Fuzzy-search markers by alias across EN/RU/HE. Useful when the upstream LLM has the lab's local name (e.g. "гликированный гемоглобин", "המוגלובין מסוכרר", "A1C") and needs to find the canonical Mediora slug.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Preferred language for returned names. Defaults to 'en'. | |
| query | Yes | Free-text marker name in EN/RU/HE; e.g. 'A1C', 'гликированный', 'TSH', 'ויטמין D'. |
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?
No annotations are provided, so the description carries the full burden. It mentions 'fuzzy-search' and cross-language matching, which adds behavioral context beyond the schema. However, it does not disclose details like whether multiple results can be returned, pagination, or result scoring. A score of 3 reflects that it provides some but not comprehensive behavioral insight.
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?
The description is two sentences with no extraneous information. It front-loads the operation ('Fuzzy-search markers by alias across EN/RU/HE') and provides a practical usage example. 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?
Given the simplicity of the tool (2 parameters, no output schema), the description adequately covers the overall purpose and usage context. It could be improved by briefly mentioning the output format (e.g., 'returns the canonical slug'), but it is sufficiently complete for an agent to understand the tool's role.
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%, both parameters (query, lang) are described with examples and enum values. The description adds context about fuzzy-search and canonical slugs, but does not significantly enhance the parameter meaning beyond the schema. Baseline score of 3 is appropriate.
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 clearly states the tool performs fuzzy-search by alias across three languages and returns the canonical Mediora slug. It uses a specific verb ('fuzzy-search') and resource ('markers by alias'), and distinguishes it from siblings like list_markers or explain_marker.
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 says 'Useful when the upstream LLM has the lab's local name... and needs to find the canonical Mediora slug.' This gives clear guidance on when to use the tool. However, it does not mention when not to use it or provide explicit alternatives, which would improve clarity.
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 resource or action: catalog list/explain tools are divided by entity type (condition, marker, panel, symptom), patient data tools separate history, details, and trend analysis, and analyze_lab_pdf/whoami have unique roles. No two tools could reasonably be confused.
The set overwhelmingly follows a verb_noun pattern (list_*, explain_*, get_*, analyze_lab_pdf, lookup_marker, whoami). The only deviation is 'longitudinal_trend', which is a noun phrase rather than an action verb; still clearly readable.
At 14 tools, the set is well-scoped for a domain that spans catalog browsing, patient data retrieval, and lab report analysis. Each tool serves a distinct purpose and none feel redundant.
The lifecycle is complete: authenticate (whoami), ingest a lab PDF (analyze_lab_pdf), retrieve patient history (get_patient_history), drill into details (get_test_details), and analyze longitudinal patterns (longitudinal_trend). The catalog is fully browsable with list_* and explain_* tools, and lookup_marker bridges aliases. No obvious missing operations.
