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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool verifies configuration and connectivity, implying a read-only diagnostic operation, but doesn't specify what happens if verification fails, whether it requires authentication, or what the output looks like. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single, clear sentence: 'Verify Jira MCP server configuration and connectivity.' It is front-loaded with the core purpose, has no unnecessary words, and efficiently conveys the essential information without waste, making it highly concise and well-structured.

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

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

Given the tool's diagnostic nature and lack of annotations or output schema, the description is incomplete. It doesn't explain what the verification entails, what results to expect (e.g., success/failure indicators), or how it interacts with the Jira system. For a tool that likely returns status information, more context is needed to guide the agent effectively.

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

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

The input schema has 100% description coverage, with the parameter 'siteName' documented as 'Optional: Specify your Atlassian site name.' The description doesn't add any meaning beyond this, such as explaining when to use the parameter or its impact on the verification process. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.

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

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

The description clearly states the tool's purpose: 'Verify Jira MCP server configuration and connectivity.' It specifies the verb ('verify') and the target ('Jira MCP server configuration and connectivity'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like jira_get or jira_post, which might also involve connectivity checks indirectly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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. It doesn't mention prerequisites, such as whether it should be used before other operations, or contrast it with sibling tools like jira_get for data retrieval. The lack of usage context leaves the agent without clear direction on when this tool is appropriate.

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

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

Without annotations, the description carries the full burden. It discloses default output format (TOON), the option for JSON, the typical 204 No Content response, and includes endpoint examples. It does not mention the potential irreversible nature of deletions or authentication requirements, which would be useful for a destructive tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is well-structured with clear sections for output format and common operations. Though moderately long, every section is informative and non-redundant. The numbered list for operations enhances scannability, and the summary sentence is front-loaded.

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

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

The description covers output format, specific paths, query params, response status, and provides an API reference. Given that there is no output schema, this is sufficiently complete for a delete tool, which often returns 204 or minimal bodies.

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

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

The schema descriptions are already thorough (100% coverage), and the description adds significant value by giving exact endpoint templates and query parameters for each operation, such as '/rest/api/3/issue/{issueIdOrKey}' and 'deleteSubtasks=true'. This goes well beyond the raw schema and helps the agent construct valid requests.

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

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

The description clearly states 'Delete Jira resources' and lists specific operations (delete issue, comment, worklog, attachment, watcher), which distinguishes it from sibling tools like jira_get, jira_post, etc. The verb 'Delete' and resource examples make the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides concrete common operations with endpoint patterns, making it clear when to use this tool for various delete tasks. However, it does not explicitly contrast with alternatives or state when not to use it, though the name and sibling context make this largely implicit.

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

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

With no annotations provided, the description carries the full burden. It discloses the token-efficient TOON output format, the cost implications of unfiltered responses, and the schema discovery pattern. It does not mention error handling or authentication, but it provides substantial behavioral context beyond what the schema and annotations could offer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is long but well-structured with sections, bullet points, and examples. Every section serves a purpose: cost optimization, schema discovery, common paths, and examples. It is front-loaded with the main purpose and cost warning. No extraneous filler, though it could be trimmed slightly without losing value.

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

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

Given no annotations and no output schema, the description compensates by covering common paths, JQL examples, output format, and cost-saving techniques. It also links to the full API reference. It does not describe error responses or return structures, but for a read tool with this level of guidance, it is remarkably complete.

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

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

The schema already provides descriptions for all four parameters (100% coverage), so the baseline is 3. The description adds significant value by providing concrete examples for path, queryParams, and jq expressions, and by explaining the outputFormat enum. This goes beyond the schema to clarify usage and cost optimization.

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

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

The description opens with "Read any Jira data," which clearly identifies the tool as a read-only GET operation. It distinguishes from sibling tools like jira_post, jira_put, jira_patch, and jira_delete, which are all mutations. The scope is broad but well-defined as covering all Jira data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides strong guidance on how to use the tool efficiently (use jq, maxResults, schema discovery pattern) and even warns against deprecated paths. However, it does not explicitly state when to use this tool versus alternatives (e.g., the write siblings), though this is implied by the verb 'Read' and the sibling names.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the default output format (TOON), the option to switch to JSON, the cost-optimization with jq, and the partial-update semantics. It does not mention authentication, error handling, or rate limits, but the provided behavior is substantial for a generic REST client.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is well-structured with clear sections for introduction, cost optimization, output format, common operations, and a note on PATCH semantics. Despite its length, each sentence provides valuable information, and key details like partial update behavior and output format are front-loaded.

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

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

The description is thorough for a tool with five parameters, nested objects, and no output schema. It covers common use cases, parameter examples, and output behavior. It lacks details on error responses and authorization, but the provided information is sufficient for typical invocation and is complemented by an API reference link.

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

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

Although the schema already has 100% coverage, the description adds significant meaning beyond parameter names by providing concrete body examples for different update operations (issue, comment, worklog), path patterns, and jq usage for response filtering. This enriches the schema descriptions with practical usage context.

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

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

The description clearly states the tool performs partial updates to Jira resources, using the specific verb 'PATCH' and identifying the resource scope. It distinguishes itself from siblings by emphasizing partial updates that leave other fields unchanged, contrasting with jira_put for full updates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool, such as updating issue fields, comments, and worklogs. It explicitly notes that PATCH only updates specified fields, implying it's for partial modifications. However, it does not explicitly name alternatives like jira_put or jira_post for full updates or creation, though the sibling names make this inferable.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure and does so effectively. It explains the default TOON output format for token efficiency, provides cost optimization guidance about using the jq parameter, warns about expensive unfiltered responses, and notes complexity for attachments. It also references the API documentation for further details, though it doesn't explicitly mention authentication requirements or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is well-structured and appropriately sized. It starts with the core purpose, then provides critical cost optimization guidance, output format details, and concrete examples of common operations. Every section serves a clear purpose - the examples help users understand parameter usage, the warnings address practical concerns, and the API reference provides further resources. No sentence feels wasted or redundant.

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

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

For a complex 5-parameter tool with no annotations and no output schema, the description provides substantial context. It covers the tool's purpose, usage guidelines, behavioral aspects like output format and cost optimization, and parameter usage through examples. The main gap is the lack of explicit information about authentication requirements, error handling, or rate limits, which would be helpful given the tool's complexity and mutation capabilities.

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

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

With 100% schema description coverage, the baseline is 3, but the description adds significant value beyond the schema. It provides concrete examples for all parameters in the 'Common operations' section, showing how path, body, and jq work together for specific use cases. The cost optimization guidance around jq usage adds practical context that the schema alone doesn't provide, though it doesn't explain all 5 parameters individually.

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

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

The description clearly states the tool's purpose as 'Create Jira resources' with a specific verb ('Create') and resource type ('Jira resources'). It distinguishes from sibling tools like jira_get (read) and jira_delete (delete) by focusing on POST operations for creation. The opening sentence is direct and unambiguous about the tool's function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives. It specifies this is for 'Create Jira resources' operations, distinguishing it from jira_get (read), jira_delete (delete), jira_patch/jira_put (update). The 'Common operations' section gives concrete examples of appropriate use cases like creating issues, adding comments, worklogs, and transitions, making it clear when this tool is the right choice.

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

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

With no annotations, the description carries the full burden. It discloses that PUT replaces the entire resource (a key destructive behavior), explains the default TOON output format, and highlights the jq parameter for cost optimization. It does not mention permissions or failure modes, but the disclosed behaviors are substantial and useful.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is well-structured with bold headers, a clear one-sentence summary, and a numbered list of examples. It is slightly longer than minimal, but each section serves a purpose—clarifying output format, cost optimization, and typical endpoints—so it remains efficient.

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

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

The tool has no output schema and moderate complexity (5 params, nested body). The description covers key operational contexts: full-update semantics, default output format, and common use cases. It could mention idempotency or authentication, but HTTP PUT semantics and the API reference provide sufficient completeness for an agent to invoke it correctly.

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

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 adds value beyond the schema by providing concrete example paths and bodies for common operations, and by reinforcing the purpose of jq in reducing token costs. This goes beyond simple schema repetition.

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

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

The description opens with 'Replace Jira resources (full update)' which precisely states the action and scope. The note 'For partial updates, prefer PATCH' explicitly distinguishes it from the sibling jira_patch tool, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage guidance is explicit: it states when to use PUT (full replacement) and when not to (partial updates, prefer PATCH). It also provides common operation examples (update issue, update project, set property) and a cost-optimization tip with jq, which further clarifies appropriate usage.

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