fda_crl_detailRetrieve one FDA Complete Response Letter by application number and optionally letter date. Returns the FDA-disclosed letter text and document URL. Confidential commercial and trade-secret material may be redacted by FDA.
| Name | Required | Description | Default |
|---|---|---|---|
| letter_date | No | Optional exact letter date, YYYY-MM-DD, when an application has multiple CRLs. | |
| application_number | Yes | Required NDA/BLA application number. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent, non-destructive), the description discloses that it returns letter text and URL, and notes that confidential material may be redacted. This adds useful behavioral context without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each informative: purpose, output, and caveat. Front-loaded with the core action, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a retrieval tool with 2 parameters and no output schema, the description covers what is returned (text, URL) and potential redaction. No gaps given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are fully described in the schema. The description adds minimal extra meaning beyond stating the two parameters and the purpose of letter_date for multiple CRLs, but does not enhance parameter understanding significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Retrieve') and resource ('one FDA Complete Response Letter') with clear parameters (application number, optionally letter date). It distinguishes from siblings like fda_complete_response_letters (plural) and other FDA tools by focusing on a single letter retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates when to use this tool (when you need a specific letter by application number). However, it does not explicitly mention when not to use it or point to alternatives, such as fda_complete_response_letters for listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.