xe-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation5/5
Each tool serves a distinct purpose: convert, get_rate, get_historical_rates, and various analytical tools (correlation, moving average, volatility) have clear boundaries. Specialized tools like nzd_corridors and optimal_send_window further differentiate. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent snake_case convention with informative verb_noun or noun patterns (e.g., get_rate, list_currencies, volatility_analysis). No naming style conflicts.
Tool Count5/5
12 tools is well-scoped for a currency exchange server, covering core operations (rates, conversions, history) and analytical features (correlation, volatility, alerts). Not overwhelming or sparse.
Completeness5/5
The tool set covers the full FX workflow: querying rates, historical data, conversion, statistical analysis, trend visualization, alerting, and a summary tool (pair_summary). No obvious gaps for typical use cases.
Average 3.9/5 across 12 of 12 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 52 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description mentions 'current mid-market rate' but omits behavioral details like rate source, real-time vs delayed, idempotency, side effects, or auth requirements. For a financial conversion tool, more transparency is expected.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no wasted words, clearly conveying the tool's purpose. However, it could benefit from additional details without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 3 parameters, no output schema, and no annotations, the description is bare. It does not explain return values, error handling, or edge cases. Given sibling tools like get_rate and pair_summary, more completeness would help agent choose 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 coverage is 100% with each parameter having a basic description. The tool description adds context about using the current mid-market rate but does not enrich parameter semantics beyond what the schema already provides. Baseline 3 is appropriate.
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?
Description clearly states the verb 'Convert', resource 'amount from one currency to another', and context 'at the current mid-market rate'. It distinguishes from sibling tools like get_historical_rates and get_rate by specifying the conversion action at current rate.
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?
No explicit guidance on when to use this tool versus alternatives such as get_rate or pair_summary. The description implies use for current-rate conversions but does not discuss prerequisites, limitations, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description discloses use of N-day distribution and statistical favorability but lacks details on statistical method, assumptions, limitations, or side effects. With no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with clear purpose, though no structural elements like bullet points or sections.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description lacks explanation of output format (e.g., percentile, recommendation) and how parameters interact. Given no output schema, this is a significant gap.
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 coverage is 100%, so baseline 3. Description adds no additional parameter meaning beyond the schema (e.g., how 'days' affects the distribution).
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?
Description clearly states the tool compares today's rate to an N-day distribution and assesses statistical favorability for conversion timing, distinguishing it from sibling tools like convert or get_rate.
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?
Description says 'Useful for FX timing decisions', providing context but no explicit when-to-use vs. alternatives or when-not-to-use guidance.
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?
No annotations exist, so the description carries full burden. It discloses the visual output (ASCII chart in terminal), but does not mention side effects, idempotency, rate limits, or handling of missing data. Basic behavioral context but not thorough.
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, no wasted words. Front-loaded with the core action. Arrow to the point and easy to read.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and moderate complexity, the description covers the basic purpose but omits details like default days, valid range (7-60), terminal width assumptions, or data freshness. Could be more complete.
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 coverage is 100% with clear descriptions for from/to/days. The description only adds 'over N days', which is redundant. No additional parameter meaning beyond schema.
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 explicitly states 'Render an ASCII line chart of a currency pair's rate history over N days', which is a specific verb+resource pair. It distinguishes from siblings like get_historical_rates (returns data not chart) and get_rate (single value). No ambiguity.
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 for visualizing trends ('Shows trend visually'), but does not provide explicit when-to-use or when-not-to-use guidance compared to siblings like moving_average or correlation_analysis. No exclusions or alternatives listed.
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?
Discloses data source and fallback behavior, which is valuable for an agent. However, no annotations exist, and the description does not cover rate limits, authentication details, or data freshness. Adequate but not comprehensive.
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, no unnecessary words. Front-loaded with the primary purpose, then adds fallback detail. Highly concise and efficient.
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 tool fetching historical rates, the description covers source behavior and scope. No output schema, but parameters are fully documented. Lacks mention of output format or error scenarios, but overall complete for moderate complexity.
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 coverage is 100%, so baseline is 3. Description adds 'mid-market rates' and 'over the past N days', but does not add additional meaning beyond the schema fields which already describe 'from', 'to', 'days' with constraints.
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?
Clearly states it 'fetches daily mid-market rates for a currency pair over the past N days'. Distinguishes from siblings like get_rate (single rate) and rate_chart (likely charting). Specific verb+resource+scope.
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?
Mentions fallback behavior (Xe vs Frankfurter) which helps agents decide based on credentials, but does not explicitly state when to use this tool over siblings or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. 'Snapshot' implies current data but no details on update frequency, caching, or whether it represents live or delayed rates. Lacks behavioral context.
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 concise sentences, front-loaded with key action ('Snapshot of NZD against all major trading pairs'). No waste.
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 tool with no output schema, description covers purpose and use case. Could add more about output format or data recency but adequate overall.
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?
No parameters, so schema coverage is 100%. Baseline of 4 applies as description adds no parameter info but doesn't need to.
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?
Clearly states it provides a snapshot of NZD against all major trading pairs simultaneously. Distinguishes from siblings like get_rate (single pair) and correlation_analysis (correlation).
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?
Indicates usefulness for a quick NZD strength overview, but does not explicitly state when not to use or contrast with alternatives like get_rate for specific pairs or volatility_analysis for deeper analysis.
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?
No annotations provided, so description must carry the burden. It lists expected outputs but does not disclose if the tool is read-only, requires permissions, or has side effects. The term 'briefing' suggests read-only, but not explicit.
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 no wasted words. First sentence efficiently states purpose and outputs, second explains composition. Front-loaded with key information.
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?
With no output schema, description sufficiently lists all returned components. However, it lacks detail on how the output is structured (e.g., separate fields or combined object). Acceptable for a summary 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?
Schema coverage is 100%, so baseline is 3. Description adds minimal extra semantics beyond schema; 'N-day range' hints at the 'days' parameter, but no further clarification on format or usage.
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?
Description clearly states it provides a morning briefing for a currency pair with specific outputs (current rate, N-day range, annualised volatility, send-window verdict, SMA(20)). It also identifies it as a composite of three other tools, distinguishing it from siblings.
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?
Suggests use as a 'one-call morning briefing' and mentions combining three tools, implying it reduces multiple calls. However, it does not explicitly state when to use it vs. alternatives like get_rate or volatility_analysis individually.
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?
Discloses computation method and return values clearly; no annotations provided, but description covers core behavior. Lacks mention of read-only nature or rate limits.
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?
Single sentence, front-loaded with purpose and output, no redundant information.
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?
No output schema, but description specifies return values; covers key inputs and methodology. Missing edge cases (e.g., insufficient data) but acceptable for a simple 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?
Schema coverage 100% with adequate parameter descriptions; description adds methodology context but no significant new semantic information beyond schema.
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?
Specific verb 'calculate' with resource 'volatility', clear output (daily std-dev and annualised), and methodology note differentiates from sibling tools like correlation_analysis and moving_average.
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?
No explicit guidance on when to use versus alternatives (e.g., correlation_analysis), no prerequisites or context for choosing this tool over siblings.
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?
No annotations provided, so description carries full burden. It discloses that it uses daily log-returns and Pearson correlation with range -1 to 1. However, it does not mention assumptions (e.g., linearity, normality), data requirements, or behavior for edge cases (e.g., missing days, pair not found). It adds value over schema by explaining log-returns and interpretation but lacks depth.
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?
Description is very concise: two sentences plus a brief range note. The core action is front-loaded in the first sentence. Every word contributes meaning without redundancy.
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?
No output schema exists, so description should clarify what the tool returns. It does explain the output range (correlation coefficient -1 to +1). However, it does not explicitly state the return format (single float? object with additional stats?) or mention any assumptions or limitations (e.g., linearity, data sufficiency). Overall, it covers the essential context for a correlation tool but could be slightly more complete.
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 coverage is 100%, so baseline is 3. Description adds context with example currencies (NZD, AUD, USD) and explains that pair1_from/pair1_to are base/quote currencies, but this does not significantly enhance understanding beyond the schema's parameter descriptions. No additional constraints or semantics for 'days' parameter beyond schema.
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?
Description clearly states it computes Pearson correlation of daily log-returns between two currency pairs over N days. Verb 'compute' and resource 'correlation' are specific. Example with NZD/USD vs AUD/USD and explanation of output range (-1 to +1) further clarify purpose, distinguishing it from sibling tools like moving_average or volatility_analysis.
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?
Description provides a clear usage scenario: 'Useful for understanding co-movement' with a concrete example. However, it does not explicitly state when not to use it or suggest alternative tools for different needs, which would improve guidance for an AI agent.
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?
No annotations provided; description carries full burden. Discloses the tool calculates and returns moving averages, which is non-destructive. However, it does not mention any limitations, prerequisites, or side effects beyond the basic calculation.
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, no redundancy. First sentence gives the core action, second adds key details. Front-loaded with primary purpose.
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?
Given the tool has 3 parameters and no output schema, the description sufficiently explains what the tool does and what it returns. For a simple calculation tool, it is adequately complete. Could mention data source or error handling but not critical.
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?
Schema coverage is 100% with descriptions for from, to, periods. Description adds value by explaining output includes current rate and distance from each MA, and that periods can be custom. This goes beyond the schema which only lists names and types.
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?
Description states 'Calculate simple moving averages (SMA) for a currency pair' with specific outputs (20, 50, 200-day SMAs or custom period, current rate, distance). Clearly distinguishes from sibling tools like convert or correlation_analysis.
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?
Says 'useful for trend assessment,' implying context but does not explicitly state when to use vs. alternatives or when not to use. Lacks explicit exclusions or comparisons to 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?
No annotations provided, so description must cover behavioral aspects. It discloses the dual-source fallback mechanism and that it provides mid-market rates. Absence of mention of rate latency or refresh frequency is a minor gap, but overall good transparency.
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, no redundancy. The most important info (what it does, source behavior) is front-loaded. Every sentence contributes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and description doesn't specify return format (e.g., numeric value, object). For a simple rate tool, the missing output description is a gap. However, the tool has decent annotations and simple parameters, so not critical but room for improvement.
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 coverage is 100% with parameter descriptions. Description adds context that it's the 'current mid-market rate' but doesn't add meaning beyond what schema provides for parameter semantics. Baseline is 3.
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?
Description clearly states 'get current mid-market exchange rate between two currencies' with specific verb and resource. Implicitly distinguishes from siblings like get_historical_rates and convert by specifying 'current' and 'mid-market'.
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?
Provides explicit guidance on data sources: uses Xe if credentials set, falls back to Frankfurter for free use. This helps the agent decide based on credential availability. No explicit when-not-to-use vs siblings, but the fallback behavior is clear.
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?
No annotations, but description adequately conveys it is a read-only check returning a boolean and current rate. No side effects mentioned, but for a check tool 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, highly concise and front-loaded with the purpose. Every sentence adds value.
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?
Despite no output schema, the description clearly explains return values and intended use. Sufficient for a tool with 4 well-documented parameters.
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 coverage is 100% with clear descriptions for all parameters. The description adds no extra semantic value beyond what the schema provides, so 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 tool checks if a currency pair's rate crossed a threshold and returns a boolean verdict and current rate. It distinguishes from siblings like 'get_rate' by specifying the threshold-crossing behavior.
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?
Explicitly mentions designed for polling loops and CI checks that trigger alerts. No explicit when-not-to-use, but the context signals sibling tools provide alternatives.
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?
No annotations present, so description carries full burden. It discloses that output depends on authentication (credentials), which is a key behavioral trait. No destructive actions or side effects are mentioned, but none are expected.
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, zero wasted words. Front-loaded with verb and resource. Every sentence adds value.
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?
Tool is simple (no params, no output schema). Description covers the two modes and credential dependency. For its complexity, it is fully complete.
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?
No parameters exist, so description adds no param info. Baseline 4 applies. The description does not need to elaborate beyond what schema (empty) provides.
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 it lists currency codes, distinguishes between two modes (common without credentials, full list with credentials), which differentiates it from siblings like convert or get_rate.
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?
Implies when to use the tool (for listing currencies, not conversions) and shows credential-dependent behavior. Lacks explicit when-not-to-use or alternative tool names, but context is clear.
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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/CedricConday/xe-mcp'

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