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 | |||
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?
With annotations already declaring readOnly, idempotent, and non-destructive behavior, the description adds meaningful context about what the response will contain and notes the conditional executable endpoint. It does not overload with safety claims but supplies useful behavioral detail beyond the structured annotations.
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 carry the essential information with no waste: what the tool returns, when to call it, and which sibling tool to prefer for formula computation. The most important scoping is front-loaded in the first sentence.
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 simple single-parameter fetch tool, the description fully equips an agent: it names the resource, the lookup key, the included content, the conditional endpoint, and the appropriate usage sequence. The presence of an output schema removes the need to describe return fields further.
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 description coverage is 100%, so the schema already documents the slug parameter with an example. The description adds no new parameter semantics beyond saying 'by slug', but that is acceptable since the schema covers it completely.
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 uses a specific verb and resource ('Get one Lab by slug') and enumerates the payload contents: formulas, assumptions, related SANTISMM content, and executable endpoint when present. It clearly differentiates from list_labs (listing) and calculate_* tools (computing formulas) among the sibling set.
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 instructs when to use this tool: 'Use this after list_labs or search_all'. It also names the alternative category ('the named calculate_* tool') and directs agents not to reimplement published formulas, giving clear routing guidance relative to siblings.
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 are cleanly separated by content type and the list_/get_ pairs are predictable. The main ambiguity is among search, search_all, and search_articles: search claims to cover the 'whole corpus' while search_all actually expands to essays, labs, claims, and the Homeric Atlas, so an agent could select the narrower search and miss content.
Every tool follows the same snake_case verb_noun pattern: calculate_*, get_*, list_*, and search_*. Even the three search variants are predictable from their suffixes, so there are no mixed naming conventions.
30 tools is above the preferred MCP size and creates real selection burden for agents, even though the multi-surface knowledge scope explains the volume. The set is systematic rather than bloated, but 25+ tools is still too many for a typical server surface.
Each content surface has browse, fetch, and search coverage, and get_related plus get_overview provide cross-cutting navigation. The only meaningful gap is that the relationship between search and search_all is not fully disjoint, which can create a dead-end if the wrong search tool is chosen first.