pharma_sponsor_diligenceBuild a pharmaceutical sponsor diligence view from ClinicalTrials.gov, Drugs@FDA, and PubMed. Returns pipeline composition, recent registry activity, FDA applications, and recent affiliation-linked literature for company research.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Registry-activity start date, YYYY-MM-DD (default 180 days ago). | |
| sponsor | Yes | Pharmaceutical company or trial sponsor. |
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?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds value by naming the integrated sources and specifying the returned components (pipeline composition, registry activity, FDA applications, literature). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff: first sentence states the action and sources, second lists outputs. Front-loaded and efficient.
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?
With no output schema, the description carries the burden of explaining return values and does so by listing four output categories. It also mentions the three data sources. While some terms like 'pipeline composition' are implicit, the description is adequate for a sponsor-research tool.
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 description coverage is 100%: both 'sponsor' and 'since' have clear descriptions, including the default for 'since'. The description does not add parameter details beyond the schema, so baseline 3 is appropriate.
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 clearly states the tool builds a 'pharmaceutical sponsor diligence view' from specified sources (ClinicalTrials.gov, Drugs@FDA, PubMed) and lists concrete output categories. This distinguishes it from sibling tools like pharma_drug_profile or pharma_indication_landscape by focusing on sponsor-level company research.
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 phrase 'for company research' provides clear context for when to use this tool. However, it does not explicitly mention alternatives or exclusions relative to sibling tools, so it falls short of full usage guidance.
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.
Every tool has a clearly distinct purpose, even within the same domain (e.g., ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta are differentiated by groundedness/beta status; polymarket_edges vs polymarket_arbitrage vs polymarket_fill_risk each target discovery vs arbitrage vs execution risk). The descriptions are highly detailed, eliminating ambiguity about when to use each.
All tool names use snake_case consistently, and most follow a verb-first pattern (ask_, compare_, discover_, search_, validate_), but a few are noun-first (entity_profile, polymarket_edges, recent_alerts). The style is readable and predictable, though not perfectly uniform in the verb_noun convention.
With 37 tools, the server is heavily over-scoped, especially given the 'Pharma Intel' name that suggests a focused pharma domain. Many tools are general-purpose (prediction markets, memory, subscription management, feedback) unrelated to the server's apparent purpose, making it feel like a grab bag rather than a cohesive set.
The pharma-specific tools cover drug profiles, safety, pipeline scans, catalysts, indication landscapes, and sponsor diligence – a solid lifecycle coverage. The broader data/query/prediction-market tools also feel complete for their respective sub-domains. The only minor gaps are niche operations (e.g., updating a subscription), but these are not critical to the core workflows.