Return the severity rubric for a dysfunction applied to observations. Returns the rubric for caller-side matching; structured matching against numeric thresholds is not implemented.
| Name | Required | Description | Default |
|---|---|---|---|
| observations | Yes | ||
| dysfunction_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesAdded value: +falseInput schema / properties / dysfunction_id / maxLengthAdded value: +256Input schema / properties / dysfunction_id / minLengthAdded value: +1Input schema / properties / dysfunction_id / patternAdded value: +"\\S"Input schema / properties / observations / items / maxLengthAdded value: +2000Input schema / properties / observations / items / minLengthAdded value: +1Input schema / properties / observations / items / patternAdded value: +"\\S"Input schema / properties / observations / maxItemsAdded value: +50Input schema / properties / observations / minItemsAdded value: +1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does this well by explicitly stating the tool returns a rubric instead of performing matching, and by flagging that structured numeric-threshold matching is not implemented — a key behavioral trait an agent needs to know. It doesn't describe the return format or error behavior, but the core contract is disclosed clearly.
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 zero filler. The purpose is front-loaded in the first sentence, and the second sentence delivers the behavioral contract. Every clause earns its place with no wasted wording.
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 relatively simple two-parameter query tool with no annotations and no output schema, the description covers the essential behavior (returns rubric, no matching). However, it never describes what the returned rubric looks like or the shape of the response, which is a notable gap given there is no output schema to fill it. Adequate but not complete.
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 0%, so the description must compensate for both parameters. The phrase 'a dysfunction applied to observations' establishes a light relationship between dysfunction_id and observations, but adds no detail about observation format, what dysfunction_id refers to, or how they interact. With zero schema coverage, this level of compensation is insufficient.
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 ('Return') and resource ('the severity rubric for a dysfunction applied to observations'), making the tool's function clear. The second sentence adds that it returns the rubric for caller-side matching rather than performing matching itself, which meaningfully separates it from siblings like differential_diagnosis. It stops short of naming the sibling it is not, so it doesn't fully earn a 5.
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 phrase 'for caller-side matching; structured matching against numeric thresholds is not implemented' implies when to use this tool — when the caller wants to do its own matching. However, it never names an alternative or gives an explicit when-not-to-use statement, and none of the siblings are referenced, so the guidance is implied rather than explicit.
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.
Each tool targets a distinct aspect of the domain: diagnosis, mapping confusions, retrieving entries, elicitation probes, listing axes/dysfunctions/compromised entries, ID resolution, statistics, severity, and interventions. No two tools overlap in purpose; descriptions clearly differentiate them.
Most tools follow verb_noun pattern (get_*, list_*, resolve_id, score_severity, suggest_intervention), but differential_diagnosis and review_stats deviate (noun-like phrases). The inconsistency is minor and doesn't obscure meaning.
11 tools is well within the ideal 3-15 range. Each tool serves a distinct purpose in the diagnostic workflow, and none feel redundant or missing for the stated domain.
The surface covers listing, retrieval, differential diagnosis, probes, severity, interventions, stats, and ID resolution. Minor gaps exist (e.g., no tool to add/update entries or directly compare two dysfunctions), but these are outside the apparent read-only research scope.