jira-pm-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: setup_project for configuration, run_sprint_audit for comprehensive sprint audit, sync_sprint_tasks for blocking task synchronization, and search_issues as an ad-hoc JQL escape hatch. No two tools overlap in functionality.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern using snake_case (setup_project, run_sprint_audit, sync_sprint_tasks, search_issues), making the naming predictable and easy to understand.
Tool Count5/5With 4 tools, the server is well-scoped for its purpose as a PM monitoring assistant. Each tool serves a necessary function (setup, audit, sync, search), and the count fits comfortably within the typical 3-15 range.
Completeness4/5The tool set covers the core PM workflow: configuration, audit, task synchronization, and ad-hoc queries. However, it references a 'get_sprint_health' tool that is not provided, and lacks tools for direct issue creation or manual updates, leaving minor gaps.
Average 4.1/5 across 4 of 4 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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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?
With no annotations provided, the description discloses key behaviors: it modifies priorities (upgrades if lower), adds sprint labels, excludes tasks with 'SIT' or 'UAT', and supports resumable execution via a temp state file. This provides good transparency, though it could mention potential side effects 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long, each earning its place: purpose, action details, exclusion and resumability. It is front-loaded with the primary goal and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step sync, no output schema, no annotations), the description covers the main behaviors, exclusions, and resumable execution. It is mostly complete, though missing details on error handling or idempotency.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes both parameters with 100% coverage (sprint_name as fixVersion name, project_keys as JIRA project keys). The description adds overall context but does not provide additional parameter-level detail beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb and resource: 'Sync blocking task priorities and sprint labels to match user stories in a sprint.' It details the actions (upgrade priority, add label) and exclusions, making the purpose unambiguous and distinct from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the context (syncing tasks within a sprint) and what the tool does step-by-step, but it does not explicitly state when to use this tool versus alternatives like run_sprint_audit or search_issues. The usage is implied but not contrasted with siblings.
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?
With no annotations, the description carries full behavioral burden. It details the entire workflow: syncing labels, computing risk flags, updating due dates, calculating velocity if certain conditions are met, writing a receipt, and supporting a dry_run mode. This is comprehensive, though it could mention destructiveness or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences: the first defines the tool's core purpose, and the second enumerates specific actions. Every phrase contributes essential information, with no wasted words. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description covers the actions, it lacks details about return values (no output schema) and error conditions. Prerequisites like project setup are not mentioned, but the sibling tools list includes setup_project, hinting at dependencies. The description is adequate for a daily audit tool but leaves some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have full schema descriptions (100% coverage), so baseline is 3. The description adds value by explaining the effect of dry_run ('compute but do not write') and contextualizing fix_version as the sprint identifier. This enriches understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a daily PM audit for a sprint, listing specific actions like syncing labels, computing risk flags, updating due dates, calculating velocity, and writing an audit receipt. This specificity distinguishes it from sibling tools such as sync_sprint_tasks, which likely focus on task-level operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a daily usage context but does not explicitly state when to use this tool versus alternatives like sync_sprint_tasks or search_issues. No explicit exclusions or alternative recommendations are provided, though the 'daily PM audit' framing offers some guidance.
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 carries full burden. It discloses validation actions and config storage as a YAML comment. However, it does not describe error handling, side effects, or return behavior, leaving some 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences. The first sentence states the core purpose, the second details actions and requirements. No unnecessary information; front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters and no output schema, the description covers core behavior but omits return values and error scenarios. For a configuration tool, understanding success/failure conditions is important, so completeness is adequate but not outstanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining that parameters are validated and config is stored as a YAML comment, which goes beyond schema descriptions. This enhances understanding of how parameters are used.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Configure a project for PM Agent monitoring.' It details specific actions (validates root epic, board, fixVersions, write permissions) and distinguishes from sibling tools like run_sprint_audit by noting it is a setup prerequisite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Must be run before any audit tools,' providing clear when-to-use guidance. While it does not mention when-not-to-use or alternatives, the prerequisite nature is unambiguous.
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?
No annotations provided, so description carries full burden. It describes return behavior (a page of matching issues with specific fields) but doesn't disclose pagination details, side effects, or auth requirements beyond what schema implies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences only: first states purpose and output, second gives usage guidance. No wasted words, front-loaded with key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters and no output schema, the description covers purpose, return fields, and usage context. It lacks examples or JQL syntax guidance, but is sufficient for ad-hoc queries.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage with descriptions for all 4 parameters. The description adds no additional parameter details beyond the schema, meeting the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it's an 'Escape hatch for ad-hoc JQL queries,' specifying the verb (search) and resource (issues). It lists return fields and distinguishes from high-level tools like get_sprint_health and run_sprint_audit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use (ad-hoc JQL queries) and when not (use high-level tools for routine PM operations). Names two alternatives, providing clear context for selection.
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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