Retrieve positive and negative Steam review counts over time to analyze sentiment trends, with weekly, monthly, and recent daily buckets for any app ID.
Get the review up/down histogram for a Steam app. Returns the positive/negative recommendation counts over time (the store review graph): weekly/monthly rollups plus recent daily buckets.
| Name | Required | Description | Default |
|---|---|---|---|
| appid | Yes | Numeric Steam app id | |
| language | No | Steam language name or 'all' |
Changes observed during successful MCP inspections.
v1.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It usefully discloses output granularity (weekly/monthly rollups plus recent daily buckets), which is real behavioral context, but says nothing about authentication, rate limits, pagination, or whether counts are cumulative or windowed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences that are front-loaded and mostly waste-free, with the resource stated first and the return shape second. The parenthetical 'the store review graph' is slightly redundant but serves as useful disambiguation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema and no annotations, so the description must convey the contract, and it only partially does: it sketches the rollup buckets but omits windowing, pagination, and history range. Adequate to call the tool, but not complete enough for a tool with no structured safety or return metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so appid and language are already documented in the schema. The description adds no additional meaning about either parameter, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Get the review up/down histogram for a Steam app') and clarifies the shape of the output (positive/negative counts over time, the store review graph). It is distinguishable from the sibling steam_reviews, but it never names a sibling to sharpen the contrast, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this versus steam_reviews or steam_reviews_search, and no exclusions or prerequisites are stated. The trend-oriented output implies a use case but the agent must infer it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.