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MikeAtPinnacle

jira-worklog-mcp

by MikeAtPinnacle
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: adding a worklog, listing worklogs, and getting user info. There is no overlap or confusion between them.

    Naming Consistency4/5

    Most tools follow verb_noun pattern (add_worklog, list_worklogs), but whoami breaks the pattern. Naming is mostly consistent with minor deviation.

    Tool Count5/5

    Three tools is appropriate for a focused worklog management server. Each tool provides essential functionality without excess.

    Completeness3/5

    Covers basic add and list operations but lacks update and delete worklog functionality. The whoami tool is auxiliary. The surface is somewhat incomplete for full worklog lifecycle management.

  • Average 4.5/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

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  "maintainers": [
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  ]
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    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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true and openWorldHint=true, so the tool is known safe and possibly incomplete. The description adds value by listing the specific fields returned (id, author, time, started, comment), which is not in annotations. No contradictions.

    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 two sentences with no extra words. The first sentence states the action and output, the second provides usage context. It is front-loaded and efficient.

    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 the tool's simplicity (one required string parameter, read-only list operation), the description covers purpose, output fields, and usage guidance. Annotations cover safety and open-world behavior. No output schema is needed as fields are listed. Complete for the 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 sole parameter 'issue_key' is fully described in the input schema (100% coverage), so the description does not need to add meaning. The tool description does not elaborate on the parameter beyond the schema, but 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 it lists worklogs on a Jira issue and enumerates the fields returned (id, author, time, started, comment). The verb 'list' and resource 'worklogs on a Jira issue' are specific. The description also distinguishes from the sibling 'add_worklog' by implying usage for verification before adding.

    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 'Useful to verify what was logged or to check for duplicates before adding more,' providing clear context for when to use this tool. It implies a read-only nature and suggests using it before 'add_worklog'. No explicit exclusions, but adequate given the simple tool set.

    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 openWorldHint=true, indicating a safe, unrestricted read operation. The description adds value by specifying the returned fields (display name, accountId, email), going 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.

    Conciseness5/5

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

    The description is extremely concise (two sentences) and front-loaded: first sentence explains what the tool returns, second states its usage. No extraneous words.

    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 no parameters, no output schema, and clear annotations, the description fully covers the tool's behavior. It explains the return value (listing three fields) and the use case (credential confirmation). Everything needed for agent selection and invocation is present.

    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 no parameters, and schema description coverage is 100% (trivially). Per guidelines, baseline is 4. No additional parameter information is needed, and the description does not add anything about parameters, which is acceptable.

    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 returns the authenticated Jira user's display name, accountId, and email. The verb 'Return' is specific, and the resource 'authenticated Jira user' is distinct from sibling tools (add_worklog, list_worklogs), which deal with worklogs.

    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 to confirm credentials,' providing a clear usage context. However, it does not mention when not to use the tool or explicitly compare to alternatives, though siblings are clearly different in purpose.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    Annotations indicate mutation (readOnlyHint=false, destructiveHint=false). The description adds behavioral details: Jira's minute granularity (minimum 60s), default for 'started', and normalization to Jira format, which go 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.

    Conciseness5/5

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

    Three concise sentences with front-loaded purpose. Every sentence adds value without redundancy.

    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?

    The description is thorough for a simple mutation tool, but lacks information about return value or confirmation (no output schema). However, annotations and schema cover safety and parameters well.

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

    Parameters5/5

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

    Schema coverage is 100%, and the description enriches parameter meaning: explains issue_key format, time_spent_seconds as duration, started default and normalization, comment as optional. Adds constraints (minimum 60s) not in schema.

    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 action (log work via POST) and resource (Jira issue), with specific parameter examples. It is distinct from sibling tools 'list_worklogs' (read) and 'whoami' (user info).

    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 provides clear context for use (logging work on an issue), but does not explicitly mention when not to use it or suggest alternatives, leaving some gap in guidance.

    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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