get_labGet one Lab by slug, including formulas, assumptions, related SANTISMM content and its executable endpoint when one exists. Use this after list_labs or search_all; use the named calculate_* tool rather than reimplementing a published formula.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Lab slug, e.g. 'evaluation-sample-size'. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false). The description adds behavioral context about response contents and the conditional nature of the endpoint ('and its executable endpoint when one exists'), which is not expressed in annotations or the schema.
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 sentences with no filler. The primary action is front-loaded, followed by content details and usage guidance, then the alternative tool. Every sentence earns its place.
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?
With a single parameter, complete annotations, an output schema, and a description that covers sequencing and alternatives, nothing essential is missing. An agent can confidently invoke this tool after reading the description.
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 schema has 100% description coverage for the sole 'slug' parameter, including an example. The description mentions 'by slug' but adds no meaning beyond the schema. Baseline 3 is appropriate because the schema fully documents the parameter.
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 states a specific verb and resource: 'Get one Lab by slug', and enumerates what is included (formulas, assumptions, related SANTISMM content, executable endpoint). It explicitly distinguishes itself from calculate_* siblings by warning against reimplementing a published formula, and the resource name separates it from other get_* tools.
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 gives explicit sequencing: 'Use this after list_labs or search_all', and names the alternative tool category: 'use the named calculate_* tool rather than reimplementing a published formula.' This tells an agent both when and when not to use this tool.
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.
Every tool targets a distinct operation and identifier: search is the entry point, list_* returns browsing summaries, get_* returns a single unit, get_related traverses the graph, and get_overview maps the corpus. Even the similar get_homeric_* trio is cleanly separated by episode/place/route.
All names follow snake_case verb_noun: get_* for singular retrieval, list_* for enumeration, plus search. get_related and get_overview are the only deviations but remain predictable read operations.
21 tools is above the typical 3-15 range, but the count is justified by the number of distinct corpora and the consistent list/get pairing for each; there are no redundant tools, so it is only slightly heavy.
The server offers a complete read-side lifecycle for this knowledge corpus: overview, search, list, get, and graph traversal. For a read-only knowledge server, there are no obvious dead ends; coverage of claims, patterns, architectures, governance, handbook and Homeric atlas is thorough.