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evipedia-mcp

evipedia-mcp

by forever-healthy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

search_reviews

Locate evidence reviews on health and longevity interventions by name, synonym, keyword, or category. Returns ranked matches with topic and URL to confirm if a review exists.

Instructions

Search evipedia.ai evidence reviews by name, synonym, keyword, or category. A discovery tool: returns the matching reviews (goal-qualified topic + URL) ranked by relevance so you can tell whether a review exists. To read one, pass its slug to get_conclusion (quick bottom line) or get_review (full Markdown).

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": {
      "query": "<query>"
    },
    "name": "search_reviews"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesSearch query — intervention name, synonym, drug class, or category

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.26

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries full responsibility. It discloses that results are ranked by relevance, include goal-qualified topic and URL, and that the tool only tells whether a review exists. This is meaningful behavioral context beyond a simple 'search' statement.

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?

The description is compact and front-loaded with the verb and resource. Three sentences each add distinct value: what it searches, what it returns, and how to proceed after finding a match. There is no redundant or filler content.

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?

Given a simple one-parameter tool with no output schema or annotations, the description is sufficiently complete. It covers purpose, result format, and follow-up actions, leaving little ambiguity about expected behavior. Minor omissions like pagination or result limits are not critical for this discovery tool.

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

Parameters3/5

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

The schema already provides 100% coverage for the single 'query' parameter with a clear description. The tool description adds context about search dimensions (name, synonym, keyword, category) and return behavior, but does not fundamentally alter or deepen the parameter meaning beyond what the schema states.

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 searches evidence reviews by name, synonym, keyword, or category. It is explicitly described as a discovery tool that returns matching reviews with topic and URL, distinguishing it from read tools like get_conclusion and get_review.

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 tells the agent when to use the tool (to check if a review exists) and provides explicit next steps by mentioning get_conclusion for quick bottom line and get_review for full Markdown. It does not explicitly contrast with list_reviews, but the search-oriented behavior is clear.

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

Deploy Server

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