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tuno-dev

Datadog MCP Server

by tuno-dev
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    The three tools are mostly distinct, with 'aggregate_spans' focusing on aggregation of trace data, 'search_logs' targeting log data, and 'search_spans' targeting trace data. However, 'aggregate_spans' and 'search_spans' both operate on spans and could be confused for overlapping purposes, though their descriptions clarify one aggregates and the other searches.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case, using 'aggregate' and 'search' as verbs paired with specific nouns like 'spans' and 'logs'. There are no deviations in naming style or convention.

    Tool Count3/5

    With only 3 tools, the server feels thin for a Datadog integration, which typically involves monitoring, metrics, dashboards, and alerts. While the tools cover traces and logs, the scope is limited, making it borderline appropriate for the apparent domain.

    Completeness2/5

    The tool surface has significant gaps for a Datadog server, missing core operations like querying metrics, managing dashboards, setting alerts, or accessing APM data beyond spans. Agents will struggle with incomplete coverage for common monitoring workflows.

  • Average 2.6/5 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
    • 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.

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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?

    With no annotations, the description carries full burden but provides minimal behavioral insight. It doesn't disclose whether this is a read-only operation, its performance characteristics, rate limits, or what the aggregation output looks like. The description only states the tool's purpose without behavioral traits.

    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 no wasted words. It's appropriately sized for a tool with a clear name and detailed schema. However, it could be more front-loaded with key details like aggregation type.

    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 6-parameter aggregation tool with no annotations and no output schema, the description is incomplete. It doesn't explain the return format, aggregation metrics, or how results are structured. The schema handles parameters well, but behavioral and output context is missing.

    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 6 parameters. The description adds no parameter-specific information beyond what's in the schema. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate or add value.

    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 aggregates Datadog trace spans, which is a clear purpose. However, it doesn't specify what aggregation means (e.g., counting, averaging, summing) or how it differs from sibling tools like search_spans. The verb 'aggregating' is specific but lacks operational detail.

    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 search_spans. The description doesn't mention any prerequisites, exclusions, or contextual cues for selection. Usage is implied only by the tool name and 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?

    With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic function. It doesn't mention authentication needs, rate limits, pagination behavior beyond the cursor parameter, error handling, or what the output looks like (especially critical since there's no output schema). For a search tool with 5 parameters and no annotations, this is inadequate.

    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 no wasted words. It's appropriately sized for a basic tool description, though it could be more informative while remaining 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 (5 parameters, no annotations, no output schema, and sibling tools), the description is incomplete. It doesn't help the agent understand what the tool returns, how to interpret results, or how it differs from similar tools. For a search operation with multiple parameters and no output schema, more context 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 all parameters are documented in the schema. The description adds no additional meaning about parameters beyond implying a search function. It doesn't explain query syntax, time format nuances, or how pagination works in practice. 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.

    Purpose3/5

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

    The description 'Tool for searching Datadog logs' states the basic action (searching) and resource (Datadog logs), but it's vague about scope and doesn't differentiate from sibling tools like 'search_spans'. It doesn't specify what kind of logs or what search capabilities exist beyond the basic verb+resource.

    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 'search_spans' or 'aggregate_spans'. The description doesn't mention any context, prerequisites, or exclusions for usage, leaving the agent with no comparative information.

    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. It only states it's a search tool without mentioning whether it's read-only, what permissions are needed, rate limits, pagination behavior (though schema hints at it), or what the output looks like. For a search tool with 5 parameters, 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a search tool, though it could be more informative by adding context about when to use it versus siblings.

    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 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the search returns, how results are structured, or behavioral aspects like pagination. For a search tool with moderate complexity, this leaves too much unspecified.

    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 5 parameters with their types, defaults, and descriptions. The description adds no additional parameter information beyond what's in the schema. This meets the baseline of 3 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 states the tool searches Datadog trace spans, which is a clear verb+resource combination. However, it doesn't differentiate from sibling tools like 'aggregate_spans' (which likely aggregates rather than searches) or 'search_logs' (which searches logs rather than spans). The purpose is understandable but lacks sibling 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. There's no mention of when to choose search_spans over aggregate_spans or search_logs, nor any context about prerequisites or typical use cases. The user must infer usage from the tool name alone.

    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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  • Evaluate tool definition quality.

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