Crawlora MCP

goodrx_condition

Read-only

Get a health condition's GoodRx overview by slug from goodrx_conditions; returns sections for definition, causes, symptoms, diagnosis, medications, treatments, and prevention, plus authors, dates, and related articles. Use for condition facts; use goodrx_condition_drugs for the medication list.

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": "goodrx_condition"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes	Condition slug from /goodrx/conditions

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 7, 2026
- addedInput schema / properties / slug / description
```
Added value: +"Condition slug from /goodrx/conditions"
```
First observedOct 5, 2026

TDQS

A4.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description enumerates the returned sections (definition, causes, symptoms, diagnosis, medications, treatments, prevention) plus authors/dates/related articles, but with an output schema present this is largely a restatement of the response shape rather than new behavioral context (no auth, rate-limit, or freshness notes).

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 sentences, front-loaded with the core action, and the alternative-tool routing is packed into the second sentence with zero filler.

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?

For a single-slug read tool with an output schema, the description covers purpose, the sibling to use instead, and where slugs come from, which is sufficient to invoke correctly. It stops short of adding anything an agent could not infer, but nothing needed is missing.

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?

Only one parameter and schema coverage is 100%, so the schema already documents 'slug' fully. The description adds marginal value by tying the slug's origin to the goodrx_conditions sibling rather than just the /goodrx/conditions path, but no format or validity guidance beyond that. Baseline 3 for near-total schema coverage.

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?

Specific verb ('Get') plus resource ('a health condition's GoodRx overview') and an explicit access path ('by slug from goodrx_conditions'). It also names the sibling it is not ('goodrx_condition_drugs'), so an agent can distinguish the two without opening either 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?

Explicitly states the selection rule: use this for condition facts, use goodrx_condition_drugs for the medication list. It also implicitly routes slug discovery to goodrx_conditions. The when/when-not split is stated rather than inferred.

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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