get_cohort_statsReturns NeuroRank's public, aggregate cognitive-combine statistics across all completed combine runs: total runs, estimated trials, median run age, and measured reliability (split-half; test-retest sample still accruing). Read-only, no authentication, aggregate (non-personal) data only.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses read-only nature, no authentication requirement, and aggregate (non-personal) scope. It lacks mention of error handling or rate limits, but for a simple endpoint this is sufficient.
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?
Single sentence, front-loaded with the key purpose. Every word adds value, no redundancy. Highly efficient.
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?
Given no parameters and no output schema, the description fully covers what the tool returns. Sibling tool names provide additional context for distinguishing use cases.
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 input schema has zero parameters, so schema coverage is 100%. The description adds value by enumerating the returned statistics (total runs, estimated trials, etc.), effectively compensating for the lack of an output schema.
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 clearly states it returns 'public, aggregate cognitive-combine statistics' and lists the specific fields. It distinguishes itself from siblings (get_player_report, get_team_report) by emphasizing aggregate and non-personal data.
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 provides clear usage context: read-only, no authentication, aggregate data. It implicitly suggests use when needing summary stats, but doesn't explicitly state when not to use or provide alternatives beyond sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a clearly distinct scope: aggregate stats for all players, individual report for one player, and team report for a roster. No overlap in purpose or output.
All tools follow a consistent 'get_<noun>_<noun>' pattern (get_cohort_stats, get_player_report, get_team_report), with only the final word varying appropriately.
Three tools is slightly lean but reasonable for the focused domain of cognitive combine data retrieval. Each tool serves a distinct user need without excess.
Covers the main data access patterns (aggregate, individual, team). Missing a tool for listing or searching players, but the core reporting functionality is well-covered.