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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
modeNoAnalysis mode. Default "trials". trials=clinical trials, pipeline=sponsor overview, approvals=FDA approvals, recalls=enforcement, adverse_events=FAERS
asyncNoIf 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.
phaseNoFilter trials by phase (1/2/3/4/NA). Only applies to modes trials and pipeline.
queryYesDrug name, indication, sponsor or molecule (e.g. "atezolizumab", "metastatic NSCLC", "Roche", "semaglutide")
countryNoISO 2-letter country code to filter trial sites (e.g. US, FR, DE).
max_resultsNoMaximum number of results to return. Default 20.
status_filterNoFilter trials by status. Only applies to modes trials and pipeline.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
queryYes
statusYes
trialsNo
recallsNo
signalsYes
sourcesYes
pipelineNo
approvalsNo
quality_scoreYes
adverse_eventsNo

TDQS

A4.6/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: signal detection (P0/P1/P2), data sources (ClinicalTrials.gov, EU CTR, OpenFDA, FAERS), optional API key quota increase, SLA ≤16s p95, and cache durations. No contradiction with annotations (readOnlyHint, destructiveHint, openWorldHint).

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 bullet points and section headings, front-loading the purpose. However, it is fairly long (multiple sections). Every sentence is informative, but it could be slightly more concise without losing clarity.

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 (5 modes, 7 params, output schema exists), the description is highly complete. It covers mode details, signal detection algorithm, data sources, performance constraints, and caching policy. Output schema exists, so return values are sufficiently documented.

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?

Input schema has 100% coverage with detailed descriptions for all 7 parameters. The description enriches parameter semantics by explaining mode-specific behavior (e.g., pipeline includes trial count by phase, signal detection thresholds). It adds value beyond the 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's purpose: 'Clinical and pharmaceutical intelligence for biotech analysts...' and lists five specific modes (trials, pipeline, approvals, recalls, adverse_events) with detailed explanations. It distinguishes itself from sibling tools by focusing on pharma intelligence with live data aggregation and signal detection.

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 provides clear guidance on when to use the tool by detailing each mode's purpose and the signal detection logic. It does not explicitly exclude cases or mention alternatives among siblings, but the mode descriptions implicitly guide selection.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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