Retrieve one source by its slug, with everything recorded about it. Slugs come back from search_sources and are permanent — the same slug names the same source across releases, so it is what to cite.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Permanent identifier, e.g. "sensor-tower". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden and it delivers: 'Retrieve' signals a safe read operation, 'with everything recorded about it' discloses the return scope, and the slug-permanence guarantee ('same slug names the same source across releases') is a genuine behavioral trait useful for citation workflows. It doesn't cover not-found error behavior, but for a one-parameter read tool that is a minor gap.
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 with zero filler. The core purpose is front-loaded in the first sentence, and the second sentence adds the permanence/citation context that justifies why the slug parameter is trusted. Every clause earns its place.
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?
For a simple one-parameter tool with 100% schema coverage and no output schema, this is nearly complete: purpose, parameter origin, return scope, and a stability guarantee are all present. The only omission is what happens on a miss (not-found behavior), which is minor for a fetch-by-identifier tool.
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?
The schema already documents slug at 100% coverage as a 'Permanent identifier', so the baseline is 3, but the description adds value beyond the schema: it reveals that slugs originate from search_sources and reinforces their permanence across releases — meaning the caller can safely persist and cite them. This is meaningful semantic enrichment rather than mere restatement.
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?
The description uses a specific verb ('Retrieve'), names the exact resource ('one source by its slug'), and defines the scope ('with everything recorded about it'). It clearly differentiates from the search_sources sibling by being the single-item fetch keyed on a slug, so an agent can tell them apart without opening the schema.
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?
The description establishes the workflow by stating that slugs come back from search_sources, implicitly telling the agent to search first, then fetch by slug. It doesn't explicitly name alternatives or exclusions, and none of the sibling tools are similar single-fetch operations, but the guidance is clear enough that the usage context is well-implied rather than fully spelled out.
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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Each tool targets a distinct resource or action: landscape maps, market profiles, source retrieval, trend listings, source search, and technique search. There is no overlap in purpose, and the descriptions clearly differentiate their roles.
All tool names follow a consistent verb_noun pattern using snake_case (get_, list_, search_). This predictable structure makes the API easy to navigate and reduces cognitive load.
With 6 tools, the surface is well-scoped for a starter pack. Each tool covers a distinct aspect of games research, and the count is within the ideal 3-15 range, neither too thin nor overwhelming.
The tool set covers the core research workflow: understanding landscape, retrieving market profiles, citing sources, exploring trends, and finding sources/techniques. The only minor gap is the absence of a dedicated list of all available markets (get_market requires a code), though search_sources partly fills this, making it workable.
