NeuroRank
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.5/5 across 3 of 3 tools scored.
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.
Available Tools
3 toolsget_cohort_statsGet NeuroRank cohort statsAInspect
Returns NeuroRank's public, aggregate cognitive-combine statistics across all completed combine runs: total runs, estimated trials, game titles and countries represented, median run age, and test-retest reliability. Read-only, no authentication, aggregate (non-personal) data only.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully covers behavioral traits: read-only, no authentication, aggregate non-personal. No contradictions.
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, well-structured, front-loaded with purpose and key constraints. 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?
Despite no input or output schema, the description thoroughly explains what data is returned. Complexity is low, and description is complete for this 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?
No parameters (0). With schema coverage at 100% (vacuously true), baseline is 4. Description adds no extra parameter info, as none are needed.
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 public aggregate cognitive-combine statistics and lists specific data points (total runs, estimated trials, etc.). It distinguishes from sibling tools by focusing on aggregate, 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 explicitly notes the tool is read-only, requires no authentication, and returns aggregate non-personal data. This implies appropriate contexts, but does not directly contrast with sibling tools or provide when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_player_reportGet NeuroRank player reportAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, description discloses key behaviors: paid, requires auth with specific headers, subscription level, player opt-in, quota limits. Provides good transparency for a read-mutation tool.
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 waste. All info is relevant: paid marker, output components, input format, auth requirements, constraints. Front-loaded with 'PAID' to immediately set expectations.
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 single-parameter tool with no output schema, the description covers input format, output components, and constraints. Could mention error states or quota specifics, but overall complete enough for agent selection.
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?
Only parameter is fully documented in schema with pattern and description. Description reinforces the NR-XXXXXX format, adding clarity beyond schema. High schema coverage (100%) means baseline 3, but the verbal format example earns a 4.
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?
Description uses specific verb 'Returns' and resource 'full cognitive-combine report for one player', clearly distinguishing from sibling tools (cohort stats and team report) by focusing on individual player reports.
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?
Explicitly states paid nature, required subscription, player opt-in, and monthly quota, giving clear context for when to use. Does not explicitly mention alternatives or when-not-to-use, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_team_reportGet NeuroRank team reportAInspect
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'] |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description covers key behavioral traits: it discloses the paid nature, subscription requirement, data inclusion conditions (opted-in only), minimum user count, and cost (monthly pull). It does not describe error handling or safety, but the provided detail is substantial for a non-destructive read operation.
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 concise, with only necessary information presented in a logical order. It starts with the key label 'PAID (team tier)', then explains function, requirements, and constraints without redundancy.
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 the absence of an output schema, the description adequately specifies the return content (per-dimension averages, strengths/weaknesses, standouts, archetype mix, summary). It also covers prerequisites, making the tool self-explanatory for selecting and invoking.
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% with a clear description for `shareableIds` including pattern and min/max. The description adds modest value by framing the parameter as a 'roster' to analyse, but does not elaborate beyond the schema's own documentation.
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's purpose: analyzing a roster of NeuroRank players and returning a detailed composition report. It lists specific outputs (e.g., per-dimension averages, strengths/weaknesses, standouts) and distinguishes itself from siblings `get_cohort_stats` and `get_player_report` through context.
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 usage context: it's a paid feature requiring an API key with an active team subscription, and it specifies constraints (only opted-in players, minimum 2 scored players, costs one monthly pull). However, it does not explicitly differentiate when to use this tool vs siblings, though the names imply the scope.
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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