Observability Agent MCP

Server Quality Checklist

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Latest release: v0.2.1
Disambiguation4/5
Tools have distinct purposes: alerts, capabilities, health, incident context, metrics query, dashboard/panel rendering. Slight overlap between health_snapshot and incident_context (which includes health), but overall clear boundaries.
Naming Consistency4/5
All tools share 'observability.' prefix and use snake_case. However, some use noun phrases (capabilities, incident_context) while others use verb_noun (query_metrics, render_dashboard), causing minor inconsistency in pattern.
Tool Count5/5
7 tools is well-scoped for an observability agent covering alerts, health, metrics, dashboards, and capabilities. Each tool serves a clear function without redundancy.
Completeness4/5
Covers core observability aspects (alerts, health, metrics, visuals). Missing logs and traces, but given the agent-safe and bounded focus, the set is reasonably complete for its stated purpose.
Average 3/5 across 7 of 7 tools scored. Lowest: 2.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 57 commits in the last 12 weeks
- Last stable release on July 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description adds only 'normalized,' which lacks specificity about behavior or data scope.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While the description is very short (one sentence), it is under-specified to the point of being unhelpful. Important details about parameters and output are omitted.
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 optional filter parameters and an output schema (implied by context signals), the description should explain what 'normalized' means and how filters apply. It lacks essential context for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% with 3 parameters, but the description mentions none of them. The schema defines filters (states, services, severities) but the description provides no explanation, leaving the agent to infer meaning.
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 'Return normalized active alert metadata,' which clearly indicates the tool retrieves alert data. However, it does not differentiate from sibling tools like incident_context or health_snapshot, and 'normalized' is ambiguous.
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 on when to use this tool versus alternatives (e.g., query_metrics or incident_context). The description provides no context for suitable use cases or prerequisites.
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?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds no further behavioral context, such as time-bounds constraints or result characteristics, beyond what is implied by parameter names.
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 concise (one sentence) but excessively brief. It does not earn its space as it fails to add meaningful information beyond the name and schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description is too sparse for a tool with five parameters and complex time formats. It lacks essential details about query bounds, template naming, step behavior, and result structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. However, it only says 'bounded named metrics query', which explains none of the five parameters (at, to, from, stepMs, queryTemplate) or their semantics.
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 'Run a bounded named metrics query' identifies the verb and resource but is vague. 'Bounded' hints at time constraints but doesn't specify the query's scope or differentiate it from sibling tools, which are clearly distinct in purpose.
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 usage guidance is provided. The description does not indicate when to use this tool versus alternatives, nor does it mention any prerequisites or context.
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?
Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds limited extra behavior: 'bounded' hints at size limits (consistent with schema maxItems=25) but doesn't elaborate on other traits like return format or permission needs.
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?
A single sentence is concise and front-loaded with the core action. However, it lacks additional context that could fit without bloating, making it adequate but not excellent.
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 presence of an output schema and annotations, the description is minimally complete for a basic tool. However, it fails to clarify the 'services' parameter and usage context relative to siblings. It meets the minimum but has notable gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should explain the services parameter. It says 'for logical services' but omits details like required, format, limits (though schema has them). The schema adds structure, but the description does not compensate for the lack of parameter descriptions.
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 'Return bounded health evidence for logical services' identifies the action (return) and resource (health evidence for logical services). It distinguishes from sibling tools like active_alerts and incident_context, though 'bounded' is ambiguous.
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. The description implies it's for health snapshots but doesn't compare with siblings like query_metrics or render_dashboard.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, and destructiveHint as true, true, and false respectively, so the tool is safe and idempotent. The description adds that it 'builds' context, implying computation but not mutation. This provides marginal additional context beyond the annotations.
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 of 8 words, highly concise and front-loaded. While it sacrifices detail, it contains no unnecessary words, earning a strong score for structure.
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 optional parameters, an output schema (unknown content), and sibling tools, the description is too sparse. It does not explain the output format, how to interpret results, or how this tool fits into the observability workflow. Completeness is poor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has three parameters (alertId, serviceId, includeVisuals) with 0% description coverage. The description does not explain any parameter semantics; it only loosely relates 'optional visual context' to includeVisuals. The tool fails to guide the agent on how to properly populate the parameters.
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 states the tool builds 'bounded health, alert, metric, and optional visual context', which clearly indicates it aggregates multiple observability aspects for incident context. The verb 'build' and resource 'incident context' are specific, but it doesn't explicitly differentiate from sibling tools like 'health_snapshot' or 'query_metrics'.
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 such as 'observability.active_alerts' or 'observability.health_snapshot'. The description lacks context on prerequisites or exclusions, leaving the agent without clear direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true. The description adds context about output being 'bounded PNG evidence' and safety ('agent-safe'), which is valuable beyond annotations.
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 concise sentence that front-loads the purpose. However, it could be structured with additional detail without sacrificing conciseness.
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 6 parameters (3 required) and no parameter descriptions, the tool description is too sparse. It fails to explain key concepts like 'allowlisted' dashboards, how to obtain dashboard IDs, or output details, despite having an output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description does not explain any parameters. The agent must infer meaning solely from parameter names, types, and constraints, which is insufficient for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Render', the resource 'dashboard', and includes constraints like 'allowlisted', 'agent-safe', and 'bounded PNG evidence'. This distinguishes it from sibling tools like render_panel.
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 is limited to allowlisted dashboards but does not explicitly state when to use this tool over alternatives like render_panel. No when-not or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety; description adds 'allowlisted' and 'bounded PNG', but no further behavioral details like authentication or output 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?
Single sentence, no unnecessary words, front-loaded with key action and output.
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?
Despite output schema existing, the description lacks detail on required parameters (from, to, dashboardId, panelId) and usage context, making it incomplete for a 7-parameter tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameter descriptions in input schema (0% coverage) and description does not explain any parameters, leaving the agent to infer meaning from names and schema constraints alone.
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 renders one allowlisted logical panel as bounded PNG evidence, distinguishing it from render_dashboard and other tools.
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?
Implies use for single panel vs render_dashboard, but no explicit when or when-not to use, no alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description reinforces 'read-only' and adds 'bounded limits' context, adding value without contradiction.
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 verb, no redundancy. Every word earns its place.
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?
Given zero parameters, rich annotations, and an output schema, the description sufficiently completes the tool definition for an agent to understand its role.
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 schema coverage is 100%. The description does not need to add param details; it effectively defines the tool's purpose.
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 'describes enabled read-only observability tools and bounded limits', with a specific verb and resource. It distinguishes from siblings (e.g., active_alerts, query_metrics) as a discovery/metadata tool.
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 discovering available tools and limits, but provides no explicit guidance on when to use this vs. alternatives 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.

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