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skheman2026-sketch

@coinrebate/mcp-server

Server Quality Checklist

75%
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  • Latest release: v2.2.1

  • Disambiguation5/5

    Each tool targets a distinct function: fees, comparison, referrals, news, cost calculation, and compliance filtering. There is no meaningful overlap; even compare_fees and get_exchange_fees are differentiated through the specific purpose and ranking output.

    Naming Consistency4/5

    All tools follow a clear verb_noun pattern (get_exchange_fees, compare_fees, get_best_referral, get_latest_news, calculate_trading_cost, get_compliant_exchanges). Slight inconsistency: compare_fees and calculate_trading_cost lack the 'get' prefix, but the pattern is otherwise uniform and predictable.

    Tool Count5/5

    With 6 tools, the server is well-scoped for a fee rebate informational service. Each tool serves a distinct user intent without redundancy, and the count is appropriately small for the domain.

    Completeness3/5

    The server covers fee lookup, comparison, referrals, cost calculation, compliance, and news. However, missing features like retrieving specific exchange details, historical fee trends, or referral program terms could be considered useful gaps. Still, the core promotional workflow is complete.

  • Average 4/5 across 6 of 6 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds 'real-time' indicating live data, but doesn't disclose latency, rate limits, or external dependencies beyond what openWorldHint implies. No contradiction found.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is two sentences, front-loaded with the verb and resource, and includes specific exchange names and fee types. No fluff or unnecessary detail.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a zero-parameter read-only tool, the description adequately conveys the return content (fee types and discount). It doesn't specify the exact data format, but given no output schema, this is a minor gap. Overall, near-complete for its simplicity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters, so schema coverage is effectively 100%. The baseline for 0 parameters is 4, and the description doesn't need to explain parameters. No additional semantics are required.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: retrieving real-time trading fees from major crypto exchanges, listing specific exchanges and fee types (spot/futures, maker/taker, discount). It distinguishes itself from sibling tools by its specific resource and scope, though it doesn't explicitly name alternatives.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus siblings like compare_fees or get_best_referral. It simply states what it does without any decision context or exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds context about the calculation nature (shows savings vs standard fees) without contradicting annotations. It provides a useful behavioral hint beyond the structured data, though it doesn't mention potential input validation or error cases.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is two sentences with no unnecessary words. The first sentence states the core purpose, and the second clarifies the output. Everything earns its place, and the key information is front-loaded.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a relatively simple calculation tool with no output schema, the description adequately hints at the output (savings vs standard fees) and implies the necessary inputs (exchange, volume, type) through the schema. It does not discuss edge cases or response format, but the schema and annotations fill most gaps. It is nearly complete for an agent to call correctly.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so every parameter (type, volume, country, exchange) is already described in the schema. The description only reinforces 'trade volume' which is already in the schema, adding no new meaning or clarifying units, formats, or constraints beyond what exists. This meets the baseline for high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'calculate' and the specific resource (trading cost and savings with CoinRebate referral codes). It also explains the output (savings vs standard fees) and distinguishes itself from siblings like get_exchange_fees by focusing on actual cost with referral codes. The wording is specific and unambiguous.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage—when you need to know savings from referral codes for a given volume—but it does not explicitly compare with alternatives such as get_exchange_fees, compare_fees, or get_best_referral. No exclusion guidance is provided, so an agent might not know when to choose this over siblings.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already carry the safety profile (readOnlyHint, idempotentHint, destructiveHint), so the bar for credit is behavioral context beyond them. The description adds meaningful, non-obvious behavior: results are 'ranked by lowest fee after CoinRebate rebate discount'—an agent would otherwise assume raw exchange fees—and passing a country code switches filtering behavior. This genuinely shapes expectations about the returned data.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Three tight sentences with zero fluff: the core purpose, the ranking behavior, then the optional country filter. Each sentence earns its place and no word is wasted.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a two-parameter, single-required-param read-only tool with strong annotations and no output schema, the essential decision-making context is all here: comparison scope, ranking criterion, rebate-adjusted fees, and compliance filtering. Minor gaps—such as whether the result is a fully ranked list or top-N, and what the filtered list contains—are low severity for an agent deciding to call this tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema already describes both parameters fully (purpose enum, ISO country code, and 'for compliance filtering'), so the schema does the heavy lifting and the baseline is 3. The description restates the purpose constraint and the compliance use of country, adding only slight interpretive value by tying the fee computation to the rebate discount context.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description opens with a specific verb and resource: 'Compare trading fees across all exchanges for a specific purpose (spot or futures trading).' The scope qualifier 'across all exchanges,' the ranking claim, and the compliance-filter note distinguish it from siblings like get_exchange_fees and get_compliant_exchanges without needing to open the schema.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies the use case—fee comparison across exchanges for spot or futures—and tells the agent when to pass the country code for compliance-filtered results. However, it never names an alternative or states when not to use it, so the agent must infer the boundary against siblings like get_exchange_fees and get_compliant_exchanges on its own.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds that it returns titles, summaries, and links, but does not disclose any additional behaviors (e.g., recency guarantees, rate limits). With annotations in place, this is adequate but not exceptional.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    A single sentence that is front-loaded with the core action and resource, and immediately specifies what is returned. No wasted words, and it reads naturally.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple read-only tool with one optional parameter and no output schema, the description covers the essential return structure and source. It doesn't mention pagination or sorting, but those are not critical for this tool's typical use. Slightly more detail on response format could push it to 5, but 4 is fair.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema provides a full description of the `limit` parameter (range and default), so schema coverage is 100%. The tool description adds no further meaning about the parameter, so the baseline 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Get'), the resource ('latest crypto news and market insights published on CoinRebate'), and the output ('articles with titles, summaries, and links'). It is distinct from all sibling tools, which focus on fees, referrals, and compliance—not news.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The purpose is so specific that an agent would naturally use this for news retrieval. However, there is no explicit 'when not to use' or mention of alternatives. Given the obvious distinction from siblings, a 4 is appropriate.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare the operation read-only and idempotent, and the description adds useful behavioral details beyond those annotations: what is returned (referral code, discount percentage, signup URL) and the compliance role of country. No contradiction exists.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is three short sentences, front-loaded with the tool's main purpose, and every sentence adds useful information: action, return value, and compliance guidance. There is no filler or repetition.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For 2 parameters and no output schema, the description covers what the tool does, what it returns, and how an optional input affects behavior. It could be slightly more explicit about edge cases like unsupported exchanges, but it is sufficiently complete for an agent to invoke it confidently.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so both parameters are already well documented. The description adds some context like 'maximum fee discount' and the compliance purpose of country, but it does not heavily extend what the schema already explains.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description names a specific verb and resource: get the best referral/signup link for an exchange. It also gives the selection criterion (maximum fee discount), which distinguishes it from sibling tools like get_exchange_fees or compare_fees.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    It clearly conveys when to use the tool: when a referral/signup link for a specific exchange is needed. It even provides contextual guidance for the optional country parameter (compliance verification). It does not explicitly mention alternatives or when-not-to-use cases, but enough context is present.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds value by specifying the 'compliance matrix' filter, which is not inferable from the schema or annotations. It does not describe return format or pagination, but given the annotations, this is not a significant shortfall.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences with zero wasted words. The core functionality is front-loaded in the first sentence, and the second sentence reinforces the purpose ('Essential for regulatory compliance'). This is appropriately concise and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with only two parameters (one optional), rich annotations, and no output schema, the description is complete. It tells the agent what the tool does, the key filter, and why it matters. Nothing essential for calling it correctly is missing. The agent can infer that the return is a list of exchanges without explicit statement.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, with both 'country' (ISO code example) and 'purpose' (enum values) clearly documented. The description adds no additional parameter-specific meaning beyond the schema, so the baseline score of 3 is appropriate. The compliance matrix mention is not tied to a specific parameter in a way that adds new semantic detail.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Get exchanges'), the resource ('exchanges available in a specific country'), and the key filter ('CoinRebate compliance matrix'). This distinguishes it from sibling tools like get_exchange_fees and get_best_referral, which target different concerns. The note about regulatory compliance adds purpose and makes the tool's intent unmistakable.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage context: use when you need compliance-approved exchanges for a country. While it does not explicitly name alternatives or exclusions, the clear focus on compliance and country-based filtering, combined with distinctly named siblings, gives an agent sufficient guidance to know when this tool is appropriate. The lack of explicit 'when not to use' is a minor gap but not misleading.

    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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