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iwaokimura

zbmath-mcp

by iwaokimura

structured_search

Filter zbMath publications by author, title, MSC class, year range, or journal. Returns total count and matching documents.

Instructions

Search zbMath documents using structured field filters.

Use this instead of search_documents when you want to restrict results to specific fields such as author name, MSC subject class, or year range.

Args: author: Author (contributor) name, e.g. "Euler" or "Riemann, B.". title: Words or phrases to match in the document title. msc_code: MSC 2020 classification code, e.g. "11" for Number Theory or "35J15" for a specific code. year_from: Earliest publication year (inclusive). year_to: Latest publication year (inclusive). journal: Journal / bibliographic source name fragment. results_per_page: Number of results (1–100, default 10). page: Zero-based page index for pagination (default 0).

Returns: JSON string with total result count and matching documents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
titleNo
authorNo
journalNo
year_toNo
msc_codeNo
year_fromNo
results_per_pageNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses return format (JSON with count and documents) and parameter behavior, but does not explicitly state read-only nature or other side effects. However, search context implies safe operation.

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?

Concise and well-structured: one-line purpose, usage guidance, parameter list with examples, and return description. No wasted words.

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

Completeness5/5

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

Given 8 optional parameters and existence of output schema, the description covers all parameters, usage, and return. It distinguishes from siblings, making it complete for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

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 explains all 8 parameters with examples (e.g., author 'Euler', msc_code '11', defaults for results_per_page and page), adding significant meaning beyond the schema.

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 'Search zbMath documents using structured field filters', with a specific verb and resource. It distinguishes itself from the sibling 'search_documents' by noting it is for restricting results to specific fields.

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

Usage Guidelines5/5

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

Explicitly states 'Use this instead of search_documents when you want to restrict results to specific fields such as author name, MSC subject class, or year range.' This provides clear condition and alternative.

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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