gapup-mcp

clinical_evidence_briefer

Read-only

Brief évidence clinique (GRADE) — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Answers: Review the clinical evidence for <drug/intervention> in — GRADE rating, key trials, safety signals. · Scan safety signals for in — adverse events, severity, frequency from FAERS and trial data. · Assess comparative effectiveness of versus for — what does the evidence show? · Is there evidence supporting drug repurposing of for — existing trials and GRADE quality? · What are the evidence gaps for in before formulary adoption? Reference case: Semaglutide 2.4mg · Chronic weight management in non-diabetic adults · GRADE high efficacy · studies found · nausea/GI signals · FDA approved · PubMed+ClinicalTrials+OpenFDA. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`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.
`topic`	Yes
`max_studies`	Yes
`intervention`	No
`evidence_focus`	Yes		all
`target_disease`	No
`date_range_years`	Yes
`intervention_type`	No

TDQS

B3.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context: inputs are validated server-side, it returns a structured/audited deliverable, and async usage is explained. No contradictions; the extra detail on validation and async behavior is helpful beyond annotations.

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

Conciseness3/5

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

The description is front-loaded with the core purpose but becomes verbose with multiple example questions and a reference case. It could be more concise by focusing on the tool's role and key constraints.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema is provided, and the description does not detail the return value structure beyond 'structured, audited deliverable'. With 8 parameters and 4 required, the description omits critical info like output fields or how results are formatted.

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

Parameters2/5

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

Schema description coverage is low (13%). The description does not explain key parameters like topic, max_studies, evidence_focus, or intervention_type. It only vaguely mentions 'send the documented case fields'. With 8 parameters and minimal schema descriptions, the description fails to compensate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it produces a clinical evidence briefing with GRADE ratings, and gives specific example queries (e.g., 'Review the clinical evidence for <drug/intervention> in <indication>'). It distinguishes from siblings like clinical_pharma_intel by focusing on GRADE and structured deliverables. However, jargon ('Gapup agent-payable C-suite expertise') slightly muddies clarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage via example questions (e.g., safety scanning, comparative effectiveness) but does not explicitly state when to use this tool versus alternatives like sci_literature_search or clinical_pharma_intel. No when-not-to-use guidance is provided.

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.

Resources

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