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mediora__lookup_marker

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoPreferred language for returned names. Defaults to 'en'.
queryYesFree-text marker name in EN/RU/HE; e.g. 'A1C', 'гликированный', 'TSH', 'ויטמין D'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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