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fastmcp-me

mcp-sentry

by fastmcp-me
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

50%
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  • Latest release: v0.6.2

  • Disambiguation2/5

    Both tools have nearly identical descriptions, both involving retrieving and analyzing Sentry issues. The only difference is the input parameter (project slug vs ID/URL), but the overlapping purpose and identical phrasing make it unclear which tool to use in a given scenario.

    Naming Consistency4/5

    Both tools follow a 'get_' prefix with snake_case, which is consistent. However, the first tool name 'get_list_issues' is slightly awkward (could be 'get_issues_by_project') but still fits the pattern. Minor deviation from ideal naming.

    Tool Count3/5

    With only 2 tools, the server feels underdeveloped for a comprehensive Sentry integration. While the scope is focused on retrieval, additional tools for updating or managing issues are expected. The count is borderline but not extremely low.

    Completeness2/5

    The tool set only covers retrieving issues by project or ID. Missing operations like updating issue status, assigning, or searching across projects create significant gaps that would hinder an agent from performing typical Sentry workflows.

  • Average 4/5 across 2 of 2 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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  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries full burden. It discloses what information the tool returns (stacktraces, first/last occurred, counts, status), which is helpful, but does not mention potential rate limits, authentication requirements, or data freshness. Adequate but not comprehensive.

    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 with two sentences plus a bullet list, front-loading the purpose. The bullet list could be slightly more compact, but overall well structured and easy to scan.

    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 simple retrieval tool with one parameter, no output schema, and no annotations, the description covers key use cases and return information. However, it lacks behavioral details (e.g., error handling, data freshness) that would make it fully complete for an agent.

    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 for the single parameter is 100%, and the schema already describes 'Sentry issue ID or URL'. The description repeats 'by ID or URL' in the first sentence, adding minimal additional meaning beyond the schema. Baseline 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 'Retrieve and analyze a Sentry issue by ID or URL' and lists specific use cases (investigate errors, access stacktraces, etc.), which distinguishes it from the sibling tool 'get_list_issues' that deals with multiple issues.

    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 this tool when you need to:' followed by a list of scenarios, providing clear usage context. However, it does not explicitly state when not to use it or mention the sibling tool as an alternative, though the sibling context from the environment suggests differentiation.

    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?

    No annotations provided, so description carries full burden. Details what the tool retrieves (stacktraces, error patterns, frequencies, timestamps, counts, status). Does not mention side effects, rate limits, or pagination, but for a read-like operation, it is sufficiently transparent.

    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 followed by bullet list that is easy to scan. No extraneous information. Well front-loaded with main purpose.

    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?

    Tool has 2 optional parameters, no output schema. Description covers functionality well but does not explain what happens if parameters are omitted or describe return value format. Minor 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?

    Schema covers 100% of parameters with descriptions. Description does not add extra meaning beyond the schema. Baseline of 3 is appropriate as schema is adequate.

    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 'Retrieve and analyze Sentry issues by project slug' and lists specific use cases like investigating errors and accessing stacktraces. Effectively distinguishes from sibling 'get_sentry_issue' by implying this tool returns a list.

    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?

    Provides explicit scenarios for when to use the tool via bullet points. Does not explicitly state when not to use, but the contrast with the sibling tool is implicit. Good guidance overall.

    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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