Pharma Intel

Pharma Pipeline Catalysts

pharma_pipeline_catalysts

Read-onlyIdempotent

Build a sponsor catalyst-monitoring view from ClinicalTrials.gov and Drugs@FDA. Returns upcoming Phase 2/3 primary-completion dates, recently completed trials for results/disclosure follow-up, and sponsor-linked FDA applications for event-driven biotech research. Trials are those the named company LEADS as registered sponsor; a study another organisation runs using its drug belongs to that organisation and is not returned here.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sponsor`	Yes	Pharmaceutical company or sponsor name (e.g., "Pfizer", "Moderna", "Vertex Pharmaceuticals")
`months_ahead`	No	Catalyst-calendar horizon in months. Default 12, max 24.

Schema Changelog

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

Changed1 schema field changedMay 26, 2026

addedInput schema / examples

Added value: +[
+  {
+    "sponsor": "Pfizer"
+  },
+  {
+    "months_ahead": 6,
+    "sponsor": "Vertex Pharmaceuticals"
+  }
+]

AddedMay 26, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare this read-only, idempotent, and non-destructive, so the description's added value is the lead-sponsor behavior and the source-scoped collection logic. It adds context about what is included/excluded beyond the annotations without contradicting them.

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?

Three sentences with no filler: the first establishes purpose and sources, the second enumerates returns, and the third states the critical scope caveat. Every sentence carries essential information and the key distinction is front-loaded.

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?

Even with no output schema, the description enumerates the three return categories in sufficient detail for an agent to judge fitness. It does not describe result shaping or pagination, but for a read-only catalyst-monitoring tool the core expectations are covered.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so a 3 is the baseline. The description goes beyond the schema by clarifying that 'sponsor' means the named company as registered lead sponsor, not any organization using the product, and it contextualizes 'months_ahead' through upcoming and recently completed time horizons.

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 opens with a specific action ('Build a sponsor catalyst-monitoring view') and names its data sources, then itemizes the returned categories (upcoming Phase 2/3 primary-completion dates, recently completed trials, sponsor-linked FDA applications). The final sentence sets a precise boundary (company LEADS as registered sponsor) that distinguishes it from broader pipeline tools like pharma_pipeline_scan.

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?

It clearly frames the intended use case ('event-driven biotech research') and gives an explicit exclusion: trials run by another organization using the drug are not returned. It does not name a specific alternative tool, so it falls just short of full 5 guidance, but the context is not left to inference.

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

All Pharma Intel tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0

Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources

Need Help?