prometheus-mcp-server

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose targeting specific Prometheus resources or query types, with no overlapping functionality. For example, get-alerts retrieves alerts, get-series finds series by labels, and instant-query versus range-query handle different query modes, making misselection unlikely.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern with 'get-' or query-type prefixes for most tools, making them predictable. However, 'context-prometheus' deviates slightly by using a noun_verb structure and being in Portuguese, which breaks the pattern but is still understandable.
Tool Count5/5
With 10 tools, this is well-scoped for a Prometheus server, covering key operations like queries, metadata, alerts, rules, and status without being overwhelming. Each tool serves a distinct and necessary function in monitoring and data retrieval.
Completeness4/5
The toolset provides comprehensive coverage for querying, monitoring, and status checks in Prometheus, including instant and range queries, alerts, rules, and targets. A minor gap exists in management operations like configuration changes or data deletion, but core workflows are fully supported.
Average 2.8/5 across 10 of 10 tools scored. Lowest: 2.2/5.
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
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action ('Get information'), but fails to describe any behavioral traits such as whether it's read-only, requires authentication, has rate limits, returns structured data, or involves pagination. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence ('Get information about alerts'), which is appropriately sized for a simple tool. However, it is not front-loaded with critical details like purpose differentiation or usage context, and the brevity borders on under-specification rather than efficient communication.
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 tool's simplicity (0 parameters, no output schema, no annotations), the description is incomplete. It fails to provide necessary context such as what 'alerts' refer to in this system, how the information is returned, or how it relates to sibling tools. Without annotations or output schema, the description should do more to explain the tool's role and behavior.
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 input schema has 0 parameters with 100% description coverage, meaning no parameters need documentation. The description does not add parameter details, which is appropriate here. A baseline score of 4 is applied as the description does not need to compensate for any parameter gaps, though it could theoretically mention the absence of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get information about alerts' is a tautology that essentially restates the tool name 'get-alerts' with minimal elaboration. While it indicates a retrieval action ('Get') and the resource type ('alerts'), it lacks specificity about what kind of alert information is retrieved or how it differs from sibling tools like 'get-rules' or 'get-status'. This makes it vague and unhelpful for distinguishing purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 alternatives. It does not mention any context, prerequisites, or exclusions, nor does it reference sibling tools such as 'get-rules' or 'instant-query' that might handle related functions. This absence of usage instructions leaves the agent without direction for 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 states 'Get metadata for metrics' but doesn't clarify if this is a read-only operation, what permissions are needed, whether it's safe or destructive, or how results are structured (e.g., pagination, error handling). This leaves significant gaps for a tool with parameters.
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 a single, efficient sentence with zero waste, making it appropriately concise. However, it's front-loaded with minimal content, which could benefit from more detail given the lack of annotations and output schema, but it's not overly 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?
Given the complexity (2 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'metadata' entails, how results are returned, or behavioral traits, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
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 input schema already documents both parameters ('metric' and 'limit') fully. The description adds no additional meaning beyond what the schema provides, such as explaining what 'metadata' includes or how parameters interact. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get metadata for metrics' states a clear verb ('Get') and resource ('metadata for metrics'), but it's vague about what 'metadata' entails and doesn't distinguish this tool from siblings like 'get-series' or 'get-rules' that might also retrieve metric-related data. It provides a basic purpose but lacks specificity.
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 guidance is provided on when to use this tool versus alternatives like 'get-series' or 'instant-query'. The description implies usage for metadata retrieval but offers no context on prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer usage scenarios.
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. The description only states 'Get information about targets' without mentioning whether this is a read-only operation, what permissions might be required, whether it returns paginated results, or what format the information comes in. For a tool with no annotation coverage, this is insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just four words, which is efficient. However, this conciseness comes at the cost of being under-specified rather than appropriately sized. The single sentence doesn't waste words but fails to provide necessary context.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'targets' are in this context, what information is returned, or how this tool relates to the sibling Prometheus-related tools. For a tool in what appears to be a monitoring system context, more explanation is needed.
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 fully documents the single parameter 'state' with its enum values and description. The description adds no additional parameter information beyond what the schema provides, which meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get information about targets' states a general purpose but lacks specificity. It mentions the verb 'Get' and resource 'targets', but doesn't clarify what kind of information is retrieved or how this differs from sibling tools like 'get-alerts' or 'get-series'. The purpose is vague rather than specific.
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 alternatives. With sibling tools like 'get-alerts', 'get-rules', and 'get-series' available, there's no indication of when this tool is appropriate versus those others. No context or exclusions are mentioned.
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 for behavioral disclosure but only states what the tool does, not how it behaves. It doesn't mention whether this is a read-only operation, what permissions are needed, how results are returned (format, pagination), error conditions, or performance characteristics. This is inadequate for a tool with parameters and 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase with zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately. Every word earns its place in this minimal but complete statement.
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 tool has 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'series' means in this Prometheus context, what the return format looks like, or any behavioral aspects. For a data query tool with multiple parameters, this leaves significant gaps in understanding how to effectively use it.
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 all three parameters (match, start, end) with good descriptions. The description adds no additional parameter semantics beyond implying label matching through 'label matchers', which is already covered in the schema's match parameter description. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Find series by label matchers' clearly states the action (find) and target (series), but is vague about what 'series' refers to in this context (Prometheus time series data) and doesn't distinguish this tool from siblings like 'get-label-values' or 'instant-query'. It provides basic purpose but lacks specificity and differentiation.
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 alternatives like 'instant-query' or 'range-query' from the sibling list. It doesn't mention prerequisites, exclusions, or typical use cases, leaving the agent with no contextual usage information beyond the basic purpose.
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. The description implies this is a read-only operation ('consultes'), but it doesn't specify what the tool returns (e.g., documentation, examples, or metadata), any rate limits, authentication needs, or potential side effects. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 a single sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and efficient, though it could be slightly more specific to improve clarity. There is no wasted verbiage, making it appropriately concise.
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 interacting with Prometheus and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., textual guidelines, structured data, or examples), how it integrates with sibling tools, or any behavioral traits. For a tool in a set with multiple query-related siblings, more context is needed to guide effective use.
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 0 parameters, and the schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. This meets the baseline for a parameterless tool, though it could theoretically hint at implicit inputs if relevant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as consulting rules and guidelines for making queries and interacting with Prometheus, which is clear but somewhat vague. It doesn't specify what specific resources or actions are involved, nor does it distinguish this tool from its siblings like 'instant-query' or 'range-query' that might also involve query-related operations.
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 alternatives. It doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage based on the name alone. This lack of explicit guidelines makes it unclear how this tool differs from sibling query-related tools.
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 states 'Get' implies a read operation but doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what format the values are in (e.g., strings, arrays). This is inadequate 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded and appropriately sized for a simple tool, making it easy 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'label values' are in this context (e.g., metric label values in Prometheus), the return format, or error conditions. For a tool with no structured metadata, this leaves significant gaps.
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%, with the single parameter 'labelName' documented as 'Label name to get values for'. The description adds no additional meaning beyond this, such as examples or constraints (e.g., label names must exist). Baseline 3 is appropriate when the schema does 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 'Get label values for a label name' clearly states the verb ('Get') and resource ('label values'), making the purpose understandable. However, it doesn't differentiate this tool from its siblings like 'get-series' or 'get-metadata', which might also involve label-related operations in a Prometheus context.
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 alternatives. It doesn't mention context (e.g., for querying Prometheus metrics), prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone among multiple sibling tools.
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. While 'Execute' implies an operation that might have side effects or performance implications, the description doesn't mention whether this is a read-only operation, what authentication might be required, potential rate limits, or what happens when queries fail. For a query execution tool with zero 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 at just 5 words, front-loading the essential information with zero wasted words. Every element ('Execute', 'instant', 'Prometheus query') earns its place in communicating the core functionality.
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?
For a query execution tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what an 'instant' query means in Prometheus context, what format the results will be in, or any error conditions. Given the complexity of query execution and the lack of structured metadata, the description should provide more operational context.
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%, meaning all parameters are well-documented in the input schema itself. The description doesn't add any additional parameter semantics beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is high, but doesn't provide extra value.
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') and resource ('an instant Prometheus query'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'range-query' tool, which appears to be a closely related alternative for querying Prometheus data with a different time range approach.
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 alternatives like 'range-query' or other Prometheus querying tools. There's no mention of what makes an 'instant' query different from other query types, nor any prerequisites or context for when this specific tool is appropriate.
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 for behavioral disclosure but only states the basic action. It doesn't mention whether this is a read-only operation, potential side effects, authentication requirements, rate limits, or error handling. For a query execution tool with zero annotation coverage, this leaves significant behavioral gaps.
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 a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an 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?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what a 'range' query entails versus instant queries, what format the results return, or any behavioral context needed for proper invocation. The agent would need to infer too much from the minimal description.
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 all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema descriptions, maintaining the baseline score 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 action ('Execute') and resource ('range Prometheus query'), making the purpose immediately understandable. However, it doesn't distinguish this from sibling tools like 'instant-query' or 'context-prometheus', which would require explicit differentiation for a perfect score.
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 alternatives like 'instant-query' or other Prometheus-related siblings. There's no mention of specific use cases, prerequisites, or exclusions, leaving the agent without contextual direction for 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 the full burden of behavioral disclosure but only states what the tool does at a high level. It doesn't describe whether this is a read-only operation, what format the information is returned in, potential rate limits, or any side effects, which are critical for a tool that fetches 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?
The description is a single, clear sentence that directly states the tool's purpose without any fluff or redundancy. It is front-loaded and efficiently communicates the core function, making it easy for an 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 lack of annotations and output schema, the description is incomplete for a data-fetching tool. It doesn't specify what 'information' includes (e.g., rule definitions, statuses, metadata), the return format, or any behavioral traits like pagination or error handling, leaving significant gaps for the agent.
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 input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. A baseline of 4 is appropriate as the description doesn't need to compensate for missing param info.
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 with a specific verb ('Get') and resource ('information about alerting and recording rules'), making it immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling 'get-alerts', which might handle similar alert-related data, leaving room for potential confusion.
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 alternatives like 'get-alerts' or 'instant-query'. It lacks context about whether this is for listing rules, retrieving details, or other specific use cases, leaving the agent to infer usage from the name alone.
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 it 'gets' information, implying a read-only operation, but doesn't specify whether this requires authentication, has rate limits, returns structured data, or involves any side effects. For a tool with zero 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse 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 has one parameter with full schema coverage and no output schema, the description is minimally adequate but incomplete. It doesn't explain what kind of information is returned (e.g., JSON structure, text output) or address behavioral aspects like error handling, which would be helpful for an agent to use it 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?
The schema description coverage is 100%, with the parameter 'statusType' fully documented in the schema including its enum values. The description doesn't add any meaning beyond what the schema provides, such as explaining the differences between status types like 'config' vs 'flags'. Baseline 3 is appropriate since the schema does 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 verb 'Get' and the resource 'status information about the Prometheus server', making the purpose understandable. However, it doesn't explicitly differentiate this from sibling tools like 'get-targets' or 'get-rules', which also retrieve information from Prometheus, so it doesn't fully distinguish itself from 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 alternatives like 'get-alerts' or 'get-rules'. It doesn't mention specific scenarios, prerequisites, or exclusions, leaving the agent with no context for tool selection beyond the general purpose stated.
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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OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mateusdata/prometheus-mcp-server'

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