Aggregate stats for a city: # programs, # daycares, % claimed by owners, % with Spanish description, % free, category breakdown. Useful for AIs answering 'how many youth programs are there in Lodi' or comparing Lodi to other cities.
| Name | Required | Description | Default |
|---|---|---|---|
| city_slug | No | City slug (e.g. 'lodi'). Defaults to 'lodi' if omitted. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It describes the output (stats) but does not explicitly state read-only behavior, auth requirements, or any side effects. The information is adequate for a non-destructive aggregation tool.
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 concise sentences with no redundancy. The first sentence lists key outputs, the second provides typical use cases. Front-loaded and efficient.
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?
For a tool with one optional parameter and no output schema, the description covers the necessary scope. It could mention data freshness or interpretation of percentages, but the core information is sufficient.
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 coverage is 100% with one optional parameter. The description adds that city_slug defaults to 'lodi', which goes beyond the schema description and clarifies behavior.
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 lists specific aggregated statistics (programs, daycares, percentages) and provides example queries, making the tool's purpose clear and distinct from sibling tools like get_program or search_programs.
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 explicitly suggests use cases (answering counts or comparisons), which guides when to use it. While it doesn't mention alternatives, the sibling list implies no other tool provides city-level aggregation.
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.
Tools are clearly grouped by prefix (admin_, daycare_, org_, parent_) and within each group have distinct, non-overlapping purposes. For example, parent_add_kid vs parent_remove_kid are unambiguous.
All tools follow a consistent snake_case pattern with role prefix and action_noun structure. Minor exceptions like 'nearby_programs' are standalone and do not disrupt the overall consistency.
With 72 tools, this is far beyond the typical well-scoped range of 3-15. While the tools cover multiple user roles, the count is excessive and likely overwhelming for an agent, falling into the 'too many' category.
The tool set covers most core workflows for parents, orgs, and admins, but notable gaps exist: there is no tool to create a program or daycare, and admin tools for approving pending claims or discoveries are missing, leaving dead ends.
