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Glama

Openfda

by io.github.pipeworx-io

Fda Complete Response Letters

fda_complete_response_letters
Read-onlyIdempotent

Search FDA-disclosed Complete Response Letters for drug and biologic applications. Defaults to letter_type "COMPLETE RESPONSE"; optionally include other letter types in the same dataset. Returns the application’s current approval_status, letter metadata, an excerpt, and the official document URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoPagination offset (default 0).
limitNoNumber of letters (1-100, default 20).
queryNoOptional full-text term or raw openFDA CRL search expression.
companyNoSponsor/company name.
to_dateNoOptional end date, YYYY-MM-DD.
from_dateNoOptional start date, YYYY-MM-DD.
application_numberNoNDA/BLA application number.
include_other_letter_typesNoInclude tentative approvals, rescissions, refusal-to-file letters, and other non-CRL records (default false).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by specifying the exact return content (approval_status, letter metadata, excerpt, document URL) and the default filtering behavior, which goes beyond what annotations provide.

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 a single, well-structured sentence that front-loads the action and resource, then lists key features. It contains no redundant words and efficiently conveys all necessary information.

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?

Despite having no output schema, the description clearly states what is returned (approval_status, metadata, excerpt, URL). It also covers the default filtering and the optional inclusion of other letter types. For a search tool with 8 parameters, the description provides sufficient context for an agent to understand when and how to use it.

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?

All 8 parameters have descriptions in the schema (100% coverage), so the schema already documents each parameter's meaning. The description does not add further semantic detail beyond the schema, such as syntax examples or format constraints, so it does not improve parameter understanding. 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 action (search) and resource (FDA Complete Response Letters), and distinguishes it from sibling tools like fda_warning_letters or fda_drug_approvals by specifying the document type and default filtering. The verb 'Search' combined with the resource name makes the purpose unambiguous.

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 explains the default behavior (only COMPLETE RESPONSE type) and the option to include other letter types, which guides usage. However, it does not explicitly state when to use this tool versus the sibling fda_crl_detail tool (likely for individual letter details), missing an opportunity to clarify the choice between search and detail retrieval.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Openfda toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have distinct names and purposes, but the large number of meta-tools (e.g., ask_pipeworx variants, deep_research) and overlapping research/scanning tools (entity_profile, compare_entities, recent_changes) could cause confusion. An agent may need to carefully read descriptions to choose correctly.

Naming Consistency3/5

Snake_case is prevalent but not universal. FDA tools are consistently named with 'fda_' prefix, but there are single-word verbs (remember, recall), camelCase is absent, and some tool names are long and descriptive (scan_competitor_ai_presence). The mix of patterns is readable but not highly consistent.

Tool Count3/5

43 tools is high and includes both dedicated tools and meta-tools that can access thousands more. There is redundancy (e.g., FDA data can be retrieved via fda_drug_approvals or ask_pipeworx). The scope is broad, but many tools could be consolidated. Count feels borderline excessive for the apparent purpose.

Completeness4/5

FDA coverage is excellent with tools for approvals, labels, events, recalls, shortages, warning letters, etc. Other domains (financial, betting, npm) are covered by meta-tools, providing breadth. However, dedicated non-FDA tools are sparse, and the server relies heavily on the universal query tools for completeness.

Resources