evipedia-mcp

get_review

Fetch the full evidence review as raw Markdown for a given health and longevity intervention slug. It covers methodology, findings, safety, dosing, and references.

Instructions

Get the full evidence review as raw Markdown — methodology, findings, safety, dosing, and references. Reviews are long (often 10k+ tokens); use get_conclusion when the bottom line is enough, or get_metadata for machine-readable dates and citations. Takes a slug from search_reviews, list_reviews, or list_updates; a full https://evipedia.ai/{slug} URL is also accepted and normalised to the slug. Read-only, no authentication required; an unknown slug raises "Review not found: {slug}".

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "get_review"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes	Review slug, e.g. 'rapamycin' (a full evipedia.ai URL is also accepted)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.26Jul 31, 2026

TDQS

A4.9/5.0

Behavior5/5

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

No annotations exist, so the description carries the full burden and discharges it: discloses output format (raw Markdown), size risk (often 10k+ tokens), auth profile (read-only, no auth), and the exact error string for an unknown slug. These are the traits an agent needs to plan around token cost and failure handling.

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?

Two tight sentences plus an error clause; content is front-loaded with the resource and return shape before routing advice. No filler or restatement of the tool name.

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?

Complete for a no-auth read tool: call source, format, size, alternatives, and error behavior are all covered. Minor gap: no output schema exists and pagination/truncation behavior for 10k+ token payloads is not addressed.

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

Parameters5/5

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

Schema covers the single parameter at 100%, but the description adds real value beyond it: URL acceptance and normalisation to slug ('a full https://evipedia.ai/{slug} URL is also accepted and normalised to the slug') and the provenance of valid slugs. That is semantics the schema only gestures at.

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?

States a specific verb and resource ('Get the full evidence review') and enumerates the returned sections (methodology, findings, safety, dosing, references). Explicitly distinguished from siblings get_conclusion (bottom line) and get_metadata (machine-readable fields), so an agent can route without opening other schemas.

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

Usage Guidelines5/5

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

Gives an explicit when-to-use signal ('when the bottom line is enough' selects get_conclusion) and names get_metadata for structured dates/citations. Also routes input source: slug comes from search_reviews, list_reviews, or list_updates.

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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