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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
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.
topicYes
max_studiesYes
interventionNo
evidence_focusYesall
target_diseaseNo
date_range_yearsYes
intervention_typeNo

TDQS

C2.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds 'Returns a structured, audited deliverable' and mentions server-side validation, but does not disclose additional behavioral traits like data sources (PubMed, ClinicalTrials, OpenFDA from example) or rate limits.

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

Conciseness2/5

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

The description is a long, unstructured paragraph mixing purpose, examples, and a reference case. It lacks clear front-loading of essential information and would benefit from bullet points or separate sections for purpose and examples.

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?

With 8 parameters and no output schema, the description should detail the return format and parameter mapping. It only vaguely states 'structured, audited deliverable' and gives example queries. Missing information on pagination, error handling, or how parameters like 'intervention' and 'target_disease' map to use cases.

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 coverage is only 13%, and the description does not explain individual parameters beyond the example queries. For instance, 'topic' is used in examples but not defined. The description adds marginal value but fails to compensate for the low schema coverage.

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 that the tool provides clinical evidence briefs with GRADE ratings, including example queries for efficacy, safety, comparative effectiveness, and evidence gaps. However, it does not explicitly differentiate from siblings like 'sci_literature_search' or 'clinical_pharma_intel', though the GRADE focus is distinctive.

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

Usage Guidelines2/5

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

The description provides example queries but no explicit guidance on when to use this tool versus alternatives. There is no mention of when not to use it or comparative advantages. The phrase 'Inputs are validated server-side' hints at usage constraints but is insufficient.

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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