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get_conclusion

Retrieve the plain-text, evidence-based bottom line of a health or longevity review by slug—ideal for quick 'does X work?' answers without full methodology or references.

Instructions

Get just the plain-text conclusion of an evidence review — the evidence-based bottom line, without the methodology, findings, or references. Prefer this for 'does X work?' questions; use get_review only when the user needs the full evidence, or get_metadata for 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}".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesReview slug, e.g. 'rapamycin' (a full evipedia.ai URL is also accepted)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.26

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden, and it does most of it: read-only, no authentication required, and the exact error surfaced for an unknown slug ('Review not found: {slug}'). It does not mention rate limits or any caching/response envelope detail, but for a single-resource read the disclosure is well above adequate.

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?

Three sentences, each earning its place: scope, routing, then parameter/auth/error mechanics. The most decision-relevant information (what you get, which sibling to prefer) is front-loaded.

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?

There is no output schema, so the description must describe the return, and it does ('plain-text conclusion... evidence-based bottom line, without methodology, findings, or references'). Combined with the slug source guidance and the documented error case, nothing an agent needs to call this correctly is missing.

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?

Schema coverage is 100% and the schema already documents the slug format and URL acceptance, so the baseline is 3. The description adds value beyond the schema by specifying the provenance of valid slugs (search_reviews, list_reviews, list_updates) and restating URL normalisation and the failure mode for an unknown slug.

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 just the plain-text conclusion of an evidence review') and immediately bounds the scope by naming what is excluded: methodology, findings, references. It distinguishes itself from get_review and get_metadata within the same sentence, so an agent can route without opening any schema.

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 positive trigger ('does X work?' questions) and explicit alternatives with their conditions ('use get_review only when the user needs the full evidence, or get_metadata for dates and citations'). This is a complete when-to-use / when-not-to-use map against named siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.