OpenTelemetry MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between 'query_loki' and 'search_logs' as both handle log queries, which could cause confusion. The other tools are clearly differentiated by their target systems (Prometheus vs. Loki) and query types (instant vs. range).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with clear, descriptive naming. The verbs ('list', 'query', 'search') are used appropriately, and the nouns ('labels', 'metrics', 'logs') accurately reflect the target resources, making the set highly predictable.
Tool Count5/5
With 9 tools, the server is well-scoped for its OpenTelemetry monitoring purpose. It covers essential operations for both Prometheus (metrics) and Loki (logs), including discovery, querying, and searching, without being overly complex or insufficient.
Completeness4/5
The tool set provides strong coverage for querying and discovering metrics and logs, but lacks write operations (e.g., creating or updating metrics/logs) which might be expected in a full OpenTelemetry workflow. However, for read-only monitoring tasks, it is largely complete.
Average 3.4/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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
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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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool retrieves values for discovery purposes, but fails to detail critical behaviors such as whether it's read-only, if there are rate limits, authentication requirements, or how results are returned (e.g., pagination, format). This omission is significant for a tool with no annotation coverage.
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?
The description is concise and front-loaded, with two sentences that efficiently convey the tool's purpose and usage context. Every sentence adds value: the first defines the action and examples, and the second explains the use case. There's no wasted text, making it easy to scan and understand quickly.
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 the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the basic purpose and usage context but lacks behavioral details and output information, which are crucial for effective tool invocation. Without annotations or an output schema, more guidance on behavior and results would improve completeness.
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 description coverage is 100%, so the input schema already documents both parameters ('label' and 'metric') thoroughly. The description adds minimal value by listing example labels ('service', 'job', 'namespace', 'instance') and hinting at the 'metric' parameter's filtering role, but doesn't provide additional syntax or format details beyond what the schema offers. 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.
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: 'Get all values for a specific label' with examples like 'service', 'job', 'namespace', 'instance'. It specifies the verb ('Get') and resource ('values for a specific label'), making it easy to understand. However, it doesn't explicitly distinguish this tool from its sibling 'list_log_label_values', which might handle similar data but for logs, leaving some 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 provides implied usage guidance by stating 'Use this to discover what services/namespaces are monitored' and listing common labels, which suggests it's for exploration and monitoring contexts. However, it lacks explicit when-to-use vs. when-not-to-use instructions or direct alternatives among siblings like 'list_labels' or 'list_log_label_values', leaving room for confusion in tool selection.
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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'useful for discovering,' which hints at read-only behavior, but doesn't explicitly state whether it's safe, if it requires authentication, what the return format is (e.g., list, JSON), or any rate limits. This leaves significant gaps for an agent to understand how to invoke it effectively.
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?
The description is two short sentences that are front-loaded with the core purpose. There's no wasted text, and it efficiently conveys the main use case. However, it could be slightly more structured by explicitly separating purpose from guidance, but it's still very concise.
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 the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and a hint of usage, but lacks details on behavior (e.g., response format, safety) that would be helpful for an agent. Without annotations or output schema, the description should do more to compensate, but it only partially meets that need.
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 has 100% description coverage, with the 'prefix' parameter well-documented in the schema itself. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., no examples of common prefixes or clarification on filtering behavior). With high schema coverage, the baseline is 3, and the description doesn't compensate with extra insights.
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 action ('List all available metrics') and resource ('in Prometheus'), making the purpose immediately understandable. It distinguishes from siblings like list_labels or query_prometheus by focusing specifically on metrics. However, it doesn't explicitly differentiate from all siblings (e.g., list_log_labels is for logs, not metrics), so it's not a perfect 5.
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 provides implied usage guidance by stating it's 'Useful for discovering what metrics are being collected,' suggesting it's for exploration rather than querying. However, it doesn't explicitly state when to use this vs. alternatives like query_prometheus (for actual metric values) or list_labels (for label names), nor does it mention any prerequisites 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns time series data, which is useful, but doesn't cover critical aspects like authentication requirements, rate limits, error handling, or performance implications. For a query tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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 extremely concise and front-loaded: two sentences that directly state the tool's function and usage. Every word earns its place with zero waste, making it easy for an AI agent to parse quickly.
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?
Given the complexity of a PromQL query tool with no annotations and no output schema, the description is incomplete. It lacks details on return format (e.g., structure of time series data), error cases, or prerequisites. While concise, it doesn't provide enough context for safe and effective use without additional assumptions.
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%, meaning the input schema fully documents all parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain query syntax or step resolution trade-offs). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
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: 'Execute PromQL query over a time range. Returns time series data.' It specifies the verb (execute), resource (PromQL query), and scope (time range). However, it doesn't explicitly differentiate from sibling tools like 'query_prometheus' (which likely handles instant queries), so it misses the highest score.
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 provides implied usage guidance: 'Use this for getting metrics over time.' This suggests when to use it (for time-series metrics) but doesn't explicitly state when not to use it or name alternatives like 'query_prometheus' for instant queries. It offers basic context but lacks explicit exclusions or sibling comparisons.
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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool is 'easier than writing raw LogQL queries,' which hints at a simplified interface, but fails to describe key behaviors such as authentication requirements, rate limits, error handling, or the format of returned results. For a search tool with no annotation coverage, this is a significant gap.
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 extremely concise with two short sentences that are front-loaded and waste no words. Every part earns its place by stating the purpose and a key comparative advantage, making it efficient and easy to parse.
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?
Given the complexity of a search tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, result formatting, error cases, and explicit usage scenarios. While the schema covers parameters well, the overall context for effective tool invocation is insufficient.
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 has 100% description coverage, providing clear details for all 7 parameters. The description adds minimal value beyond the schema, only implying that filters are 'simple' without elaborating on parameter interactions or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't significantly enhance parameter understanding.
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: 'Search logs with simple filters.' It specifies the action (search) and resource (logs) with a qualifier (simple filters). However, it doesn't explicitly distinguish this tool from its siblings like 'query_loki' which might handle more complex queries, though the mention of 'easier than writing raw LogQL queries' hints at differentiation.
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 provides implied usage guidance by stating it's 'easier than writing raw LogQL queries,' suggesting this tool is for simpler searches compared to alternatives. However, it lacks explicit when-to-use rules, prerequisites, or clear comparisons with sibling tools like 'query_loki' or 'list_logs' (if present), leaving some ambiguity.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool lists labels for 'filtering' but doesn't describe key behaviors like whether it's read-only, how it handles time ranges (start/end parameters), potential rate limits, or what the output looks like (e.g., list format). This leaves significant gaps for an agent to understand the tool's operation.
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 concise sentences with zero waste: the first states the purpose, and the second provides usage context. It's front-loaded with the core action and efficiently conveys essential information without unnecessary details.
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 the tool's low complexity (2 optional parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and usage but lacks details on behavior, output format, and error handling. For a discovery tool in a logging context, more context on what 'labels' represent or how results are structured would improve completeness.
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 has 100% description coverage, with clear documentation for 'start' and 'end' parameters. The description doesn't add any parameter-specific semantics beyond what the schema provides, such as explaining time format or default behavior. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 verb 'List' and resource 'all log stream labels in Loki', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_labels' or 'list_label_values', which likely have overlapping functionality in the same Loki context.
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 provides implied usage guidance by stating 'Use this to discover what labels are available for filtering', which suggests when to use it (for discovery before filtering). However, it doesn't explicitly state when not to use it or name alternatives among the sibling tools, leaving some ambiguity about tool selection.
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?
With no annotations provided, the description carries full burden but only states it 'executes' queries without disclosing behavioral traits like authentication requirements, rate limits, error handling, or what happens on execution (e.g., read-only vs. destructive). It mentions searching and filtering but doesn't elaborate on system impact or constraints.
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 concise sentences with zero waste: the first states the action and target, the second clarifies the purpose. It's front-loaded and appropriately sized, with every sentence earning its place by adding value.
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 5 parameters with 100% schema coverage but no annotations or output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavioral context, return values, or error handling, leaving gaps for a tool that executes queries against a logging system.
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 parameters are fully documented in the schema. The description adds no additional parameter semantics beyond the general purpose, maintaining the baseline score of 3 as it doesn't compensate but also doesn't detract from schema information.
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 action ('Execute raw LogQL query') and target ('against Loki'), with the purpose 'to search and filter logs'. It distinguishes from siblings like list_labels or query_prometheus by focusing on raw LogQL execution, but doesn't explicitly contrast with search_logs which might overlap.
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 executing LogQL queries, but provides no explicit guidance on when to use this tool versus alternatives like search_logs or query_prometheus. It mentions the general purpose 'to search and filter logs', which gives some context but lacks specific when/when-not instructions.
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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves label names but doesn't cover critical aspects like whether it's read-only, if it requires authentication, rate limits, pagination, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operational behavior.
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 highly concise and well-structured: two sentences that directly state the purpose and usage without any fluff. Each sentence earns its place by providing essential information, making it easy to parse and front-loaded with the core functionality.
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 the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and usage but lacks details on behavior, output format, or error cases. Without annotations or output schema, more context on what the tool returns would improve completeness for agent invocation.
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 has 100% description coverage, with one optional parameter 'metric' documented as 'Optional metric name to get labels for.' The description adds no additional parameter semantics beyond this, as it doesn't explain how the metric parameter affects results or provide examples. Baseline score of 3 is appropriate since the schema handles the heavy lifting.
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: 'Get all label names available in Prometheus.' It specifies the verb ('Get') and resource ('label names'), and distinguishes it from siblings by focusing on label discovery rather than values, metrics, or queries. However, it doesn't explicitly differentiate from 'list_log_labels' (which handles logs vs. metrics), leaving minor ambiguity.
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 provides clear usage guidance: 'Use this to discover what labels you can filter by.' This implies it's for exploration before filtering, distinguishing it from tools like 'query_prometheus' that perform actual queries. It doesn't explicitly state when not to use it or name alternatives, but the context is sufficient for basic differentiation.
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 are provided, so the description carries the full burden. It describes a read operation ('Get all values') which implies it's non-destructive, but doesn't mention authentication requirements, rate limits, or what happens when no values are found. The time range parameters suggest temporal filtering behavior, but this isn't explicitly explained.
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 perfectly concise with two sentences that each serve distinct purposes: the first states the core functionality, the second provides usage context and examples. There's zero wasted language, and the most important information comes first.
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?
For a read-only tool with 3 parameters and no output schema, the description provides adequate but minimal context. It explains what the tool does and why to use it, but doesn't describe return format, pagination, error conditions, or how the time parameters affect results. Given the lack of annotations and output schema, more behavioral detail would be helpful.
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 the schema already documents all three parameters. The description adds value by providing concrete examples of label values ('namespace', 'job', 'app') and explaining the tool's purpose in discovering namespaces/services, which gives context for the 'label' parameter. However, it doesn't add significant semantic information beyond what the schema provides.
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: 'Get all values for a specific log label.' It specifies the resource (log label values) and provides examples of common labels. However, it doesn't explicitly distinguish this tool from its sibling 'list_label_values' which appears to have a similar function.
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 provides clear context for when to use this tool: 'Use this to discover what namespaces/services have logs.' It gives practical examples of labels to query. While it doesn't explicitly state when NOT to use it or name alternatives, the context is sufficiently clear for basic usage.
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?
With no annotations provided, the description carries the full burden. It discloses the query type (instant) and return semantics (metric values at a point in time), which is useful. However, it lacks details on error handling, rate limits, authentication needs, or response format, which are important for a query tool with potential complexity.
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 concise sentences with zero waste. The first sentence states the action and resource, and the second clarifies the return value and temporal scope. Every word earns its place, making it highly efficient and front-loaded.
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?
For a query tool with no annotations and no output schema, the description is minimally adequate. It covers the core purpose and distinguishes from siblings, but lacks details on behavioral traits (e.g., error cases, performance implications) and output structure, which could hinder an agent's ability to use it correctly in complex scenarios.
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 the schema fully documents both parameters. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., no examples of valid 'time' formats beyond what's in the schema). Baseline 3 is appropriate as the schema does the heavy lifting.
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 specific action ('Execute raw PromQL instant query') and resource ('against Prometheus'), distinguishing it from siblings like 'query_prometheus_range' (instant vs range queries) and 'query_loki' (Prometheus vs Loki). It explicitly defines the return value ('metric values at a specific point in time'), making the purpose unambiguous.
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 provides clear context by specifying this is for 'instant query' and 'at a specific point in time', which implicitly distinguishes it from 'query_prometheus_range' for range queries. However, it doesn't explicitly state when NOT to use this tool or mention alternatives like 'list_metrics' for discovery, leaving some guidance gaps.
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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