Compact organization overview (~500 tokens). Returns services, providers, schedules, active features, key counts, and an onboarding_status checklist showing what is configured vs missing (services, providers, availability, public agenda). Use as first call to orient yourself — cheaper than report_dashboard. If onboarding_status.ready is false, follow the missing steps before booking.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | No | ||
| orgSlug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It describes return content, token size (~500 tokens), and the meaning of onboarding_status (configured vs missing). It implicitly signals read-only behavior via 'overview' and 'orient yourself.' Slightly more explicit statement that it does not mutate state would earn a 5.
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?
Two tight sentences with front-loaded core purpose, then a compact list of return contents, and a clear usage recommendation. No wasted words.
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?
Despite no output schema, the description enumerates all key outputs and adds decision logic based on onboarding_status. The tool's complexity is low-to-moderate and the description fully covers what an agent needs to orient and proceed.
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?
Schema has 0% description coverage and the tool description does not mention orgSlug or apiKey. Parameter names are partially self-explanatory (orgSlug) but no semantic details are added. Since coverage is low, the description should compensate, but it does not.
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?
Description clearly states what the tool does: 'Compact organization overview' returning specific data categories. It lists exact content (services, providers, schedules, active features, key counts, onboarding_status checklist) and distinguishes itself from report_dashboard by being cheaper and intended as first call.
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?
Explicit guidance is provided: 'Use as first call to orient yourself — cheaper than report_dashboard.' It also gives conditional follow-up: 'If onboarding_status.ready is false, follow the missing steps before booking.' This establishes when to use it and how to react to output.
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.
Multiple parallel booking creation flows (booking_create, scheduling_book, public_booking_create) and session state transition tools (booking_update_status, lifecycle_transition) create ambiguity. While descriptions are detailed, an agent could easily select the wrong tool for a given task, especially with 111 tools to choose from.
Most tools follow a consistent domain_action pattern (e.g., client_create, service_update, comms_list_campaigns). Minor deviations include Spanish/English mixing (cierre_*, report_deuda_real) and a few noun-only names like org_summary, but the overall structure is predictable.
With 111 tools, the server is massively over-scoped for an MCP surface. Even for a broad service business domain, this exceeds reasonable limits and will overwhelm agents, making tool selection slower and more error-prone.
The tool surface is extremely thorough, covering org setup, services, providers, booking (internal/public/spec), finance, payroll, closing, disputes, clinical notes, treatment plans, and reporting. Gaps are rare and often intentional (e.g., no deliver via MCP, read-only treatment plans), so agents can complete most workflows end-to-end.