns: [{pattern: "cold_email_spam", category: "email_deliverability", confidence: 0.9, status: "active", impact: "critical", detail: "Bulk cold emails without domain warmup go to spam"}]}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reportsNoArray of DETAILED agent reports. Each must have: agent, report (severity, summary, metrics, warnings, recommendations), memory (learned_patterns)
brain_export_urlNoURL for Central to pull brain data from (alternative to sending reports directly)
Behavior4/5

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

No annotations provided, so the description carries full burden. It describes the expected input format and the consequence of not sending enough detail. It does not mention authentication or side effects, but the main behavior is well-covered.

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 longer due to the example, but the structure is logical: purpose, important note, bullet points, and a complete JSON example. It is front-loaded and every part serves a purpose.

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 complexity of the nested arrays and no output schema, the description comprehensively explains the required input structure. It leaves no ambiguity about what data to send.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds extensive detail beyond the schema: it specifies required fields, structure, and an example for the 'reports' parameter, and explains the 'brain_export_url' as an alternative.

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 action ('Push detailed agent data to AgentMinds Central') and provides a specific resource. It distinguishes from sibling tools (e.g., agentminds_actions, agentminds_status) by focusing on data submission.

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 instructs to send full data and warns that empty data yields empty recommendations. It lacks an explicit when-not-to-use clause, but the context is clear.

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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