NeuroRank exposes its esports cognitive-combine data over MCP. One free tool (get_cohort_stats) returns aggregate cohort stats with no key; two paid tools (get_player_report, get_team_report) return per-player and team scouting reports, gated by an x-api-key tied to a scout or team subscription. Streamable HTTP, stateless. Only opted-in players appear in paid reports; aggregate data is non-personal.
Scored across 3 tools
Each tool targets a clearly distinct scope: public aggregate cohort statistics, individual player reports, and team composition reports. There is no meaningful overlap or ambiguity between them.
All tool names follow the same get_<target>_<resource> pattern: get_cohort_stats, get_player_report, get_team_report. The naming style is uniform and predictable, with no mixed conventions.
Three tools is a well-scoped size for this server's purpose: one public aggregate endpoint, one individual report endpoint, and one team report endpoint. Each tool clearly earns its place with no redundancy.
The set covers the main report surfaces for NeuroRank: aggregate, individual, and team levels. The only notable gap is the lack of a player search or roster discovery tool, so callers must already know profile IDs, but this can be worked around with externally supplied IDs.
Returns 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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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.
PAID. Returns a full cognitive-combine report for one player by NeuroRank profile id (NR-XXXXXX): six dimension scores, archetype, the written report, and coaching notes. Requires an API key (header x-api-key) belonging to a user with an active scout or team subscription, and is limited to players who have opted in to scouting. Subject to a monthly quota.
| Name | Required | Description | Default |
|---|---|---|---|
| shareableId | Yes | NeuroRank profile id, e.g. NR-AB12CD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full transparency burden. It discloses that the tool is paid, requires specific authentication and player opt-in, and is subject to a quota. This provides sufficient behavioral context for safe invocation.
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?
The description is a single sentence with a clear colon-separated list of report contents, followed by a second sentence covering requirements. It is front-loaded with 'PAID' and contains no unnecessary words.
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 one parameter, no output schema, and no annotations, the description comprehensively explains the tool's purpose, return contents, and all usage constraints (payment, subscription, opt-in, quota). No gaps remain.
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 coverage is 100% (the shareableId parameter is fully described with pattern and example). The description adds the example format 'NR-AB12CD' but does not add significant meaning beyond what the schema already provides.
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 the tool returns a full cognitive-combine report for a single player by NeuroRank profile ID, listing specific contents (dimension scores, archetype, written report, coaching notes). This is distinct from siblings get_cohort_stats and get_team_report, which focus on aggregated or team-level 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 explicitly marks the tool as PAID, requires an API key with a specific subscription (scout or team), limits to players who opted in, and mentions a monthly quota. It does not explicitly state when to use alternatives, but the sibling tools' purposes are implied by their names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
PAID (team tier). Analyses a roster of NeuroRank players and returns a composition report: per-dimension team averages, strengths and weaknesses, per-dimension standouts, score spread, archetype mix, and a written team intelligence summary. Requires an API key (header x-api-key) on an active team subscription. Only players who have opted in to scouting are included; at least 2 opted-in, scored players are required. Costs one monthly report pull.
| Name | Required | Description | Default |
|---|---|---|---|
| shareableIds | Yes | 1–10 NeuroRank profile ids to analyse as a roster, e.g. ['NR-AB12CD','NR-EF34GH'] |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description fully carries the burden. It discloses the subscription cost, API key requirement, opt-in constraint, and necessary minimum player count, leaving no hidden behavioral surprises.
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?
Three dense, front-loaded sentences with zero waste. Every sentence conveys essential information: purpose, prerequisites, and constraints.
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?
The description fully covers the tool's purpose, input requirements, output content, and usage context. Given the single parameter and no output schema, no additional information is needed.
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 coverage is 100%, so baseline is 3. The description adds value by explaining the ID format (e.g., 'NR-AB12CD'), range of 1–10 items, and the 'shareableIds' purpose, which goes beyond the 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 a NeuroRank composition report with specific components (averages, strengths, weaknesses, etc.). It distinguishes from sibling tools like get_player_report (individual) and get_cohort_stats (cohort) by focusing on a team roster.
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 specifies it is a paid feature requiring an API key and active subscription, and it outlines player opt-in and minimum count conditions. It does not explicitly contrast with siblings, but usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_cohort_statsget_player_reportget_team_report
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