gapup-mcp

clinical_pharma_intel

Read-only

Clinical and pharmaceutical intelligence for biotech analysts, healthcare fund managers, pharma BD teams, catalyst-driven hedge funds and health journalists. Aggregates live data across five modes: • trials — active/completed clinical trials (ClinicalTrials.gov v2 + EU CTR in parallel, 450k+ records) • pipeline — full pipeline by sponsor: trial count by phase + top indications • approvals — FDA drug label approvals + mechanism of action (OpenFDA) • recalls — FDA enforcement recalls classified by severity (Class I/II/III) • adverse_events — FAERS aggregated reactions: top 10 reactions + serious%

Signal detection (P0/P1/P2): P0 if Class I recall OR trial terminated for safety reason P1 if serious adverse events >30% OR ≥3 recalls in 12 months P2 otherwise (standard monitoring)

All sources are public and keyless. Optional env OPENFDA_API_KEY raises daily quota from 1,000 to 120,000 requests. SLA: ≤16s p95 (parallel fetch, 8s budget per source). Cache: 6h trials, 24h approvals, 12h recalls, 6h adverse events.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	No	Analysis mode. Default "trials". trials=clinical trials, pipeline=sponsor overview, approvals=FDA approvals, recalls=enforcement, adverse_events=FAERS
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`phase`	No	Filter trials by phase (1/2/3/4/NA). Only applies to modes trials and pipeline.
`query`	Yes	Drug name, indication, sponsor or molecule (e.g. "atezolizumab", "metastatic NSCLC", "Roche", "semaglutide")
`country`	No	ISO 2-letter country code to filter trial sites (e.g. US, FR, DE).
`max_results`	No	Maximum number of results to return. Default 20.
`status_filter`	No	Filter trials by status. Only applies to modes trials and pipeline.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`query`	Yes
`status`	Yes
`trials`	No
`recalls`	No
`signals`	Yes
`sources`	Yes
`pipeline`	No
`approvals`	No
`quality_score`	Yes
`adverse_events`	No

TDQS

A4.4/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description adds significant behavioral context: public keyless sources, optional API key for quota, SLA (≤16s p95), cache durations per mode, and signal detection schema (P0/P1/P2). This fully discloses operational traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for modes, signal detection, and technical details. It front-loads the purpose and target users. While somewhat lengthy, every sentence adds value and the structure aids readability.

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?

Given the tool's complexity (7 params, 5 modes, output schema), the description covers all essential aspects: mode functions, signal detection, source info, caching, SLA, and optional API key. It leaves no significant gaps for an AI agent to correctly invoke the 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?

Schema coverage is 100%, so baseline is 3. The description repeats some parameter info (e.g., mode options, default) but adds context like 'trials' default mode and signal detection. No major additional semantics beyond schema.

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 tool provides clinical and pharmaceutical intelligence, listing five distinct modes (trials, pipeline, approvals, recalls, adverse_events) with specific sources and purposes. This differentiates it from other tools in the sibling list, which cover unrelated domains.

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 implicitly signals usage through target audience (biotech analysts, healthcare fund managers, etc.) and mode definitions. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion cases, leaving some gap.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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