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kaysiz

DataCite Librarian MCP

by kaysiz

repository_health

Aggregate repository quality assurance metrics, including completeness and issue samples. Filter results by client ID or prefix to identify and resolve data quality problems in repositories.

Instructions

Aggregate repository QA: completeness, issues, samples. Scope by client_id or prefix.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prefixNo
client_idNo
max_recordsNo
max_sample_issuesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the aggregation behavior but does not mention whether it is read-only, potential performance costs, permissions, or what the output includes beyond the bare hints. This is minimal behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (19 words) and front-loaded with the key verb and resource. It is efficient with no filler, though brevity contributes to under-specification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values are defined, but the description remains incomplete for a 4-parameter aggregation tool. It does not cover the meaning of max_records, the scope of aggregation (whole repo vs client), or any operational caveats. The description is too sparse to answer basic usage questions.

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 description coverage is 0%, so the description must compensate. It does add meaning to client_id and prefix as scope selectors, and 'samples' loosely maps to max_sample_issues. However, max_records is not explained, and max_sample_issues default is not mentioned. Partial compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Aggregate') and a resource ('repository QA'), with sub-aspects 'completeness, issues, samples'. This is clear and distinguishes it somewhat from siblings like export_health_issues (export vs aggregate) and coverage_report, but it does not explicitly name an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Only gives a scoping note ('Scope by client_id or prefix'), which is a parameter usage hint, not guidance on when to choose this tool over alternatives. No exclusions or comparisons to sibling tools are provided.

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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