HP ALM MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes (defects, requirements, test cases, runs, etc.), but some overlap exists between general search and specific lookups, and between single and bulk creation. Descriptions are clear enough to resolve most ambiguities.
Naming Consistency5/5
All tools follow a consistent 'alm_verb_noun' pattern, e.g., 'alm_create_defect', 'alm_get_test_case'. No mixing of conventions, making the set predictable.
Tool Count3/5
34 tools is on the high side for a typical MCP server. While ALM is complex and many operations are covered, the count feels slightly excessive, approaching the upper bound of what is reasonable.
Completeness3/5
Covers core CRUD for defects, test cases, runs, and requirements, but lacks delete operations entirely. Update is missing for requirements, and there is no tool to get full test set details. Several notable gaps exist.
Average 4.1/5 across 34 of 34 tools scored. Lowest: 3.4/5.
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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 only mentions it creates a manual test run record and lists parameters. It does not disclose side effects, permissions required, or potential conflicts (e.g., whether a run already exists for the instance).
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 structured with an Args block and concise parameter explanations. It is not overly long but could be slightly more compact by removing the 'Args:' header if not necessary.
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 creation tool with no annotations and no output schema, the description covers parameters and return value adequately. However, it lacks details on error conditions, prerequisites (e.g., must test instance exist), or relationship to other tools.
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?
Schema description coverage is 0%, so the description adds value by explaining each parameter's meaning. For example, it clarifies that status can be 'Not Completed', 'Passed', etc., and that peer_reviewer maps to a specific field.
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 'Create a manual test run record for an existing test instance.' This is a specific verb+resource combination that distinguishes it from sibling tools like alm_execute_test or alm_update_run_status.
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 explicit guidance on when to use this tool versus alternatives (e.g., alm_execute_test for executing runs, alm_update_run_status for updating). The description only explains what it does, not the context of use.
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?
No annotations are provided, and the description does not disclose behavioral traits such as whether the operation is read-only, requires authentication, or has rate limits. It only states what the tool does, not its side effects or constraints.
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 a one-line summary and structured Args/Returns sections. It is front-loaded with the key action. However, including the full return format adds a bit of length but is justified by the lack of output schema.
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?
The description covers the basic purpose and return structure, but lacks details on pagination, error handling, or limits. For a simple list operation, it is adequate but not fully comprehensive.
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 description adds meaningful context to the folder_id parameter by specifying it is an 'ALM numeric ID of the test plan folder', which goes beyond the schema's type 'string'. This clarifies the expected format and scope.
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 explicitly states the verb 'list', the resource 'test cases', and the scope 'inside a Test Plan folder'. This clearly distinguishes it from sibling tools like alm_create_test_case or alm_find_test_by_name.
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. The description does not mention when not to use it or any prerequisites, leaving the agent to infer from the sibling list alone.
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?
No annotations exist, so the description must disclose behavioral traits. It mentions the return format but does not explain side effects (e.g., locking, concurrency, what happens if already checked out). Critical behavioral context is missing.
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 (4 sentences), front-loads the purpose, and includes a structured Args/Returns section. No wasted words.
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 checkout tool with one parameter and no output schema, the description covers the basic purpose and return. However, it lacks context about the lifecycle (e.g., need to check in later, error handling) and does not leverage sibling tools to clarify usage.
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?
Schema coverage is 0%, and the description adds meaning by explaining the parameter: 'test_id: ALM ID of the test case.' This goes beyond the schema's minimal label. However, it does not specify format or constraints.
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 ('check out'), the resource ('a test case'), and the purpose ('so its fields and design steps can be edited'). It is specific and distinguishes from sibling tools like 'alm_checkin_test' by implication.
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 (e.g., when to check out vs. directly edit), no prerequisites, and no conditions for use. It simply states what it does.
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?
No annotations provided, and the description only covers the return format and parameter. It does not disclose whether the operation is read-only, potential error conditions, or any side effects. The description carries the full burden but discloses minimal 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences plus clearly formatted Args and Returns. Every part adds value, and the main action is front-loaded. No 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?
For a simple lookup tool with one parameter and no output schema, the description explains the input constraint and return structure adequately. However, it could mention whether the name is case-sensitive or if multiple test sets can share the same name, which would enhance completeness.
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?
The single parameter test_set_name is described as 'Exact name', adding critical constraint that the schema (which only has title and type) did not provide. This fully compensates for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds a test set by exact name and returns its ID. The verb 'Find' and resource 'test set in Test Lab' are specific. However, it does not explicitly differentiate from similar sibling tools like alm_find_test_by_name, leaving room for confusion.
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 on when to use this tool versus alternatives. There is no mention of prerequisites, when not to use it, or how it compares to other search tools in the sibling list.
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?
No annotations exist, so description carries all burden. It fails to mention authentication, error handling (e.g., invalid ID), or response behavior. Only basic output format is given.
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?
Description is concise with clear Args and Returns sections. No unnecessary words, but could be slightly more structured for readability.
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?
Given simple tool (1 param, no output schema), description covers purpose, parameter, and return format. Missing error details but adequate for typical use.
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 is 0%, so description must add meaning. It adds 'ALM numeric ID', which clarifies the type despite schema saying 'string'. Not exhaustive but helpful.
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 retrieves full details of a test case by ID, including all ALM fields. This distinguishes it from siblings like alm_create_test_case or alm_find_test_by_name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly guide when to use this tool over alternatives. It only implies use for retrieval by ID, but lacks context like 'use when you have the ID' or contrast with list tools.
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?
No annotations provided. The description lacks behavioral traits such as idempotency, permissions, or side effects. It only describes the action without disclosing any operational impacts.
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?
Structured with Args and Returns sections, front-loaded with purpose. Each sentence adds value, but could omit some redundancy (e.g., 'name: Requirement name / title' is fine).
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?
No output schema, but description provides return format. Covers all parameters. Lacks error handling details, but acceptable for a straightforward creation tool.
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?
Schema coverage is 0%, but description adds meaning to all 4 parameters: name is 'requirement name / title', req_type shows examples, description is clear, extra_fields is an optional dict. This compensates for missing schema descriptions.
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 'Create a new requirement in the ALM project' with specific parameters and return format. It distinguishes from sibling tools like alm_get_requirement (read) and alm_update_defect (different entity).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when or when-not to use. For a creation tool, usage is implied but no mention of prerequisites, error handling, or alternatives.
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?
The description discloses that the tool creates missing folders, indicating side effects (mutation). However, it does not mention authentication requirements, rate limits, or failure behaviors (e.g., invalid path permissions). Since no annotations are provided, the description partially covers behavioral aspects but lacks depth.
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 short and includes a structured Args/Returns format. It is front-loaded with the main action and includes essential detail. No superfluous sentences, though the Returns section could be omitted if output schema exists.
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?
For a simple tool with one parameter and no output schema, the description covers the core behavior and return value. It lacks edge-case handling (e.g., if the folder already exists) but is adequate for typical use. Given the tool's low complexity, completeness is sufficient.
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 only parameter 'folder_path' is described with format ('/ separated path') and an example ('Automation/Sprint1'), adding meaning beyond the input schema (which has 0% description coverage). This helps an agent understand the expected input format, though it could clarify root folder or allowed characters.
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 explicitly states 'Ensure a Test Lab (test-set) folder path exists, creating any missing folders.', which clearly identifies the verb ('ensure existing/create missing') and resource ('Test Lab folder path'). It distinguishes from sibling 'alm_ensure_test_plan_folder' by specifying 'test lab' versus 'test plan'.
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 (e.g., 'alm_create_test_set'). It does not mention prerequisites, pitfalls, or exclusions. An agent would not know when to prefer this over sibling tools.
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?
Description discloses the side effect of creating missing folders and returns the folder ID and path. However, it does not clarify behavior when the folder already exists (e.g., idempotent), nor mention permissions or errors. With no annotations, this is acceptable but not thorough.
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?
Very concise: two lines plus a returns block. Every sentence serves a purpose. The main action is front-loaded, and the parameter description is efficient.
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?
Given the tool's simplicity (one param, no output schema), the description adequately covers purpose, parameter format, and return structure. It could mention idempotency or error handling, but for an 'ensure' tool, the basics are sufficient.
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 Args section adds meaning beyond the input schema by describing the format ('/' separated path) and providing an example ('AppName/Sprint1/Regression'). Since schema coverage is 0%, this provides needed context for the single parameter.
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 states specific verb 'Ensure' and resource 'Test Plan folder path', and explains that it creates missing folders. This clearly distinguishes it from sibling tools like 'alm_ensure_test_lab_folder' which operates on a different context.
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 explicit guidance on when to use this tool vs alternatives like 'alm_ensure_test_lab_folder' or other folder-related tools. The description implies a use case for ensuring a path exists, but lacks conditions or exclusions.
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 are provided, so the description carries the burden. It discloses that results are limited by page_size and returns a JSON with defects and count, but does not mention pagination behavior, error handling, or read-only nature.
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 well-structured with Args and Returns sections, and examples are helpful. It is concise but could be slightly more compact without losing clarity.
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?
Given the absence of output schema, the description provides the return format. It adequately covers the tool's behavior for listing defects, though it lacks details on error responses and project context.
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?
The input schema has 0% description coverage, but the description compensates fully with detailed parameter info, including default values and multiple HPQL examples. This adds significant meaning beyond the 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 'List defects from the project with optional HPQL filter,' specifying the verb (list), resource (defects), and scope (from the project). This distinguishes it from sibling tools like alm_create_defect, alm_get_defect, and alm_update_defect.
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 such as alm_get_defect for a single defect or alm_search for general queries. No when-to-use or when-not-to-use context is given.
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?
The description includes the return format, showing the structure of the response (found, test_id, test_name). However, it does not disclose behavioral traits like idempotency, error handling, authentication requirements, or rate limits. Without annotations, the description carries the full burden, but it provides the basic output behavior.
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, using a single sentence for purpose and structured sections for Args and Returns. Every element adds value without redundancy, fitting within a minimal and clear format.
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 (single parameter, no output schema), the description is fully sufficient. It covers the purpose, parameter meaning, and return structure. No additional context is necessary for an AI agent to use this tool correctly in most scenarios.
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 description explains that test_name is the 'Exact name of the test case in ALM', adding specificity beyond the schema's type-only definition. This conveys that an exact match is required and ties the parameter to the ALM context. However, it could be more precise about case sensitivity or whitespace handling.
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 'Look up a test case ID by its exact name', specifying the verb (look up) and resource (test case ID by name). This distinguishes it from sibling tools like alm_get_test_case (by ID) and alm_list_test_cases (list all).
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 does not provide guidance on when to use this tool versus alternatives, such as alm_get_test_case or alm_find_test_instance. It only implies usage when the exact name is known, but no explicit when-to-use or when-not-to-use conditions are given.
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 are provided, so the description carries full burden. It mentions mutation ('check in') and provides return format, but does not disclose details like requiring prior checkout, versioning behavior, or side effects.
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 very concise, consisting of two sentences plus structured Args/Returns. It is front-loaded with purpose and contains no superfluous information.
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?
Given the simple nature (one parameter, no output schema), the description covers the purpose, argument, and return value. However, it could elaborate on the meaning of 'vc_status' and any required preconditions.
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?
Schema coverage is 0% since the schema only provides a title and type. The description adds meaning by specifying 'ALM ID of the test case', which clarifies the parameter's purpose beyond the 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 ('check in'), the resource ('test case'), and the context ('after editing to save a new version'). It effectively distinguishes from sibling tools like alm_checkout_test and alm_update_test_case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage after editing but does not explicitly state when to use this tool versus alternatives like alm_checkout_test or alm_update_test_case. Lacks guidance on prerequisites or exclusions.
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?
No annotations are provided, so the description carries the full burden. It discloses the return format but lacks details on side effects, permissions needed, error conditions, or idempotency. For a mutation tool, more behavioral context is expected.
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 concise with a clear structure: a single sentence stating the purpose, followed by Args and Returns sections. Every sentence adds value, and the key information is front-loaded.
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?
For a simple creation tool with two parameters, the description covers the essential aspects: what it does, required parameters with guidance, and return format. It lacks error handling or constraints, but given the tool's simplicity, it is largely complete.
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 input schema has 0% description coverage, but the tool description adds meaningful context: parent_folder_id is linked to alm_ensure_test_lab_folder, and test_set_name is described as 'Name for the new test set'. This compensates for the schema's lack of parameter descriptions.
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 'Create a new test set' and specifies the location 'inside a Test Lab folder'. It uses a specific verb-resource pair that distinguishes it from sibling tools like alm_find_test_set (find) and alm_add_test_to_set (add to existing set).
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 by instructing to use alm_ensure_test_lab_folder to obtain the parent_folder_id. It implies when to use this tool (after ensuring a folder exists) but does not explicitly state when not to use it or list alternatives.
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, description carries burden. It describes the return format but does not disclose if read-only, permissions needed, error handling (e.g., not found), or rate limits. Adequate but could add more.
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?
Very concise, no wasted words. Front-loaded with purpose, followed by clear parameter and return specification. Efficient and scannable.
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?
Given no output schema, description includes return structure (defect_id and fields). Still missing error scenarios or read-only hint, but complete enough for a simple get operation.
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?
Schema provides no description for defect_id. Description adds 'ALM numeric ID', clarifying the format and system context. Adds meaningful value beyond the 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?
Clearly states 'Get the full details of a defect by its ID'. The verb 'Get' and resource 'defect' are specific. Differentiates from siblings like alm_list_defects (list) and alm_update_defect (update).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly indicates when to use (when you have a defect ID), but no explicit when-not-to-use or alternatives. Could mention that for listing defects use alm_list_defects or for searching use alm_search.
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 exist, so description carries full burden. It discloses the return format but lacks other behavioral traits such as read-only nature, error handling, or authentication requirements.
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?
Very concise: two sentences plus Args/Returns. Front-loads the purpose and avoids wasted words.
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?
For a simple get operation with one parameter, the description covers purpose, param, and return format. However, it could explain 'full details' more or mention that fields are dynamic.
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 description adds meaning to the sole parameter 'req_id' by specifying it as 'ALM numeric ID of the requirement,' which is beyond the schema's minimal type and title.
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 states 'Get the full details of a requirement by ID,' which is a specific verb+resource. It clearly distinguishes from siblings like alm_list_requirements (list) and alm_create_requirement (create).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when needing full details of a single requirement by numeric ID, but it does not provide explicit guidance on when to use this vs alternatives like alm_list_requirements or alm_get_defect.
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?
Describes return format but no side effects, permissions, rate limits, or error conditions. With no annotations, description carries full burden but lacks depth.
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?
Extremely concise: two sentences for purpose, then Args and Returns. No redundant text.
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?
Covers input and output format. Lacks details on null return meaning and prerequisites (e.g., test must exist). Adequate for simple tool.
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?
Schema coverage is 0%, but description defines test_id and its purpose in Args section, adding meaning beyond schema title and type.
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?
Clearly states it gets the version control status of a test case with specific possible values (Checked_In or Checked_Out). Distinguishes from sibling tools like checkin/checkout.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage to check checkin/checkout status but provides no explicit when-to-use or when-not-to-use guidance, nor alternatives.
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 are provided, so the description carries full burden. It implies a read operation by stating 'list', and specifies the return structure (id, name, status). However, it does not disclose any behavioral traits like authentication requirements, side effects, or pagination behavior, which would be expected for a list operation.
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 with no wasted words. It includes an Args and Returns section with a clear JSON structure, making it easy to understand quickly.
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 (1 parameter, no nested objects, no output schema), the description is complete. It specifies the parameter meaning and the exact return format, which is sufficient for correct invocation.
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?
The sole parameter test_set_id is described as 'ID of the test set', which adds meaning beyond the schema's title alone. Given that schema description coverage is 0%, the description compensates fully by providing clear semantics.
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 all test instances inside a test set, with a specific verb and resource. It distinguishes from sibling tools like alm_find_test_instance (find specific) and alm_list_test_cases (different entity).
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 does not provide guidance on when to use this tool vs alternatives, nor any exclusions or prerequisites. It simply states what it does without contextual usage advice.
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 must fully disclose behavior. It describes the action and return values but lacks details on side effects (e.g., whether it moves or copies the test case), permissions, error conditions, or state changes. Some traits are inferred but not explicit.
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 concise and front-loaded, starting with the core action. It then provides clear Args and Returns sections. Every sentence adds value with no 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?
Given the tool has 3 parameters, no output schema, and no annotations, the description covers the purpose, parameters, and return values adequately. However, it lacks some behavioral context (e.g., error handling, confirmation of action) that would enhance completeness.
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 description coverage is 0%, so the description fully compensates. It explains each parameter: test_set_id (target test set), test_id (test case in Test Plan), and status (initial run status with options 'No Run', 'Passed', 'Failed', 'Blocked' and default 'No Run'). It also describes the return format.
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 ('Pull (add) a test case from Test Plan into a Test Lab test set') and explicitly mentions it is the 'Move TC from Test Plan to Test Lab' operation. The verb 'add' and resource 'test case to test set' are specific and distinguish from siblings like alm_create_test_set or alm_execute_test.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (to add a test case to a test set) but does not provide explicit guidance on when to use this tool versus alternatives. There is no mention of prerequisites, nor when not to use it.
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 burden. It discloses automatic check-out/check-in and return format, but omits side effects like permissions, folder state modifications, or error handling. Partial but not full transparency.
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 brief with two paragraphs, front-loaded purpose, and clear args/returns. Every sentence adds value with no 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?
Parameters and return format are covered. Missing context like required domain/project scope or folder existence prerequisites. Still mostly complete for a creation tool.
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 description coverage is 0%, but the description fully explains all three parameters: folder_id (ID of parent folder), test_name (name), and steps (optional list of dicts with name/description/expected). Adds structure and meaning beyond 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 verb 'create' and resource 'manual test case in a Test Plan folder', with optional steps. It distinguishes from siblings like alm_bulk_create_test_cases or alm_create_defect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use by mentioning automatic check-out/check-in with steps, but lacks explicit exclusions or alternatives like bulk creation tools. Guidance is implied but not comprehensive.
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 provides the return format and notes partial match support. However, it lacks details on whether partial matches return multiple results, the exact matching behavior, or error handling. It does not disclose authorization or destructive potential (likely read-only).
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 concise, front-loaded with the main purpose, and structured with separate lines for args and returns. Every sentence adds value with no 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?
Given the low complexity (2 params, no output schema), the description covers the purpose, parameters, and return format adequately. Minor gaps include not specifying behavior when multiple partial matches exist or if the test set ID is invalid, but overall it is sufficient.
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 0%, but the description adds full meaning for both parameters: test_set_id explained as ID of the test set and test_case_name as name with partial match info. This compensates entirely for the schema's lack of descriptions.
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 finds a test instance inside a test set by test case name with partial match support. It distinguishes from siblings like alm_find_test_by_name (finds test case) and alm_find_test_set (finds test set) by specifying the scope (inside a test set) and the returned entity (test instance).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a test set ID and test case name, but does not explicitly state when to use this tool versus alternatives like alm_list_test_instances. No guidance on when not to use or exclusions.
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 are provided, so the description must fully disclose behavioral traits. It indicates a write operation ('update') and specifies allowed status values. However, it does not mention potential side effects, permissions required, or behavior on invalid input. The return format {updated: true} adds transparency, but more detail on edge cases would be beneficial.
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 concise and well-structured, using clear Args/Returns sections. Every sentence adds value, and the most important information (the action and parameters) is front-loaded. There is no unnecessary verbiage.
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?
Given the simplicity of the tool (2 parameters, no output schema), the description is fairly complete. It covers the action, allowed parameter values, and the return format. However, it lacks information about possible error conditions, prerequisite states (e.g., existence of test run), or edge cases, which slightly reduces completeness.
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?
The input schema only provides type information (string) for the two parameters, with 0% schema description coverage. The description adds crucial semantics: test_run_id is identified as the ALM ID, and status is given explicit allowed values ('Passed', 'Failed', 'Not Completed', 'Blocked'). This fully compensates for the schema's lack of descriptions.
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's purpose: updating the overall pass/fail status of a test run. It uses a specific verb ('Update') and resource ('status of a test run'), and it is distinct from sibling tools that update different entities (e.g., alm_update_defect, alm_update_test_case) or perform other actions on test runs (e.g., alm_create_test_run).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used to change the status of a test run, but it does not explicitly state when to use it versus alternatives, nor does it mention any prerequisites or exclusions. There is no guidance on when not to use this tool, which leaves some ambiguity.
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 are provided, so the description carries the burden. It discloses sorting and return structure but omits potential side effects, permissions, or limitations. The behavioral traits are adequately but not exhaustively covered.
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 concise with just three sentences. It front-loads the purpose, then provides usage guidance, and ends with parameter and return details. No wasted words.
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?
For a simple one-parameter tool, the description covers the return structure and usage context. It lacks details on errors or permissions but is sufficient given the tool's simplicity and no output schema.
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 single parameter 'test_run_id' is described as 'ALM ID of the run', adding clear meaning beyond the schema's minimal typing. Since schema coverage is 0%, the description compensates well.
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 gets all run steps for a test run, sorted by step order. It uses a specific verb ('Get') and resource ('run steps'), and it distinguishes itself from sibling tools that add steps or execute tests.
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 advises using the returned step IDs to map evidence before calling alm_update_run_step or alm_attach_to_entity, providing clear context. It does not explicitly state when not to use, but the usage guidance is helpful.
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 the description must cover behavior. It states the return format but does not disclose permissions, side effects, or whether updates are immediate. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise with structured sections (common fields, Args, Returns). 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?
Covers main functionality, required parameters, and return format. Lacks details on error handling or prerequisites, but is sufficient for a simple update tool given no output schema or annotations.
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?
Schema has 0% description coverage. The description adds definitions for defect_id and fields with an example, providing useful context beyond the bare 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 it updates any field(s) on an existing defect, lists common fields, and is distinct from sibling tools like alm_create_defect or alm_update_run_status.
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 implies usage for modifying existing defects but does not explicitly state when to use it versus alternatives like alm_create_defect or alm_get_defect. Includes common fields but lacks when-not 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?
Describes auto_checkout behavior, return format, and field requirements, but does not discuss error scenarios, permission needs, or other side effects. With no annotations, this is adequate but not thorough.
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?
Brief and focused, listing common fields and args in a readable format. Could be slightly better organized but no superfluous content.
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?
Covers all parameters, return structure, and key behavioral details (auto-checkout). Missing error handling and permission context, but sufficient for a mutation tool with 3 params and no output schema.
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 description coverage is 0%, and description fully compensates by explaining test_id as ALM ID, fields as a dict with example, and auto_checkout default effect. Adds significant meaning beyond 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?
States 'Update any field(s) on an existing test case' with specific verb and resource, lists common fields, and clearly distinguishes from sibling read/create/checkout tools.
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?
Clearly explains when to use (updating an existing test case) and the prerequisite of checkout with automatic handling via auto_checkout. Lacks explicit when-not-to-use but context is sufficient.
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?
The description discloses the return format and the upload action, but no annotations exist to supplement. It omits details on permissions, file size limits, error handling, or side effects like overwriting existing attachments, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the purpose, and uses a structured format with bullet points for args and a return specification. Every sentence adds value, making it efficient for an AI agent to parse.
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?
Given the tool's complexity (multiple entity types) and lack of output schema, the description covers parameter details and return format well, and even references a sibling tool for run-step IDs. It misses error scenarios and idempotency, but overall is fairly complete.
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?
With 0% schema coverage, the description fully compensates by explaining each parameter: entity_type lists valid values with context, entity_id specifies numeric ALM ID and special case for run-steps, file_path requires absolute path with an example, significantly enhancing schema meaning.
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 verb 'Upload' and the resource 'local file as an attachment to any ALM entity', distinguishing it from sibling tools which are primarily CRUD or search operations.
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 specific guidance for each entity_type, including the relationship between run-step and run entities, and references alm_get_run_steps for obtaining IDs. However, it lacks explicit instructions on when not to use this tool or alternatives.
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?
The description discloses the steps involved (finds instance, creates run, updates status and steps), which is helpful. However, it does not mention idempotency, error handling, or whether a new run is always created. Given no annotations, more detail on behavioral traits would be beneficial.
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 concise, with a short intro, a bullet list of high-level steps, and structured Args and Returns sections. Every sentence adds value, and the format is front-loaded with the core 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?
The description covers the composite operation, all parameters, and return format. It positions the tool among siblings. Missing some context on prerequisites or error conditions, but given the tool's complexity and lack of output schema, it is reasonably complete.
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?
The description provides an Args section with clear explanations for each parameter, including status values and optional fields. Since the input schema has 0% description coverage, the description fully compensates by adding meaning beyond just names and types.
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 performs 'full end-to-end test execution' in a single call, listing specific steps. It distinguishes from sibling tools by emphasizing it is a composite operation, unlike more granular tools like alm_create_test_run or alm_update_run_status.
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 'This is the primary tool for recording automation results in ALM,' providing clear context for when to use it. While it does not list specific alternatives or when not to use it, the context is sufficient for an agent to understand its primary role.
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 are provided, so the description carries the full burden. It discloses the update behavior and return value but lacks details on side effects, permissions, idempotency, or error conditions. Adequate but not rich.
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?
Very concise: a one-line summary, followed by Args and Returns sections. Every sentence adds value, no fluff. Front-loaded with the core 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?
Covers inputs and return shape, but does not address error handling, prerequisites (e.g., run existence), or permissions. For a simple update tool, it is mostly complete. Could be improved with a note on when the update might fail.
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?
With 0% schema description coverage, the description compensates by explaining each parameter: test_run_id, run_step_id, status (with allowed values), and comments (actual result). It also notes the provenance of run_step_id. This adds significant semantic value beyond the schema titles.
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?
Clearly states 'Update the status and actual-result comment for a single run step,' specifying the verb, resource, and fields. This distinguishes it from siblings like alm_update_run_status (updates overall run status) and alm_get_run_steps (retrieves steps).
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 context by explaining that run_step_id comes from alm_get_run_steps and lists allowed status values. However, it does not explicitly state when to use this tool vs alternatives like alm_update_run_status, nor does it mention when not to use it.
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 are present, so the description bears full responsibility. It accurately describes the creation behavior, required fields, and output format, but omits details like authentication, permissions, or side effects (e.g., duplicate handling).
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 structured with Args and Returns sections, uses bullet points, and is front-loaded with the purpose. It is slightly verbose but still efficient.
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?
Given 5 parameters, no output schema, and no annotations, the description covers all inputs and output. It provides extra_field examples but could mention project/domain context.
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 0%, and the description adds significant value: it explains each parameter, provides valid values for severity/priority, and gives concrete examples for extra_fields, making the input schema fully interpretable.
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 'Create a new defect (bug) in the ALM project.' with a specific verb and resource, and it distinguishes from sibling tools like alm_update_defect, alm_list_defects, etc.
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 by listing parameters and return values, and the name implies creation vs. update. However, it does not explicitly state when not to use this tool (e.g., for updates), but the sibling context fills the gap.
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 are provided, so the description carries full burden. It discloses automatic folder creation and the return format (folder_id, created, failed). It does not mention permissions, reversibility, or error handling, but covers key 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: a one-sentence purpose, a note on automatic folder creation, then structured Args and Returns sections. Every sentence adds value with no 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?
Given no output schema, the description includes return format. Both parameters are thoroughly explained. The tool has moderate complexity (bulk creation with folder path), and the description covers essential aspects. Could mention max batch size or failure handling for full completeness.
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 description coverage is 0%, meaning the schema itself provides no parameter details. The description compensates fully by explaining folder_path with an example and test_cases with a detailed structure and example, adding significant value beyond the raw 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 'Create multiple test cases with design steps under a folder path in one call.' It uses specific verbs and resources, and is distinct from sibling tools like alm_create_test_case (single) and other entities.
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 implicitly indicates bulk creation versus single, and mentions automatic folder creation. However, it does not explicitly state when to use this tool instead of alternatives, nor does it provide when-not conditions or prerequisites.
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 burden. It discloses the scope ('accessible to the current user') and the return format. No side effects or authentication details are provided, but the behavior is sufficiently transparent for a read-only listing tool.
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, front-loaded with the main action. No extraneous information. Every sentence contributes to understanding.
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?
Despite no output schema, the description provides the return structure. It fully covers the tool's purpose, usage, and output for a simple zero-parameter tool.
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 zero parameters, so the baseline is 4. The description adds value by showing the return format, which compensates for the lack of an output 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 tool lists all ALM domains and projects accessible to the current user. It uses a specific verb (list) and resource (domains and projects), and is distinct from sibling tools that focus on other operations.
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 to use this to discover available domains and project names, providing clear guidance. No alternatives are mentioned, but the tool's unique purpose within the sibling set makes that acceptable.
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 the full burden. It discloses the return format and pagination behavior, indicating a read-only operation. It does not mention error handling or invalid query behavior, but overall it provides sufficient transparency for a list tool.
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 concise with a front-loaded main purpose, followed by clear Args and Returns sections. Every sentence adds value, and the format aids quick comprehension.
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?
For a simple list tool with 2 parameters and no output schema, the description covers purpose, parameters with examples, and return format. Minor omissions: no error handling details, but overall it is sufficiently complete.
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 description coverage is 0%, so the description must compensate. It does so effectively by explaining that query is an HPQL filter string with concrete examples, and that page_size controls maximum results with a default. This adds meaningful context beyond the 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 explicitly states 'List requirements from the project with optional HPQL filter.' It uses a specific verb ('list') and resource ('requirements'), clearly distinguishing from sibling tools like alm_get_requirement (single item) or alm_list_defects (different resource).
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 guidance on the query parameter with examples (e.g., 'status[Not Covered]') and explains behavior when left empty. It defines page_size default. However, it does not explicitly state when to use this tool versus alternatives like alm_search, which is a minor gap.
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 are provided, so the description carries the full burden. It discloses the heartbeat-first-then-reconnect strategy and the return format. This is sufficient for a session management tool, though it could mention if any state is cleared.
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 with three well-structured sentences. It front-loads the purpose, gives usage guidance, and shows the return value. No unnecessary 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 the tool has no parameters, no output schema, and no annotations, the description is fully complete. It covers purpose, usage context, behavior, and return format adequately.
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?
There are no parameters. With zero parameters and 100% schema coverage, the description correctly provides no additional parameter information. Baseline score of 4 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 refreshes or reconnects the ALM session, using specific verbs 'refresh' and 'reconnect' with a clear resource. It distinguishes itself from sibling tools that perform CRUD operations on entities.
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?
Explicitly says 'Use this if other tools start returning authentication or session errors,' providing clear context when to use. However, it does not mention when not to use it or alternative approaches.
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 provided, the description carries full behavioral disclosure. It explains the prerequisite (checked out state), the steps format with an example, the delete_existing behavior, and the return value. It does not mention potential side effects like version updates or permission requirements, but the core mutation behavior is clearly described.
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 concise, well-structured, and front-loaded. It opens with the core purpose and precondition, then details parameters with example, and concludes with return value. Every sentence earns its place with no redundant information.
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 moderate complexity (3 parameters, no nested objects, no output schema), the description covers all necessary aspects: precondition, parameter semantics with example, behavioral options, and return structure. It is fully complete for an agent to select and invoke this tool correctly.
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 0%, so the description must compensate. It does so excellently: test_id is explained as 'ALM ID' with state requirement, steps are described as dicts with required keys and an example, and delete_existing has explicit behavior. This adds significant value beyond the schema's type-only definitions.
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 verb 'add' and resource 'design steps to a test case', with a crucial precondition ('test must already be checked out'). This distinguishes it from siblings like 'alm_create_test_case' which creates a test case from scratch, and 'alm_update_test_case' which modifies general test case fields.
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 on when to use: the test must be checked out first. It explains the delete_existing option. However, it does not explicitly mention when not to use this tool (e.g., when the test is in another state) or suggest alternative tools like alm_update_test_case for non-step modifications.
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 are provided, but the description details the return format (found, test_config_id, test_id), which gives transparency about the output. It does not disclose potential side effects or permissions, but as a read operation, it is likely safe.
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 concise with a single purpose sentence and a clear Args/Returns section. It is front-loaded and contains no unnecessary information.
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?
For a simple tool with one parameter and no output schema, the description provides the return structure explicitly, making it complete for an agent to use effectively.
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?
The only parameter, test_id, is described as 'ALM ID of the test case', which adds meaningful context beyond the schema's simple type information. Since schema coverage is 0%, the description fully compensates.
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 'Get the test configuration ID for a test case' and explains its necessity for creating test runs. This distinguishes it from sibling tools like alm_create_test_run or alm_get_test_case.
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 indicates the tool is 'required internally to create test runs', providing clear context for when to use it. However, it does not explicitly state when not to use it or mention alternative tools for similar purposes.
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?
Despite no annotations, the description discloses return format, query limitations (HTTP 500), and valid fields for tests. It would benefit from mentioning read-only nature, but overall transparent for a search tool.
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?
Well-structured with sections, front-loaded purpose, and necessary details. Slightly long but justified due to complexity and need for examples. Could tighten some example text slightly.
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 31+ sibling tools, description clearly positions itself for advanced queries. Covers all parameters, return format, and known limitations. No output schema but return structure described. Sufficient for agent to understand tool's role.
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 0%, but description provides extensive semantics: entity_type with examples, query with detailed examples and notes on syntax and limitations, fields with examples and valid field list, page_size with default. This fully compensates for schema's lack of descriptions.
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 performs HPQL search across any ALM entity collection, with the verb 'search' and resource 'any ALM entity collection'. It distinguishes itself from sibling specific tools by targeting advanced queries not covered elsewhere.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use this for advanced queries not covered by the specific tools above', providing clear when-to-use guidance. Includes caveats like HTTP 500 for multiple OR values and recommends querying each folder separately, offering when-not-to and alternatives.
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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