List all companies in the operator's portfolio with basic metrics (lead/quote/job counts).
| Name | Required | Description | Default |
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No arguments | |||
| Name | Required | Description | Default |
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No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, confirming the tool is safe to use. The description adds value by specifying the returned metrics (lead/quote/job counts), but does not mention potential limitations like pagination or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the key action and output. Every word serves a purpose, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and an existing output schema, the description fully explains what the tool returns. It provides sufficient context for an agent to understand the tool's functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema coverage is 100%. According to guidelines, the baseline is 4 for zero-parameter tools, and the description does not need to add parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list', the resource 'companies in the operator's portfolio', and the specific metrics (lead/quote/job counts). It effectively distinguishes from sibling tools like operator.dashboard by specifying the scope and output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidelines on when to use this tool versus alternatives or when not to use it. Although its purpose is clear, the lack of usage context limits guidance for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct resource+action naming (e.g., leads.create, jobs.list), but some overlap exists in analytics tools (dashboard, detailed, financials) and workflow automations (process_lead vs google_ads_pipeline). Descriptions clarify purposes, so slight confusion is possible but limited.
All tools follow a consistent `domain.action` pattern (e.g., leads.create, billing.status). No mixing of camelCase or snake_case. Even complex names like lifecycle.assess or scaling.readiness_score adhere to the convention.
95 tools is far beyond typical well-scoped servers (3-15). While the server aims to cover an entire business management platform, this volume is overwhelming for an agent, making selection and memory difficult.
The tool surface is exceptionally comprehensive, covering formation, hiring, compliance, funding, leads, quotes, jobs, invoicing, payments, marketing, analytics, integrations, webhooks, workflows, and more. Almost no obvious gaps in the lifecycle of running a trade business.