Structured map of LKA's public URLs and content sections. Equivalent to llms.txt — gives an AI grounding agent the full topology of the site so it knows what's worth crawling/calling.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description lacks behavioral details like destructive/read-only, rate limits, or side effects. It implies a safe, read-only map but doesn't state it explicitly.
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, no waste, front-loaded with core purpose and analogy to llms.txt.
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 zero parameters and no output schema, the description adequately conveys purpose and use case. Could enhance by clarifying output structure or confirming read-only nature.
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?
No parameters; description adds value by explaining the tool's output (structured map/topology) beyond the empty schema. However, it could detail the output format more concretely.
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 the tool's function: providing a structured map of public URLs and content sections, equivalent to llms.txt for AI grounding. It distinguishes itself from sibling tools by its role as a topology overview.
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?
Description implies usage before crawling or calling other endpoints, but does not explicitly state when not to use or contrast with alternatives among many sibling tools.
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.
