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Search OPDP Untitled Letters

fda_search_opdp_untitled_letters
Read-onlyIdempotent

Search official FDA OPDP untitled letters for pharmaceutical promotion and advertising issues. Filter by company, product, issue date, close-out availability, or keyword in the extracted untitled-letter text when available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (1-500)
offsetNoResult offset for pagination
date_toNoEnd date for issued_date range (YYYY-MM-DD)
keywordNoKeyword to search in the extracted untitled-letter text
date_fromNoStart date for issued_date range (YYYY-MM-DD)
company_nameNoCompany name (fuzzy match)
has_close_outNoWhether the record has a linked close-out letter
product_issueNoProduct or issue text from the OPDP table (partial match)

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds a useful caveat that keyword search works only 'when available' in extracted text, but it does not disclose response shape or further behavioral constraints. No contradiction with annotations.

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: the first states the core purpose, the second lists the facets available. It is front-loaded, concise, and every sentence contributes useful information without redundancy.

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?

With no output schema, the description covers the data source and all major filter dimensions, including a caveat about keyword text availability. It does not explain pagination or return fields, but the schema already documents limit/offset and the search intent is clear, making it reasonably complete for a read-only search tool.

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?

The input schema has 100% coverage with descriptions for all 8 parameters. The description restates the filter categories in natural language, but adds no semantic details beyond what the schema already provides, so it receives the baseline score.

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 uses a specific verb ('Search') and names a precise resource ('official FDA OPDP untitled letters') with a clear domain ('pharmaceutical promotion and advertising issues'). This clearly distinguishes it from sibling tools like fda_search_warning_letters.

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 provides clear context by enumerating filter dimensions (company, product, issue date, close-out availability, keyword), which implies when it is useful. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of full guidance.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes with clear boundaries, such as fda_search_drugs for drug applications and fda_search_510k for device clearances. However, some overlap exists, like fda_device_udi and fda_device_udi_lookup both querying UDI data, which could cause confusion despite differences in scope.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with a clear fda_ prefix, using descriptive verbs like search, get, list, and link. This uniformity makes the set predictable and easy to navigate, with no deviations in naming style.

Tool Count2/5

With 48 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to manage. While the domain is broad (FDA data), the toolset feels bloated with many specialized or overlapping tools that could be consolidated.

Completeness5/5

The toolset provides comprehensive coverage of FDA data domains, including drugs, devices, inspections, compliance, recalls, and facilities. It supports full CRUD-like operations (e.g., search, get, link, save) and lifecycle workflows, with no obvious gaps for the intended purpose.

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