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Rootly-AI-Labs

oncallhealth-mcp

Official
by Rootly-AI-Labs
OverviewSchemaRelated ServersScoreDiscussions(1)
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: execute chains calls, get_schema retrieves parameter details, and search finds tools. There is no overlap or ambiguity.

    Naming Consistency4/5

    Tool names are imperative verbs (execute, search) and one uses get_ prefix (get_schema). While mostly consistent, the underscore in get_schema differs from the others, causing a minor deviation.

    Tool Count2/5

    With only 3 tools, the server feels underdeveloped for the domain implied by 'oncallhealth'. The tools are generic meta-tools rather than domain-specific actions, making the count too low for its apparent scope.

    Completeness1/5

    The tool set is severely incomplete for an oncall health server. It lacks any domain-specific tools (e.g., managing incidents, schedules, alerts) and only provides generic utility functions, failing to cover the intended domain.

  • Average 3.7/5 across 3 of 3 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
    • Last stable release on
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • 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.

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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?

    No annotations are provided, so the description must fully disclose behavioral traits. It only mentions ranking by relevance without explaining criteria, limitations, or side effects. No information about authentication, rate limits, or error handling.

    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 immediately convey the core function and return behavior. No unnecessary words, and the most important information is 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?

    Given the output schema exists, return values are covered. However, the description lacks context on search scope (all tools?), ranking algorithm, and interactions with other parameters. It is minimally adequate but not fully complete for a search tool with optional tags and detail options.

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

    Parameters3/5

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

    Schema coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema's parameter descriptions. It repeats 'by query' but offers no additional context like expected input format or behavior for each parameter.

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

    Purpose5/5

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

    The description clearly states the tool's purpose: 'Search for available tools by query' and 'Returns matching tools ranked by relevance.' It specifies the verb (search), object (tools), and method (by query), distinguishing it from siblings 'execute' and 'get_schema'.

    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 'execute' or 'get_schema'. It does not specify when not to use it, 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?

    No annotations are provided, so the description bears the full burden for behavioral disclosure. It does not indicate side effects, idempotency, or safety. The description only states what the tool does, not its behavioral characteristics, which is insufficient for an agent.

    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 consists of two sentences, is front-loaded with the purpose, and contains no unnecessary words. Every sentence is meaningful.

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

    Completeness4/5

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

    Given the tool's simplicity (2 parameters, output schema exists), the description covers the essential purpose and usage context. It does not lack critical information for a basic retrieval tool, though it could mention that different detail levels return different formats.

    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% coverage with descriptions for both parameters. The tool description does not add additional meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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

    Purpose5/5

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

    The description clearly states 'Get parameter schemas for specific tools', which is a specific verb+resource. It distinguishes itself from sibling tools 'execute' and 'search' by indicating it follows search and precedes execution.

    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 explicitly says 'Use after searching to get the detail needed to call a tool', providing clear context for when to use this tool. It does not mention when not to use it or alternatives, but the guidance is actionable.

    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, the description must fully disclose behavior. It mentions that code is async, only call_tool is in scope, and return produces output. Missing details on error handling, side effects, or resource 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?

    Three sentences, front-loaded with main purpose. No redundant information; every sentence adds 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?

    No output schema exists, so the description should clarify return format. It mentions 'return' for output but does not specify structure, error behavior, or limits. Somewhat incomplete for a code execution tool.

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

    Parameters4/5

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

    Schema coverage is 100%, but the description adds semantics by explaining that the code parameter should contain async code using call_tool and that output is produced via return. This goes beyond the schema's description.

    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 that the tool allows chaining await call_tool(...) calls in one Python block, which is a specific verb+resource. It distinguishes from sibling tools 'get_schema' and 'search' by focusing on execution of multiple tool calls rather than schema retrieval or searching.

    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 advises to 'prefer returning the final answer from a single block' and specifies that only call_tool is available, providing context for usage. However, it does not explicitly exclude scenarios or compare to alternatives.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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