QualityMax Cloud

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the write nature is partially covered; the description adds that the write target is 'the framework.' It says nothing beyond that — no reversibility, no effect on already-accepted tests, no error behavior for an invalid pending_test_id.

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

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

Two short sentences, front-loaded with the action and resource, with the mode variant second. Zero filler, but also no structure beyond a single clause pair.

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

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

For a simple 3-parameter mutation tool with full schema coverage and annotations, the description is adequate but thin. It never explains where pending proposals come from or what state the project ends in after acceptance, which would orient an agent in this large 80+ tool surface.

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

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

Schema description coverage is 100%, so the schema already documents project_id, pending_test_id, and the context parameter (including its 15-25 word requirement). The description's 'single test or all' phrasing merely restates the pending_test_id semantics, adding no new meaning.

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

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

Clear specific verb + resource: 'Accept pending test proposals for a project, writing them into the framework.' An agent can tell this is a promotion/commit operation distinct from generation tools like generate_tests. It does not explicitly name any sibling as an alternative, so it lands just short of 5.

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

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

The second sentence clarifies the two modes (single vs. all), which is genuine usage guidance for choosing between calls. However, there is no statement of prerequisites (must pending proposals exist, must they be reviewed first) or of which sibling tool produces the pending tests in the first place.

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

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

Beyond the annotations (readOnly=false, destructive=false), the description discloses the key hidden side effect: the framework is created if it does not exist, and a test file is written to disk. It does not say what happens if the script is already in the framework (overwrite vs. duplicate) or what the call returns.

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

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

Two sentences, front-loaded with the action and immediately followed by the three-step mechanics. No filler and nothing repeated from the schema.

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

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

Annotations cover the safety profile and no output schema exists, so the description only needs to explain the mutation. It covers creation and file writing but leaves idempotency/overwrite semantics and failure conditions unstated for a write tool.

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

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

Schema coverage is 100%, so baseline is 3. The description adds light mapping ("fetches the script by ID" → script_id, "ensures the framework exists" → framework) but nothing on the framework fallback behavior or the required `context` analytics parameter beyond what the schema already documents.

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

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

Specific verb plus resource ("Add an existing automation script to the project's test framework") with the mechanism spelled out: fetch by ID, ensure framework, write file. It is distinct from generate_tests or update_script, though it never names a sibling to contrast against.

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

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

Usage is implied by "existing automation script" — the script must already exist, so this is not for generating code. There is no explicit when-not guidance and no pointer to alternatives such as generate_code_for_test_case or export_framework for the reverse direction.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds meaningful non-obvious behavior beyond that: the call is asynchronous, returns a job_id immediately on worker-backed deployments, and requires polling — context the agent could not infer from annotations alone.

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

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

Two sentences, zero waste. The core action is front-loaded and the async/polling behavior follows immediately, making the most operationally important detail easy to scan.

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

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

For a read-only, six-parameter tool with no output schema and fully documented parameters, the description covers what an agent needs to invoke and follow up. It does not describe the shape of the review output beyond 'suggest improvements', a minor gap given there is no output schema.

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

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

Schema description coverage is 100%, with all six parameters documented in the input schema including defaults and enum values for focus_areas. The description contributes no additional parameter meaning, so the baseline 3 is appropriate.

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

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

States a specific verb and resource ('Start an AI-powered code review') plus the outcome ('suggest improvements'), and explicitly distinguishes itself from the polling sibling check_ai_review_status. An agent can identify the tool's role without opening the schema.

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

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

Gives clear sequencing guidance: worker-backed deployments return a job_id and the agent should poll check_ai_review_status until completion. It names the alternative tool and the condition that selects it, though it does not state when this is preferable to other review-related siblings like repo_quality or analyze_coverage.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is clear. The description adds the focus on identifying gaps, but does not describe scope limits, output shape, or other behavioral details beyond the annotations.

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

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

The description is a single focused sentence with no wasted words. Its purpose is front-loaded and appropriately sized.

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

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

The tool is a simple read-only analysis operation with fully documented parameters and safety annotations. The description could mention return behavior or the include_suggestions option, but given the rich schema and annotations, it is largely complete for correct invocation.

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

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

Schema description coverage is 100%, so all three parameters are documented in the input schema. The description does not add meaning beyond the schema, which is acceptable at the baseline when the schema already carries the parameter semantics.

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

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

The description states a specific verb and resource: analyze test coverage and identify gaps in the repository. It is clear what the tool does, but it does not explicitly differentiate itself from the sibling repo_coverage tool.

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

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

There is no guidance on when to use this tool versus alternatives such as repo_coverage, repo_quality, or generate_gap_tests. The implied use case is coverage analysis, but the description provides no explicit conditions or exclusions.

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

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

Annotations already declare readOnlyHint=false, destructiveHint=false, openWorldHint=true, so safety framing is partly covered. The description adds real behavioral context the annotations cannot: the tool both mutates and executes, returns a compact result, and is intended to be looped. It does not state that the script is permanently modified or how a version conflict surfaces, which keeps it from a 5.

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

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

Three tight sentences: purpose first, then the alternative it replaces, then iteration guidance. Nothing is restated and no sentence is filler relative to the 11-parameter surface.

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

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

For an 11-parameter mutation-plus-execution tool with no output schema, the description covers what it does, when to choose it, and how to continue after failure. It omits what the compact result contains and any mention of the concurrency guard, but the 100% schema coverage absorbs most of that gap.

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

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

Schema description coverage is 100%, so every parameter including patch, expected_version, and include_logs is already documented inline. The description adds no parameter-level meaning beyond 'one compact result', leaving the schema to do all the work. Baseline 3 applies.

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

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

States a specific compound verb+resource (patch a script and re-run it) plus the return characteristic ('one compact result'). Distinguishes itself from the sibling pair update_script + run_tests by naming exactly what it replaces.

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

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

Explicitly names the alternatives ('instead of update_script followed by run_tests') and the condition that selects this tool ('collapses the fix-and-verify loop into a single round trip'). It also gives decision guidance for the iteration branch: iterate when the failure is a test defect, preserve assertions when it is an application defect.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered and the description's job is to add context beyond that. It adds the purpose of polling for completion and the expectation of informing the user, but says nothing about polling cadence, the possible status values, or whether a completed state carries the results payload.

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

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

Two short sentences, front-loaded with the operation and followed by the usage cue. No filler; it could only be improved by spending a few more words on state values or the results sibling.

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

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

There is no output schema, so the description is the only place an agent can learn what a status response contains or how completion is signalled, and it does not say. For a polling tool in a large sibling set with a required context parameter, the definition leaves the return-value and next-step story incomplete.

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

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

Schema description coverage is 100%, so both crawl_id and the mandated context parameter are fully documented in the schema, including the 15-25 word and third-person constraints. The description adds no semantics beyond that, which is the expected baseline when the schema does the work.

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

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

Specific verb+resource ("Check the status of an AI crawl job"), which is clear enough to act on. However, it never distinguishes itself from close siblings like get_ai_crawl_results, list_ai_crawl_jobs, or check_generation_status, so an agent must infer which one fetches actual results versus just polling state.

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

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

"Use this to see if a crawl has completed" gives an implied trigger, but there is no guidance on where crawl_id comes from (start_ai_crawl vs list_ai_crawl_jobs) nor on what to do after completion (get_ai_crawl_results). The when-to-use is present; the when-not-to-use and the next-step routing are absent.

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

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

Annotations already establish readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds useful context that this is a background/asynchronous review and that the result is returned "when complete," but it does not describe pending-state behavior, polling expectations, or failure handling.

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

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

The definition is a single front-loaded sentence with no filler. Every element (check, background AI review, started by ai_review, result when complete) contributes to correct tool selection or invocation.

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

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

For a simple read-only status tool with fully described parameters and safety annotations, the description provides enough context to call it correctly and understand that it relates to ai_review. It could be slightly more complete by clarifying what is returned while the job is still pending, but no output schema is present and the core workflow is clear.

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

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

Schema description coverage is 100%, and the job_id parameter is already documented as being returned by ai_review. The description adds little parameter meaning beyond reiterating the provenance of the job, so the baseline 3 for high schema coverage is appropriate.

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

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

The description states a specific verb ("Check") and resource ("a background AI review"), and explicitly ties it to the sibling tool ai_review by saying the review was "started by ai_review." This lets an agent distinguish it from the starter tool and from unrelated status-check siblings.

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

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

The phrase "started by ai_review" clearly indicates this should be used after ai_review has launched a background job, and the schema reinforces that job_id comes from ai_review. It does not explicitly name when not to use it or contrast with the other status-check siblings, so it stops short of a 5.

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

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

Beyond the read-only annotations, it explains that generation is asynchronous and may take minutes, why worker-backed deployments return a job_id, and that seat jobs must not be queued in parallel. These are material operational details for correct invocation and polling.

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

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

The description is four tight sentences with no filler. It front-loads the core purpose, then layers in polling behavior and the concurrency constraint in a logical order.

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

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

For a read-only status-checking tool with no output schema, it is complete enough to call correctly. It covers the upstream producer, polling expectations, terminal states, and the parallel-execution restriction.

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

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

Schema coverage is 100%, so the schema already documents job_id and the required context parameter. The description only adds the origin of job_id via generate_code_for_test_case, which is useful but does not substantially extend parameter semantics.

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

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

It states a specific verb and resource: checking a background code-generation job. It explicitly ties the tool to generate_code_for_test_case, so an agent can distinguish it from unrelated status-checking siblings such as check_ai_review_status or check_ai_crawl_status.

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

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

The description gives explicit triggering context: use after generate_code_for_test_case. It also specifies polling cadence (about every 30s), terminal conditions ('completed', 'failed', or 'timed_out'), and a concurrency caveat for seat jobs.

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

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

Beyond the annotations, the description discloses that generation may enqueue asynchronous jobs, that callers should poll check_generation_status, that import is non-atomic, and that partial imports skip generation. These are important behavioral traits for an agent invoking a mutating, open-world tool, though failure details and side effects on existing projects/scripts are not fully described.

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

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

The description is front-loaded with the core action and then adds only high-value operational details: async polling, non-deterministic model-assisted generation, and non-atomic import behavior. Each sentence contributes useful context without repetition.

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

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

For a complex conversion tool with no output schema, the description covers purpose, asynchronous behavior, status polling, and partial-import behavior adequately. It is slightly incomplete on the exact result/report structure and what is returned after generation is requested, but it gives enough context to invoke the tool correctly.

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

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

Schema description coverage is 100%, so the schema already documents source, context, and project_id, including that omitting project_id creates a new project. The description adds no additional parameter-level meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description states a specific verb and resource: persist QTML contracts and request model-assisted Playwright generation that persists automation scripts. It distinguishes the operation from deterministic compilation and a file-only editor action, but does not explicitly differentiate it from close siblings such as import_qtml or generate_code_for_test_case.

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

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

Usage is implied by the workflow description and the note that this is not deterministic compilation or a file-only editor action. However, it does not explicitly say when to choose this tool versus import_qtml, parse_qtml, validate_qtml, or generate_code_for_test_case.

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

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

Annotations already declare this is a non-destructive, closed-world write. The description adds genuinely useful behavior beyond that: the secret is auto-generated and shown only once, and the response includes provider-specific setup instructions. It omits what happens on a duplicate-provider conflict.

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

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

Four short sentences with zero filler; the create action, side effects, and the one-per-provider constraint are all front-loaded and each sentence carries distinct information.

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

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

With no output schema, the description responsibly covers return contents (secret shown once, URL, setup instructions). The only gap is unspecified behavior when a provider integration already exists, which matters given the stated uniqueness constraint.

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

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

Schema description coverage is 100%, so parameters are already documented by the schema. The description only adds the provider-default hint implicitly and says nothing extra about project_id or the context analytics parameter, so baseline 3 applies.

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

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

States a specific verb and resource ('Create a deploy-provider webhook integration for a project') and is clearly distinguishable from siblings like list_deploy_integrations, delete_deploy_integration, and test_deploy_integration.

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

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

The 'One integration per provider per project' line gives a prerequisite constraint, but the description never says when to choose this versus list/test/delete siblings, nor what to do if an integration already exists. Usage is implied rather than guided.

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

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=true, so the write and external-visibility profile is known. Beyond that the description adds nothing: no statement that a branch will be created or pushed, no mention of required permissions or repo state, no side-effect disclosure for an open-world mutation.

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

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

A single front-loaded sentence with no filler. It is efficient, though the brevity is partly a consequence of under-specification rather than disciplined concision.

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

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

For a six-parameter, open-world mutation with no output schema, an agent needs to know what is returned (PR URL or id) and whether prerequisites like generated tests or an existing branch matter. None of that is present, leaving the definition thin for the tool's complexity.

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

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

Schema description coverage is 100%, so every parameter including context, branch_name, and test_files is already documented in the schema; baseline 3 applies. The phrase 'generated tests or fixes' loosely corresponds to test_files and the default branch name, but the description supplies no additional semantics such as accepted file shapes or branch naming rules.

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

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

States a specific verb and resource (create a pull request) plus scope ('with generated tests or fixes'), which is enough to distinguish it from sibling write tools like accept_pending_tests or create_test_case that handle artifacts rather than PRs. It stops short of an explicit contrast with any alternative, keeping it below a 5.

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

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

There is no statement of when to call this rather than something else, no precondition such as tests having been generated or a branch existing, and no guidance on what happens after the PR is created. The agent must infer the entire workflow context.

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

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

Annotations only declare write/non-destructive/non-open-world; the description adds real behavior beyond them by disclosing that similar existing projects are checked and a confirmation prompt is raised. It still omits permissions needed, idempotency of name/key, and what happens if confirmation is declined.

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

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

Two compact sentences, front-loaded with the primary action and followed by the notable side effect. No filler or restated boilerplate.

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

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

For a mutation tool with six parameters and no output schema, the description covers the creation action and the confirmation behavior but says nothing about what is returned (project identifier) or what the context parameter's mandatory-looking requirement means in practice. Adequate but with clear gaps.

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

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

Schema coverage is 67% and the schema already documents key derivation, main_url, context, and force_create in detail. The description only restates that main_url is optional, adding no syntax or interaction detail beyond the schema, so baseline 3 applies.

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

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

States a specific verb and resource ('Create a new project') plus the optional main_url, which is unambiguous. It does not explicitly distinguish itself from siblings such as update_project or import_repository, so it stops short of a 5.

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

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

Implies when to use it (adding a new project, with optional main_url for AI crawl) and notes a similarity check triggers a confirmation prompt. There is no explicit guidance on alternatives (e.g., when to use force_create vs. waiting for the confirmation, or when to prefer update_project).

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

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=false, so safety is covered structurally. The description adds genuinely valuable behavior beyond that: that omitted steps trigger AI auto-generation and how to disable it. It still omits returned payload structure and any project-permission requirements, so it is not a 5.

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

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

Two sentences, zero filler, with the primary action stated first and the conditional side-effect immediately after. The opt-out flag is placed where the agent will act on it.

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

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

For a 10-parameter mutation tool with no output schema and 40% coverage, the description is thin. It does not state what the call returns (a created case ID would be essential for chaining), and it leaves most optional parameters unexplained. The auto-generation note is the one substantive addition.

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

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

With only 40% schema description coverage across 10 parameters, the description carries a real compensation burden, and it only explains the steps/auto_generate_steps interaction. Parameters like category, tags, context, and detailed_instructions remain undocumented in both places. The auto-gen clarification is meaningful, but the coverage gap keeps this at a baseline 3.

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

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

States a specific verb and resource ('Create a test case in a project'), which is instantly distinguishable from update_test_case and delete_test_case. It does not, however, explicitly contrast itself with sibling creation paths like generate_tests or import_test_cases_from_document.

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

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

Usage is only implied through the auto-generation clause: it tells the agent what happens when steps are omitted and how to opt out with auto_generate_steps=false. There is no guidance on when to choose this tool over the numerous sibling generators (generate_tests, enhance_test_case, k6_generate_from_test_case), which matters given how crowded that namespace is.

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

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the mutation/safety profile is covered structurally. The description adds the non-obvious behavioral fact that deletion is one-sided and leaves the provider still emitting events — a partial-state consequence the annotations cannot express. Irreversibility and behavior on a missing/invalid integration_id are not stated.

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

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

Two sentences, the identification of the operation first and the critical caveat second. Every clause carries information an agent needs; nothing is redundant with the title or annotations.

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

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

For a destructive 3-parameter tool with full schema coverage and no output schema, the description covers the main gotcha an agent must relay to the user. Remaining gaps (irreversibility, whether dependent resources are affected, error behavior) are minor for a leaf delete operation.

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

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

Schema description coverage is 100% and both required parameters (project_id, integration_id) plus the context field are fully documented in the schema. The description adds no meaning beyond them, so the baseline of 3 applies.

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

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

States a specific verb and resource: 'Delete a deploy-provider webhook integration.' The resource is distinctive enough to separate it from create_deploy_integration, list_deploy_integrations and test_deploy_integration in the sibling list. It does not, however, name those siblings to make the distinction explicit.

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

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

'The provider must be told to stop sending events afterwards — this only removes the QualityMax side' gives clear sequencing context that determines whether the agent should treat this as a complete cleanup. No explicit exclusions or named alternatives are given, so it stops short of a 5.

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

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the bar is lower, and the description still adds high-value specifics: the cascade scope (associated test cases, scripts, and executions are also destroyed) and the ownership requirement. It stops short of stating that the deletion is irreversible or whether any confirmation/soft-delete exists.

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

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

Three short sentences, front-loaded with the action, then the precondition, then the blast radius. Every sentence earns its place with zero filler.

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

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

For a two-parameter destructive tool with full schema coverage and no output schema, the essentials are present: what it deletes, what else gets deleted, and who may call it. Only the permanence/recovery question is left unaddressed.

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

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

Schema description coverage is 100%, with project_id documented and the context parameter given a detailed usage contract, so the schema carries the burden. The description adds no parameter-level meaning beyond what is already structured. Baseline 3 applies.

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

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

States a specific verb and resource ("Delete a project"), which cleanly separates it from delete_test_case, delete_script, and delete_deploy_integration among the siblings. It does not explicitly name an alternative or say how it differs from update_project, but the resource is unambiguous.

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

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

"Requires ownership" is a real precondition that shapes whether an agent may call it at all. However, there is no guidance on when to prefer this over alternatives (e.g., archiving, or deleting individual test cases/scripts instead) and no explicit when-not-to-use warning.

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

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the danger class is known. The description adds a genuine behavioral fact beyond the annotations — the ownership/permission requirement — but does not say whether deletion is permanent, whether dependent framework associations or runs are removed, or what the response returns.

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

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

Two short sentences, action first and precondition second, with no filler. Every clause carries information the agent needs.

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

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

For a destructive, non-open-world tool with no output schema, the description covers the action and the authorization requirement but leaves out irreversibility and side effects on linked resources — important context an agent should have before invoking a delete.

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

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

Schema coverage is 100%, and the schema already describes both script_id and the context parameter in detail, including length and phrasing rules. The description adds nothing about parameter format, so baseline 3 applies.

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

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

States a specific verb and resource ('Delete an automation script'), which clearly distinguishes it from update_script, get_script, and the sibling delete_* tools by resource type. It stops short of naming a sibling or contrasting scope, so it is clear but not maximally differentiated.

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

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

The 'Requires ownership of the parent project' line gives one precondition for use. However, there is no guidance on when to prefer this over alternatives (e.g., deleting via delete_project, or disabling instead of deleting) and no note about 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.

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the safety profile is covered. The description adds useful context beyond that (project ownership is required), but does not say whether deletion is permanent, whether dependent generated scripts/test runs are removed, or what happens on failure.

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

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

Two short sentences, action first and the ownership constraint second. Nothing is padded and no restatement of the title.

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

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

For a destructive, irreversible-shaped operation with no output schema, the description covers the permission gate but leaves out the consequences of deletion (cascade effects, recoverability). Enough to call it, not enough to warn an agent about impact.

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

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

Schema description coverage is 100%, and test_case_id plus the analytics 'context' parameter are each fully documented in the schema. The description adds no parameter-level meaning beyond the schema, so baseline 3 applies.

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

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

States a specific verb (Delete) and resource (a test case), which is unambiguous and clearly distinct from sibling mutators like update_test_case or delete_script. It never names an alternative, but the object being acted on is fully clear.

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

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

The sentence 'Requires ownership of the parent project' is a genuine precondition that guides whether the agent can call it. There is no guidance on when to delete versus update/archive, or on related alternatives such as delete_script for downstream artifacts.

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

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

Annotations already mark this as non-read-only but non-destructive and open-world, so the safety profile is partly covered. What the description does not disclose is the key behavior: whether the enhancement overwrites the existing steps/description in place or returns a proposal, whether an LLM round-trip is involved (latency, cost), and whether the original is recoverable. For a mutation tool that performs AI generation, these are material gaps.

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

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

A single well-formed sentence with the output artifacts front-loaded after the verb. No filler, though the space freed by that brevity would have been better spent on the in-place-vs-proposal question.

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

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

No output schema exists, and the description says nothing about return value or whether the changes persist to the test case. For a two-parameter tool with full schema coverage this is close to adequate, but the persistence semantics of an AI mutation are exactly what an agent needs before calling.

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

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

Schema description coverage is 100% and the context parameter carries an extensive contract already, so the schema does the heavy lifting. The description adds no meaning to test_case_id beyond what the schema states, which is the expected baseline.

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

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

Specific verb+resource (enhance a test case) plus the concrete artifact it produces: detailed steps, verification points, improved description. It is distinguished from update_test_case by the AI-driven nature, though the description never names that sibling to make the split explicit.

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

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

No guidance on when to call this versus update_test_case or generate_code_for_test_case, and no prerequisites (e.g., must the test case already exist with a description to improve). The agent must infer usage entirely from the verb.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so safety is covered. The description goes further and discloses the return channel per format — HTML as text, PDF as base64 specifically to avoid binary corruption in MCP clients — which is real behavioral value not present in annotations.

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

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

Two sentences, zero filler, and the most decision-relevant fact (what is produced and from what) is front-loaded before the return-format detail.

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

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

With no output schema, the description correctly compensates by explaining how each format is returned. Combined with the schema covering all params, an agent has enough to call it, though error cases (invalid run_id, oversized reports) are unaddressed.

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

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

Schema description coverage is 100%, so all four parameters (including the enum on format and the required project_id/run_id) are already documented in the schema. The description only echoes the format-to-encoding behavior, so the baseline 3 applies.

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

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

Specific verb (generate/export) plus concrete resource (standalone HTML or PDF report) and source object (saved, project-attributed persona review). This clearly separates it from siblings like run_persona_review, run_persona_consensus, and render_mobile_audit_report.

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

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

Establishes the prerequisite implicitly by requiring a 'saved, project-attributed' review and pointing at run_persona_review via the run_id description, so an agent knows it must run a review first. It stops short of explicit when-not-to-use or naming alternative export paths, so no exclusion guidance.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so safety profile is clear. The description adds valuable context: MCP cannot return binary data, so the zip is saved to a temporary file and the path and size are returned. This explains behavior beyond annotations, though it could mention file persistence or cleanup.

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

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

Two sentences, front-loaded with the core action and followed by essential behavioral context about the return value. No wasted words.

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

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

For a read-only export tool with rich annotations and full schema coverage, the description adequately explains the return value (file path and size) and the temporary file workaround. An output schema is absent, but the description covers what is returned.

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

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

Schema description coverage is 100%, so all parameters (context, framework, project_id) are fully documented in the schema. The description adds no additional parameter semantics; baseline 3 is appropriate.

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

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

States a specific verb 'Export' and resource 'project's test framework as a zip archive', clearly distinguishing it from export_project_as_qtml (which exports QTLM). However, it does not explicitly name alternatives or state differentiation, leaving some inference to the agent.

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

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

The description implies usage (when you need the framework files as a zip) but provides no explicit when-to-use, when-not-to-use, or alternative tools. The agent must infer from context that this is the right export format versus siblings like export_project_as_qtml.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds one genuinely useful piece of context beyond that: the result is a 'document string' rather than a file download, which affects how the agent should handle the output. No mention of permissions, size limits, or included metadata.

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

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

A single sentence with no filler; the verb, resource, and return type are all front-loaded. Nothing could be removed without losing information.

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

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

For a simple read-only export with full schema coverage, the description covers what is exported and that the result is a string. There is no output schema, but the 'document string' phrasing compensates adequately; only edge cases (very large projects, error behavior) are left unspecified.

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

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

Schema description coverage is 100%, so all three parameters (including the verbose context field) are documented in the schema itself. The description's phrase 'and its test cases' aligns with the optional test_case_ids parameter but adds no syntax or defaulting guidance beyond the schema's 'omit for all'.

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

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

The description names a specific verb (export) and resource (a project and its test cases) plus the output format (QTML document string), which clearly separates it from siblings like export_framework or convert_qtml_to_playwright. It never explicitly names a sibling, so no explicit differentiation is provided.

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

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

There is no statement of when to use this tool versus parse_qtml, validate_qtml, convert_qtml_to_playwright, or export_framework. An agent must infer the use case entirely from the verb and resource.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds real value by scoping which Gatling constructs are supported (HTTP builders, injection profiles, checks, feeders), but says nothing about what happens to unsupported constructs or whether the result is persisted via project_id.

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

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

Two tight sentences, front-loaded with the conversion and followed by the supported-feature scope. No filler or restatement of the title.

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

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

With no output schema, the description should clarify what comes back (raw k6 script text versus a saved script in project_id) and how partial conversions are reported. The feature-coverage sentence helps, but the return/persistence behavior is left unaddressed.

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

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

Schema description coverage is 100%, so all five parameters are already documented in the schema. The description adds no parameter-level meaning beyond that baseline, such as the allowed values for test_type or the effect of target_url overrides.

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

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

States a specific verb and resource ('Convert a Gatling Scala simulation to k6 JavaScript') and names the covered feature set. Among siblings like jmeter_to_k6, locust_to_k6, and playwright_to_k6, the source format alone makes it unambiguous which tool to pick.

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

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

Usage is only implied by the name and the source format; there is no explicit 'use when you have Gatling Scala code, not JMeter/Locust' statement even though four sibling converters exist. It also doesn't explain when to supply project_id (save) versus just getting the converted output.

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

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

Annotations declare readOnlyHint=false and openWorldHint=true, so the agent knows this writes something and reaches outside, but the description never says what artifact is produced or persisted, whether the call is long-running/expensive ('deep' analysis implies cost), or whether prior plans are replaced. With no output schema, the description should have carried this and does not.

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

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

A single front-loaded sentence with no filler, which is structurally sound. It is arguably too terse for the amount of unspecified behavior, but there is no bloat to penalize.

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

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

For a non-read-only, open-world generation tool with no output schema, the description omits what a 'plan' contains, whether/how to retrieve it, and how it feeds downstream tools like generate_tests_for_areas. The most important context an agent needs to call it correctly and use the result is absent.

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

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

Schema description coverage is 100%, so both repo_id and the heavily-specified context parameter are already documented in the schema. The description adds no meaning beyond that (no hint about repo_id format or the analytics/word-count constraint on context), so the baseline 3 applies.

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

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

The description names a concrete action (run deep gap analysis) and an output (map testing areas) scoped to a repository, which is more than a restatement of the name. However, it does not distinguish this from close siblings such as generate_gap_tests, generate_tests_for_areas, or repo_quality, leaving the agent to guess which stage of the workflow this is.

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

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

There is no statement of when to call this versus generate_gap_tests or generate_tests_for_areas, no prerequisites (e.g. repository must be imported/refreshed first), and no note on what to do with the result. Usage is only inferable from the word 'analysis' in the sentence.

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

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

Annotations only say readOnlyHint=false and openWorldHint=true; the description adds real behavioral detail beyond them: the appium path is compiled deterministically with no AI generation, runs no coding-seat job, answers inline, and returns compact metadata rather than code, while generic frameworks produce browser-oriented code for native cases. It doesn't disclose how the main AI-generation lane behaves in terms of latency, async job submission, or side effects, which keeps it from a 5.

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

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

Front-loaded with purpose and the most important routing constraint, then organized into a separate native-mobile paragraph. It is slightly verbose: the framework list and appium/platform rules are duplicated between the description and the enum descriptions, so a bit of text could be trimmed.

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

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

For a non-read-only, open-world mutation tool with no output schema, the description covers the workflow (capabilities lookup first, execute after) and the return type for the appium lane. It never states what the primary AI-generation lane returns (raw code, a script ID, or an async job reference), which leaves a real gap for the main path.

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

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

Schema coverage is 100% and the schema descriptions already cover framework auto-detection never selecting appium, platform being required for dual-platform projects, and platform being rejected for other frameworks. The description restates these relationships in narrative form rather than adding new parameter semantics, so the baseline of 3 applies.

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

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

States a specific verb+resource ('Generate automation test code for a specific test case') and immediately distinguishes itself from siblings by naming get_test_capabilities, run_tests, and run_native_test. The native-appium lane is explicitly carved out as a different mode, so an agent can tell what this tool does and does not do without opening the schema.

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

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

Gives an explicit call-order prerequisite ('Call get_test_capabilities first and use its recommendation unless evidence favors an override') and an explicit next step ('execute the result and continue its autonomous QA loop'). It also names the exact alternative to avoid in the native case ('Run the resulting script_id with run_tests — not run_native_test').

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

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

Annotations already declare destructiveHint=false, openWorldHint=true, and readOnlyHint=false, so the mutation/open-world profile is known. The description adds the input source (deep-scan results) and the test categories produced, plus that max_tests is per category, but it omits the consequential downstream behavior: whether generated tests are created as pending (cf. sibling accept_pending_tests) or immediately active, and whether generation is costly/asynchronous (cf. check_generation_status).

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

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

Two sentences, front-loaded with the action and its data dependency, with no filler. Slightly abrupt in omitting any usage routing, but nothing wastes space.

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

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

For a non-read-only, open-world generator with no output schema, the description covers inputs and output categories but leaves the lifecycle unclear (pending vs. accepted tests, execution length, whether results need polling). That gap is material for an agent deciding whether and how to chain this call.

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

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

Schema coverage is 100%, so repo_id, context, and max_tests are fully documented in the schema. The description only reinforces that max_tests is per category, which is a marginal gain over structured data; baseline 3 applies.

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

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

States a specific verb+resource ("generate test cases") and ties it to a concrete input source ("coverage gaps found by analyze_coverage", "deep-scan results"). It also names the output categories (E2E, smoke, integration, security), which differentiates it from generic generators like generate_tests or create_test_case, though that contrast is only implicit.

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

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

The dependency on analyze_coverage implies a prerequisite and a usage moment, but there is no explicit "use this when / use generate_tests instead when" guidance. With siblings like generate_tests, generate_tests_for_areas, enhance_test_case, and create_test_case in the same family, an agent must infer which generator applies.

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

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

Annotations already flag it as non-readOnly, non-destructive, and open-world, and the description adds nothing beyond that. For a generation tool whose sibling check_generation_status exists, failing to say whether the call is asynchronous, long-running, or how results are retrieved is a real gap.

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

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

A single short sentence with no waste and the resource front-loaded. It is terse, though, bordering on under-specified rather than maximally efficient.

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

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

With 5 parameters, an open-world generation action, and no output schema, the description should explain the result lifecycle (async generation and status checking) and what the generated artifacts look like. None of that is present, leaving the agent without the information needed to follow through after the call.

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

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

Schema coverage is 100%, so every parameter is documented in the schema, including the enum for test_type and the strict context parameter rules. The description adds no extra meaning about how feature_description, selectors, or test_type interact, so the baseline 3 applies.

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

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

States a specific verb and resource: generate Playwright tests for a feature or component. It is clear what the tool produces, but it does not distinguish itself from close siblings like generate_gap_tests, generate_tests_for_areas, or generate_code_for_test_case.

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

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

No guidance on when to choose this over generate_tests_for_areas, generate_gap_tests, or generate_code_for_test_case, and no mention of prerequisites such as an imported repository or an existing feature spec. The agent must infer the selection criteria from sibling names alone.

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

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

The annotations already indicate this is a non-read-only, open-world, non-destructive operation, but the description adds no behavioral detail. It does not explain whether generated tests are persisted, returned as IDs, run asynchronously, or subject to limits beyond max_tests_per_area.

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

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

The description is a single front-loaded sentence with no filler. It is appropriately concise, though its brevity contributes to gaps in routing and behavioral context.

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

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

For a five-parameter generation tool with no output schema, this description is too sparse. It does not explain the lifecycle of generated tests, the relationship to discovered areas or area plans, or what the caller should expect in return.

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

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

Schema coverage is 100%, so the schema fully documents all five parameters, including repo_id, area_ids, categories, and max_tests_per_area. The description adds no parameter meaning beyond what the schema already provides, making the baseline 3 appropriate.

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

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

The description states a specific verb and resource: generating test cases for discovered test areas. It is clear enough to understand the core action, but it does not distinguish this tool from siblings like generate_tests or generate_gap_tests.

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

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

The description gives no explicit guidance on when to use this tool versus alternatives such as generate_tests or generate_area_plan. At best, 'discovered test areas' weakly implies a prerequisite, but no actionable usage context is provided.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is fully covered. The description usefully adds what the response contains (per-test scores, judge reasoning, transcripts), which goes beyond annotations, but says nothing about behavior for a missing or still-running run_id, nor about result size or pagination.

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

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

One tight sentence that front-loads the action and then enumerates the returned content. Nothing redundant; only minor gain could come from a sentence on prerequisites.

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

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

With no output schema, the description carries the burden of explaining return values, and it does list the main components of the result. It is close to complete for a simple read tool, with the only gap being the absence of guidance on run status or missing-run handling.

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

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

Schema description coverage is 100%, so both run_id and the required context parameter are fully documented in the schema. The description adds no parameter-level meaning (e.g., where run_id comes from), so the baseline 3 applies.

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

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

The description uses a specific verb and resource ('Get detailed results for an agent evaluation run') and names the payload (per-test scores, judge reasoning, transcripts). This clearly distinguishes it from the sibling run_agent_eval, which triggers a run, though the relationship is never stated explicitly.

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

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

Usage is only implied: because it fetches results for an existing run, an agent can infer it should be called after run_agent_eval with a valid run_id. There is no explicit when-to-use guidance, no mention of preconditions (run must exist/complete), and no named alternative for related reads.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the state precondition (job must be completed) and the content categories returned, but says nothing about behavior for an incomplete/invalid crawl_id or whether results are partial. With annotations carrying the safety load, this is adequate but thin.

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

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

A single front-loaded sentence with the verb and resource first and the return contents second. No filler, no repetition of the title.

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

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

There is no output schema, so the description must convey return values, and it does name the three main artifacts (scripts, test cases, flow memory). It leaves the shape of the response (structure, sizes, pagination) and failure modes unspecified, which is a modest gap for a read tool.

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

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

Schema description coverage is 100%, with crawl_id and the analytics-only context parameter fully documented in the schema, so the baseline of 3 applies. The description adds no meaning beyond the schema, such as the format of the crawl ID or where to obtain it.

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

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

Names a specific verb (get) and resource (AI crawl results) and enumerates the payload: test scripts, test cases, flow memory. It does not distinguish itself from close siblings like check_ai_crawl_status or list_ai_crawl_jobs, so an agent must infer the boundary from those tools' own descriptions.

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

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

The adjective 'completed' implies results should be fetched only after the job finishes, which weakly points to check_ai_crawl_status as the prerequisite. However, no alternative is named and there is no explicit when-not guidance (e.g., what to do for a job still running, or how this differs from list_ai_crawl_jobs).

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds useful post-call context (each node carries grounding status and confidence), but says nothing about graph size, latency, or whether ungrounded nodes appear.

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

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

Two sentences, front-loaded with what is returned before the use case. Every clause carries content; the dense arrow chain is readable but slightly jargon-heavy.

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

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

With no output schema, the description must carry return-value meaning, and it does describe the node types plus per-node grounding status and confidence. It is nearly complete for a read-only fetch; only graph breadth and edge cases (e.g. empty or partial lineage) are unaddressed.

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

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

Schema description coverage is 100%, so repo_id and the required context parameter are fully documented in the schema. The description adds no constraint or format guidance beyond what is already structured, which is the expected baseline.

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

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

States a specific verb and resource ("Return the repository's evidence-grounded discovery graph") and enumerates the node chain repository → evidence → test areas → test cases → automation scripts, which an agent can distinguish from a generic coverage report. It stops short of naming or contrasting with near siblings such as analyze_coverage or repo_coverage.

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

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

"Use to audit test coverage lineage" gives one implied scenario but no explicit when-not conditions and no routing to alternatives like analyze_coverage, repo_coverage, or get_repository. The agent must infer selection criteria from the word "lineage."

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety profile is covered. The description adds the useful default-payload behavior (counts unless explicitly requested) and flags that receipt references have no download resolver, which is genuine behavioral context. It stops short of describing return shape, size, or error behavior.

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

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

Three tight sentences with the core purpose front-loaded and no filler. The final sentence about legacy links versus opaque receipt references is dense and slightly cryptic, but it is short and carries information not stated elsewhere in plain prose.

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

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

With no output schema, the description does the work of telling the agent what comes back (status, timing, outcome, counts, artifacts, receipts) and that optional detail requires flags. That is enough for a single-required-param read tool; only return format details like pagination or size limits are absent.

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

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

Schema description coverage is 100%, so all five parameters including the mandatory `context` analytics field are already documented in the schema. The description restates the default-count behavior for include_logs/include_artifacts and the receipt-link semantics, but adds no syntax or constraint detail beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description names a specific verb and resource ('Get a test execution') and enumerates what is retrieved: status, timing, and outcome. An agent can immediately tell this apart from write/run siblings such as run_tests or trigger_framework_run, though it does not explicitly distinguish itself from adjacent read tools like get_framework_run or k6_report.

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

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

Usage is implied by describing default versus requested behavior ('Log entries and artifact paths are counted by default and returned only when explicitly requested'), which tells the agent the cheap path. However, no alternative tool is named and there is no explicit when-to-use / when-not-to-use guidance.

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

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

Adds substantial context beyond the readOnly/non-destructive annotations: no credit consumption, the full status vocabulary returned, poll-state rules, and a security warning to treat draft content as untrusted evidence rather than instructions — a genuine prompt-injection guard. This is exactly the behavioral detail annotations cannot express.

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

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

Every sentence carries load: identity of the read, cost note, return states, polling rule, trust caveat. Slightly dense, but front-loaded with the most decision-relevant information (free read vs. generation) first.

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

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

With no output schema, the description steps in to enumerate the possible return states and characterize the payload (observed evidence plus unverified hypotheses). Combined with the polling and trust guidance, an agent has everything needed to call it and interpret the result.

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

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

Schema coverage is 100%, so run_id, project_id, context, and if_version_matches are all documented in-schema. The description adds no parameter-level detail (no mention of version matching or what happens when a version matches), so baseline 3 is correct.

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

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

States a specific verb and resource ('Read the same private saved customer handoff shown in run details') and immediately scopes it against the generating sibling by noting it does so 'without generating or spending credits'. An agent can distinguish this read path from request_framework_handoff without inspecting either schema.

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

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

'Poll only while queued or generating' gives explicit timing guidance and implicitly excludes polling on terminal states. It stops short of naming request_framework_handoff as the alternative for producing a handoff that does not yet exist, so the when-to-use-instead routing is only implied.

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

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

Annotations already declare readOnly/non-destructive/non-open-world, yet the description still adds real value: it discloses that results are compact counts plus handoff status, that no raw logs are returned, and that the call does not incur a paid model call — a cost/side-effect fact the annotations cannot express. It further surfaces a downstream cost-and-consent obligation around disabled handoffs, including that consent must never be inferred from a test-run request.

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

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

Roughly 70 words across five short sentences, with the purpose and the return shape front-loaded so the agent can act on the first two sentences. The consent-policy sentences are longer and more procedural than the rest but still carry invocation-relevant behavior rather than filler. Slight density penalty for mixing routing, return shape, and policy in one flat block without separation.

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

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

For a read-only status-poll tool with a fully documented three-parameter schema and no output schema, the description covers what an agent needs: input provenance, what comes back (counts, handoff status), what does not (logs, model calls), and the required follow-up when a failed run offers disabled handoffs. No meaningful gap remains that would cause a wrong invocation.

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

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

Schema coverage is 100%, so the baseline is 3 and the schema does the heavy lifting on the three parameters. The description nevertheless adds provenance the schema lacks by tying run_id to trigger_framework_run, resolving where the identifier comes from. It adds nothing about the context or project_id parameters, but that gap is minor given full schema documentation.

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

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

States a specific verb and resource ('Poll a framework run_id') and anchors it to the producing sibling ('returned by trigger_framework_run'), so the agent knows exactly which object it operates on. It also explicitly contrasts the return payload with raw logs and disqualifies a competing interpretation (a paid model call), separating it cleanly from trigger_framework_run and get_framework_handoff.

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

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

The precondition is explicit: call this with a run_id produced by trigger_framework_run, and switch to get_framework_handoff when the full draft is needed. The consent workflow (present pricing, ask once, wait for explicit approval, respect a refusal) gives concrete when-do-what guidance. It stops short of explicit exclusions, notably polling frequency or backoff guidance, which for a poll-style tool would have made routing unambiguous.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds a genuinely useful behavioral fact beyond that: the returned command embeds the user's registration secret, which warns the agent the output is sensitive. It does not mention authentication requirements or whether the secret is single-use, keeping it short of a 5.

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

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

Two sentences, zero filler, with the resource identified first and the return value second. Nothing is repeated from the title.

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

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

For a one-parameter read-only tool with no output schema, the description covers the essentials: what is produced and that it contains a secret. A note on any prerequisite (e.g., that the user must already be registered) would close the only remaining gap.

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

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

Schema coverage is 100% and the single 'context' parameter is fully specified in the schema, including wording, length and tone constraints, so the baseline of 3 applies. The description says nothing about the parameter, though given how heavily constrained it is in the schema, that omission is tolerable.

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

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

Names a specific verb and resource ('Get the install command for the QualityMax local agent') and states the return value ('a pip install command with the user's registration secret'). It does not explicitly differentiate itself from any sibling, though none of the listed siblings plausibly overlap, so the risk of misselection is low.

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

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

Usage is only implied: an agent can infer this is for when a user needs to install the local agent. There is no explicit statement of when to call it, no prerequisites (e.g., whether the user must be registered first), and no alternatives referenced.

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

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

Adds useful context beyond the annotations: the audit must be completed, the tool returns screenshot URLs and scanner evidence, and the evidence is 'bounded' (though the bound is not specified). Annotations already cover read-only and non-destructive behavior, so this additional workflow and return-shape context is a meaningful supplement.

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

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

Two tight sentences, with the return content front-loaded and the workflow constraint clearly following. Every sentence earns its place.

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

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

For a read-only retrieval tool with full schema coverage and no output schema, the description adequately conveys what comes back at a high level. More detail on the shape or size of 'bounded scanner evidence' would improve it, but it is largely complete.

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

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

Schema description coverage is 100%, so both audit_id and context are fully documented in the schema. The description implies an audit_id is needed for a completed audit but adds no syntax or format detail beyond that, so the baseline of 3 applies.

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

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

States a specific verb (return) and resource (screenshot URLs and bounded scanner evidence) for a completed Mobile Readiness Audit. It distinguishes itself somewhat from report-generation siblings by emphasizing raw evidence, though it does not explicitly name parse_mobile_audit_report or render_mobile_audit_report.

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

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

Gives a clear sequencing rule: use before Claude writes a qualitative report. The prerequisite that the audit be completed is also stated. It stops short of naming alternatives or when-not-to-use conditions.

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

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

The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, covering the safety profile. The description adds useful detail about the return payload (status, score, report path) but does not explain status values, polling behavior, or prerequisites for the audit_id.

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

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

The description is a single, front-loaded sentence that names the operation and its return fields with no wasted words.

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

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

For a read-only status tool with annotations and full schema coverage, the description adequately states what is returned. However, it omits lifecycle context such as whether this should be polled after start_mobile_audit or how it relates to evidence and report-parsing siblings.

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

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

Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no parameter-specific meaning beyond what the schema provides, which is the baseline case for a 3.

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

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

The description states a specific verb ('Get') and resource ('Mobile Readiness Audit') and names the returned fields (status, score, report path). It does not distinguish this tool from siblings like get_mobile_audit_evidence or list_mobile_audits, so it stops short of full clarity.

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

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

The description provides no explicit guidance on when to use this tool versus alternatives such as get_mobile_audit_evidence, parse_mobile_audit_report, or list_mobile_audits. Usage is only implied by the tool name and the word 'status'.

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

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

Annotations already declare readOnlyHint and destructiveHint=false, and the description reinforces rather than contradicts that with a clear negative-scope list. It also discloses the return payload despite having no output schema. A minor gap: no note on size limits or whether instructions are cached/versioned beyond the 'revision' mention.

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

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

Three sentences: purpose first, return payload second, scope/next-step third — good front-loading with no filler. The phrasing 'for testing changing preview/staging URLs' is slightly awkward, but every sentence contributes new information.

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

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

With no output schema, the description does the necessary work by listing the returned artifacts and stating what the tool will not do. It stops short of explaining what 'allowlisted saved project facts' entails or any authentication/precondition needs, which a fully complete definition would cover.

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

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

Schema description coverage is 100%, including a detailed 'context' parameter and the 'workflow' enum, so the schema carries all parameter meaning. The description adds essentially nothing about individual parameters beyond implying the preview-testing workflow, which matches the required baseline of 3.

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

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

The description names a specific verb and resource ('Get project-specific instructions ... for changing preview/staging URLs') and enumerates the concrete artifacts returned (copyable prompt, SKILL.md, revision, allowlisted saved project facts). That level of specificity separates it cleanly from neighbors like get_project_memory and get_install_command.

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

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

It gives an explicit next-step rule ('Use run_tests.base_url for the exact deployment after checking hardcoded URLs') and states exclusions ('does not select a deployment, change settings, or prove script portability/access'). What's missing is a direct comparison against sibling retrieval tools such as get_project_memory or get_framework_handoff, so the agent still has to infer which retrieval to call.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so safety is covered. The description adds genuinely new behavioral context: results are compact and intended to keep context window and cost low, and it is a precursor to a narrower follow-up call. It does not discuss result volume or the limit cap, which the schema handles.

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

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

Two sentences, zero waste. The content of the return is front-loaded and the workflow instruction plus its rationale follow immediately. No repetition of schema or annotation material.

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

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

With no output schema and 7 parameters, the description must convey return shape, and it only names content categories (negotiations, learnings, verified runs) without structure or size expectations. The workflow guidance is complete, but an agent still cannot predict whether it receives prose, ranked lists, or records.

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

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

Schema description coverage is 100%, so query, intent, limit, and context are already documented in structured data. The description reinforces the query semantics ('most relevant verified script') and ties the recall intent to generate/repair workflows, but adds no syntax or filtering detail beyond the schema. Baseline 3 applies.

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

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

Specific verb-plus-resource: returns 'compact durable memory' scoped to prior agent negotiations, project learnings, and verified test runs. It also signals its boundary against the sibling get_script, so an agent can distinguish recall from script retrieval without opening a schema. The wording 'durable memory' is slightly abstract, but the enumerated sources anchor it.

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

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

Explicit sequencing: 'Call this before generating or repairing tests for an existing project; then call get_script only for the most relevant verified script.' It names the alternative and the reason (cost/context), which is strong guidance. It stops short of explicit exclusions (e.g., no memory for a brand-new project), so not a 5.

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

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

The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds some useful context about what the summary includes, but says nothing about permissions, error handling, or return format/pagination.

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

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

Two sentences, front-loaded with the core operation and followed by usage examples. There is no filler, and the most important information appears first.

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

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

For a simple read-only summary tool with full schema coverage and clear annotations, the description is nearly complete. It does not mention the optional return subsets such as last_activity or project, but a caller can discover those from the fields parameter.

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

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

Schema description coverage is 100%, so project_id, fields, and context are already documented in the schema. The description does not add syntax or meaning beyond that, so the baseline of 3 applies.

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

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

The description states a specific verb and resource ('Get a summary of a project') and narrows scope with 'counts of test cases, scripts, and executions.' It does not explicitly distinguish itself from siblings like list_projects or get_project_memory, but the stated contents make the intent clear.

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

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

It gives concrete user-question triggers ('what's in my project?' / 'show me project stats') that indicate when to call the tool. However, it names no alternatives or when-not-to-use conditions, so the agent must infer routing versus list_projects or get_project_memory.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered without the description. The phrase 'stored analysis' adds a small amount of return-content context, but it is left undefined — no indication of what analysis, how fresh, or whether it is cached.

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

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

A single front-loaded sentence with no filler. Its brevity is efficient, though it borders on under-specification rather than tight writing.

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

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

With no output schema, the description must carry the burden of explaining what 'repository details and stored analysis' actually returns, and it does not. Combined with the undocumented repo_id and no sibling routing, an agent has too little to invoke this confidently in a crowded repo-tool namespace.

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

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

Schema description coverage is only 50%: the 'context' parameter is heavily documented, but repo_id has no type-level description and the tool description says nothing about it — not where to obtain a valid ID (e.g., from list_repositories) nor what happens with an invalid/nonexistent ID. The description fails to compensate for the coverage gap.

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

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

The description gives a concrete verb ('Get') and resource ('repository'), plus a hint that it returns stored analysis. It is unambiguous on its own, but it draws no boundary against close siblings like list_repositories, repo_coverage, repo_quality, or refresh_repository, so the agent must guess which one to pick.

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

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

There is no statement of when to use this tool versus the many repo-related siblings (list_repositories to enumerate, refresh_repository to re-index, repo_coverage/repo_quality for specific analytics). The agent gets no prerequisites or exclusions, only the schema's generic 'context' prompting.

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

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

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuine behavior beyond that: the returned value is an *effective* preference set produced by merging per-repo overrides over global defaults, which tells the agent the result is computed, not a raw stored record.

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

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

Three short sentences, front-loaded with the core purpose, then the return semantics, then the invocation rule. Nothing is redundant or padded.

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

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

For a two-parameter, read-only tool with no output schema, the description covers purpose, return semantics (effective merged preferences) and the optional-parameter behavior. An agent has everything it needs to call it correctly and interpret the result.

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

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

Schema coverage is 100%, so both parameters are already documented, and the schema itself states 'Omit to get global defaults only'. The description's repository_id note largely restates that; the one added nuance is that supplying it yields merged overrides rather than just the repo row. Baseline 3 is appropriate when the schema carries the load.

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

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

States a specific verb and resource ('Get the user's AI code-review preferences') and immediately clarifies the resource with the parenthetical 'which categories to check, which to skip'. It is clearly distinct from the write-side sibling set_review_preferences by virtue of its read framing, though it never names that sibling explicitly.

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

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

'Call without repository_id for global defaults' gives one concrete invocation rule, which is more than most definitions offer. However, there is no guidance on when to prefer this over set_review_preferences or how preferences relate to the ai_review workflow, so usage is implied rather than fully framed.

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

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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world). The description adds a real behavioral trait annotations don't: the conditional fetch via if_version_matches, which can return `not_modified` instead of the code. It doesn't mention error behavior, but for a read tool that gap is minor.

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

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

Two sentences, front-loaded with what is returned, then the two optimization knobs. No filler, and each clause carries distinct information.

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

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

For a read-only getter with a fully documented 4-parameter schema and no output schema, this is nearly complete. The one gap is the relationship to sibling retrieval tools, which is not addressed.

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

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

Schema coverage is 100%, so the baseline is 3; the description still earns credit by explaining intent — the version value should come from a previous get_script/update_script, and `fields` exists to trim payload. This adds purpose beyond the schema's literal parameter definitions.

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

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

States a specific verb and resource: fetches an automation script's details, including the generated Playwright/Cypress test code. That distinguishes it from list_scripts and update_script, but the description doesn't say how it differs from other retrievers, such as k6_get_script.

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

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

Gives concrete guidance on how to use the tool well: reuse the version from a previous read so the call returns a cheap `not_modified`, and pass `fields` when only part of the record is needed. It doesn't state when to choose this over similar getters, but the usage context is clear.

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

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

Beyond the read-only annotations, the description discloses the autonomous QA loop behavior, proactive gap suggestion, continuing through safe steps, and stop conditions. It also states what the tool returns and mentions discovery of project-specific agent instructions.

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

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

The description is front-loaded with the key instruction and return summary, then adds the autonomous-loop behavior efficiently. Every sentence contributes useful information without unnecessary repetition.

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

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

With no output schema, the description still explains the main return components and the expected follow-up behavior. It gives enough context for an agent to invoke the tool correctly and know what to do with the result.

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

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

Schema coverage is 100%, so all parameters are already documented in the input schema. The description adds no additional meaning about project_id, context, or test_case_id; baseline 3 is appropriate.

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

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

The description states a specific purpose: return testing capabilities, recommendations, and an autonomous QA loop, and says to start here before generating or running tests. It clearly distinguishes this pre-testing discovery step from siblings like generate_tests and run_tests.

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

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

It gives clear usage context with "Start here before generating or running tests" and instructions for following the loop on testing requests. It does not explicitly name alternatives or exclusion cases, but the sequencing guidance is strong.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral context: the tool can return a `not_modified` sentinel instead of a full record, which is an agent-relevant control-flow detail not present in the schema or annotations.

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

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

Two sentences, zero waste: the first front-loads what the tool returns, the second covers the two optimization knobs. Every clause carries information an agent can act on.

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

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

With no output schema, the description does the work of sketching the return shape (description, steps, automation scripts) and the `not_modified` alternative. It stops short of describing error behavior for invalid or missing test_case_id, which is the only meaningful gap for a read tool of this complexity.

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

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

Schema description coverage is 100%, so all four parameters are already documented in detail, including the exact field list and the context-parameter word-count rules. The description only restates the intent of `fields` and `if_version_matches` in looser terms, adding little beyond the schema; baseline 3 applies.

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

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

States a specific verb and resource ("Get detailed information about a specific test case") and enumerates what comes back: description, steps, and related automation scripts. That distinguishes it from list_test_cases and search_test_cases by singularity and depth, though those siblings are never named.

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

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

The second sentence gives conditional guidance for two optional parameters (skip the body with `if_version_matches`, trim the payload with `fields`), which implies an efficiency-oriented use case. However, there is no explicit statement of when to prefer this over list_test_cases/search_test_cases or what prerequisites exist.

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

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

Beyond the annotations, it discloses the critical approval gate: the first call generates a virtual test plan, and the crawl proceeds only on a second call with approved_virtual_test_plan. This is essential behavioral context for a write/open-world tool whose annotations only state readOnly=false, openWorld=true, destructive=false.

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

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

The workflow is front-loaded, and the critical approval constraint is stated clearly in about 65 words. There is some repetition around user approval between the second and third sentences, though it reinforces an important guardrail.

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

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

For a 12-parameter, open-world, multi-step tool with no output schema, the description covers the crucial approval workflow and second-call requirement. It leaves some gaps around the first-call return shape and how import/test-case creation interacts with the approval step.

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

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

Schema description coverage is 100%, so the schema already documents all 12 parameters. The description adds workflow meaning for approved_virtual_test_plan and mentions document/text import, but does not enrich semantics for crawl depth, framework, test type, or other parameters.

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

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

The description names a specific multi-step workflow: import document/text, create test cases, generate a virtual test plan for approval, then initiate an AI crawl. It is clearly more than a generic import, but it does not explicitly distinguish itself from siblings like import_test_cases_from_document or start_ai_crawl.

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

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

It clearly explains the mandatory two-call usage pattern: present the generated plan, wait for user approval, then call again with approved_virtual_test_plan. It gives no explicit alternatives or when-not guidance against related import and crawl tools.

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

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

Annotations already declare destructiveHint=false, readOnlyHint=false, and openWorldHint=true, so an external write is understood. The description adds the genuinely useful auth prerequisite about saved credentials, but says nothing about how imports behave on duplicates, partial failures, or what is created.

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

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

A single compact sentence, front-loaded with the action and resource, with the credential prerequisite appended. No wasted text, though it is arguably under-specified rather than optimally sized.

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

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

For a mutation tool with no output schema and incomplete parameter documentation, the description omits key details: what a TestRail suite/project id maps to, import scope, and result behavior. It leaves too much for the agent to infer.

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

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

Schema coverage is only 25% (only the context param is documented), yet the description explains none of project_id, testrail_project_id, or testrail_suite_id. With such low coverage the description should compensate but does not.

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

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

States a specific verb and resource (import test cases) and the source system (TestRail) plus the destination (into a project). It is distinguishable from document/qtml import siblings by naming TestRail, though it doesn't explicitly contrast them.

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

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

The parenthetical 'requires saved TestRail credentials' gives a useful precondition, but there is no explicit when-to-use vs alternatives guidance or mention of the TestRail import vs document/qtml import siblings. Usage is only implied by the source system.

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

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

Annotations already declare readOnlyHint=false, destructiveHint=false and openWorldHint=false, so the agent knows this is a non-destructive mutation without external side effects. The description adds that it creates a project and/or test cases, but does not say whether an existing project_id is modified in place, whether existing test cases are appended or replaced, or what is returned on success — the details that matter most for a write tool.

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

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

A single front-loaded sentence with no filler. It is slightly under-specified rather than verbose, and 'and/or' costs precision without buying length savings.

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

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

Not applicable — hmm, adjust below.

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

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

Schema description coverage is 100%, so parameters are already documented, and the description mostly restates them. It does not clarify the interaction between project_id and create_project (e.g., that project_id overrides create_project), which is the one semantic gap the schema leaves implicit.

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

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

States a specific verb ('Import') and resource ('QTML source string') plus the resulting artifacts (project and/or test cases), which is clear on its own. However, it doesn't distinguish itself from sibling tools in the same family (parse_qtml, validate_qtml, convert_qtml_to_playwright, export_project_as_qtml), and 'and/or' leaves the output shape ambiguous.

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

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

There is no statement of when to use this instead of parse_qtml, validate_qtml, or import_test_cases_from_document. The agent must infer usage from the name alone; nothing addresses prerequisites such as whether the QTML should be validated first.

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

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

Annotations already declare a non-read-only, open-world, non-destructive operation; the description adds the useful disclosure that a default project is created when none is specified and that analysis is performed. It does not say how long analysis takes, whether it is synchronous, what permissions the GitHub URL requires, or what is produced.

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

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

Two tightly written sentences with the core action front-loaded. Minimal but no wasted text; slightly more could be said without becoming bloated.

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

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

With no output schema, the description should explain what the agent gets back (repository ID, analysis status, polling need) and how long the import takes. None of that is present, though the project-creation behavior is covered.

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

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

Schema coverage is 100%, so the schema already documents all seven parameters including defaults and the enum. The description adds no parameter detail beyond the project-creation implication, which is baseline-appropriate.

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

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

States a specific verb ('import and analyze') and resource ('a GitHub repository') with a clear testing scope. It implicitly distinguishes itself from read-only siblings like get_repository and list_repositories, but never names them, so the boundary with refresh_repository remains unclear.

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

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

No indication of when to call this versus refresh_repository, get_repository, or list_repositories. The only guidance offered is about default project creation, which is a side effect, not a usage condition.

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

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

The description adds the non-obvious fact that extraction is LLM-driven ('The AI will analyze the text'), which is useful context beyond the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false). It omits whether imported cases land as pending/draft (see accept_pending_tests) or are created directly, and says nothing about latency or cost.

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

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

Two short sentences with no filler, front-loading the action. It is efficient but under-informative rather than wasteful.

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

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

For an AI-extraction mutation tool with no output schema, the definition should say what comes back (number of extracted cases, pending state) and any preconditions. Annotations cover the safety profile but the outcome lifecycle remains unclear.

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

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

Schema coverage is 75%, so most parameters are documented in the schema already (context, source_name, text_content). The description only maps loosely to text_content and adds no guidance on project_id or source_name semantics, which is acceptable at this coverage baseline.

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

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

States a specific verb+resource ('Import test cases from document text content') and clarifies the AI-driven extraction mechanism, so the agent knows the input is raw text rather than a file or URL. It does not, however, differentiate itself from siblings like import_document_and_crawl, import_qtml, or generate_tests.

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

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

No statement of when to use this versus the many other import/generation siblings, and no prerequisites or exclusions. The agent must infer the choice from the source-medium phrase alone.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered externally. The description adds real value by disclosing conversion coverage of specific JMeter element types, but it is silent on fidelity limits (unsupported samplers), and it never mentions the persistence implied by the project_id parameter — a tension with readOnlyHint that the description does not resolve.

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

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

Two sentences, no filler, and the core action is front-loaded before the coverage detail. Every clause earns its place by telling the agent either what the tool does or what input it can digest.

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

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

With no output schema, the description should say what comes back — inline k6 JavaScript, a saved script reference, or both — and it does not. Five parameters including an analytics-oriented context field and a free-form test_type are left unaddressed, leaving the agent guessing at the result shape of an otherwise straightforward conversion tool.

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

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

Schema description coverage is 100%, so the baseline is 3 and the schema carries most of the burden. However, the description adds nothing about the non-obvious parameters: test_type is a free string with no stated enum of valid k6 test types, and the description does not explain the relationship between returning a script and the project_id save behavior.

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

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

States a specific verb and resource pair — converting a JMeter .jmx plan into k6 JavaScript — and enumerates the JMeter constructs handled (thread groups, HTTP samplers, timers, assertions, extractors). The named source format cleanly separates it from gatling_to_k6, locust_to_k6, and playwright_to_k6 without the agent needing to open any schema.

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

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

Usage is only implied: the agent can infer it should call this when it holds a JMeter plan and wants k6 output. There is no explicit 'when not to use', no mention of alternatives (e.g. k6_generate for from-scratch generation), and no statement of prerequisites such as plan size limits or required project context.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds that this is a polling call sequenced after a run, but says nothing about whether intermediate results are partial, whether polling is rate-limited, or what states can be returned.

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

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

Two short sentences, front-loaded with the action and followed by the sequencing hint. No waste, though the second sentence is so terse it borders on under-specification rather than efficient concision.

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

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

There is no output schema and no description of return shape, status values, or completion signal, so an agent polling this tool cannot tell from the definition when a run is finished. Combined with two undocumented optional parameters, the definition leaves real gaps for a polling tool.

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

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

Schema coverage is only 50%: include_artifacts and include_receipt_details have no descriptions anywhere, and the description does not explain what artifacts or receipt details mean or when to enable them. It only loosely gestures at "results," leaving half the parameters undocumented in both places.

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

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

States a specific verb and resource: checking status and results of a k6 test execution. It distinguishes itself from the upstream action by naming k6_run_test, but does not clarify its relationship to near-siblings like k6_report or get_execution, which plausibly also return run outcomes.

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

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

"Poll this after k6_run_test" gives a clear prerequisite and implies repeated invocation, which is useful. However, there is no guidance on when to stop polling, no polling cadence, and no exclusion telling the agent when to prefer k6_report for the final results instead.

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

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the safety profile is clear. The description adds only the supported test-type list; it does not disclose what happens when code is omitted, whether the script is stored, or what the tool returns, 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.

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

Two tight sentences with no filler, and the core action is front-loaded. Nothing could be removed without losing information.

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

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

For an 8-parameter creation tool with no output schema, the description is minimal. The rich input schema compensates for inputs, but the absence of return-value context and sibling differentiation leaves the definition only adequately complete.

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

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

Schema description coverage is 100%, so all parameters are documented in the schema. The description adds no parameter-level meaning beyond the enum values already present in the schema, making the baseline score of 3 appropriate.

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

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

The description states a specific verb ('Create') and resource ('k6 performance test script'), and lists supported test types. It does not differentiate from sibling tools like k6_generate or k6_generate_from_test_case, so the agent cannot tell which one to pick based on scope alone.

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

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

No guidance is given on when to use this tool versus the several other k6 script creation/generation tools. The list of test types describes capabilities, not decision criteria or alternatives.

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

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=true, so the mutation and open-world profile are covered. The description adds useful behavior by saying it auto-creates load profiles with thresholds, but it does not state where scripts are saved, whether existing names are overwritten, or what permissions are required.

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

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

Two sentences, front-loaded with the core action and scope. Every clause adds useful information about the source inputs and auto-created load profiles, with no filler.

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

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

For a generation tool with eight parameters, no output schema, and significant sibling overlap, the description covers the core action and one behavioral detail but omits what is returned and how the generated script/profile is surfaced. It is minimally adequate because the rich schema documents the parameters, but it does not fully prepare the agent for invocation and follow-up.

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

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

Schema description coverage is 100%, so the schema already explains all eight parameters, including vus, duration, test_type, and the required project_id. The description only mentions target_url and endpoints, adding no syntax, defaults, or enum guidance beyond the schema, so the baseline 3 applies.

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

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

The description gives a specific verb and resource: generate a k6 performance test script from a target URL and optional endpoint list. That source-based scope implicitly separates it from k6_generate_from_test_case, but it does not explicitly name or distinguish among the several k6 sibling tools.

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

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

It implies when to use the tool by stating it generates from a target URL and optional endpoint list, but it gives no explicit when-to-use versus alternatives, prerequisites, or exclusions. The agent must infer the selection criteria relative to k6_create_script and k6_generate_from_test_case.

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

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

Annotations already declare readOnlyHint=false, openWorldHint=true, and destructiveHint=false, so the agent knows this is a non-destructive generation operation that may involve external context. The description adds that it extracts HTTP interactions and builds a load test, but it does not say whether a new script is persisted, returned, or how it relates to k6_get_script or k6_list_scripts.

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

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

The description is two tightly written sentences with no filler, and the core action is front-loaded: generate a k6 performance test from an existing test case. The second sentence adds useful detail without bloating the definition.

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

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

The tool has a fully described 5-parameter schema and meaningful annotations, and the description explains the transformation source and output type. However, there is no output schema and the description does not clarify what the generated artifact looks like or whether it is stored, which leaves a minor completeness gap for a generation tool.

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

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

Schema description coverage is 100%, so the input schema already documents test_case_id, vus, duration, test_type, and context, including the enum for test_type. The description adds no parameter-specific meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description states a specific verb and resource: generate a k6 performance test from an existing QualityMax test case, and adds that it extracts HTTP interactions. It is clear what the tool does, but it does not explicitly distinguish this from nearby siblings such as k6_generate, k6_create_script, or the various *_to_k6 converters.

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

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

The source condition is implied by 'from an existing QualityMax test case', which tells the agent this is for cases where a test case already exists. However, there is no explicit guidance on when to prefer this over k6_generate, k6_create_script, or conversion tools, and no exclusions are given.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description usefully adds that the response contains the script's code (relevant since there is no output schema), but says nothing about error behavior for a missing/invalid ID.

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

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

A single short sentence with no wasted words, and the key distinguishing element ('including its code') is front-loaded. It is efficiently written, though arguably too terse to be an ideal definition.

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

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

For a simple read-only lookup with full schema coverage, no output schema, and annotations carrying the safety profile, the description covers the essentials: what it returns (the script plus code). Minor gaps around failure modes keep it from a 5.

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

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

Schema description coverage is 100%, so both parameters (context and script_id) are already documented in the schema. The description adds no format or constraint detail beyond what the schema states, making the baseline 3 appropriate.

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

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

States a specific verb+resource ('Get a k6 script') and adds a distinguishing detail ('including its code'), which separates it from k6_list_scripts and k6_update_script. It stops short of naming a sibling explicitly, so it lands at 4 rather than 5.

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

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

The description gives no when-to-use guidance, no prerequisites (e.g., that a script_id typically comes from k6_list_scripts), and never mentions alternatives such as k6_list_scripts or the generic get_script. Usage is only inferable from the name.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds nothing further — no mention of result volume, ordering, pagination, or whether an empty project returns an empty list or an error.

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

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

A single front-loaded sentence with zero filler. It is efficient, though it errs toward under-specification for a tool with a filtering parameter.

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

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

For a simple read-only list tool with full schema coverage and no output schema, the description is minimally viable. It still leaves the agent without any sense of return shape or how test_type narrows results, which matters when choosing between this and k6_get_script.

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

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

Schema description coverage is 100%, so project_id and context are already documented. The description echoes project scoping but says nothing about test_type filtering, which is the one parameter whose accepted values (k6 test kinds) are non-obvious. Baseline 3 applies when the schema does the work.

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

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

States a specific verb ('List') plus a specific resource ('k6 performance test scripts') scoped to a project. The 'k6' qualifier implicitly separates it from the generic list_scripts and from k6_get_script, but the description never names a sibling to make the distinction explicit.

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

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

There is no guidance on when to use this versus list_scripts, k6_get_script, or k6_generate, and no stated preconditions. The agent must infer usage from the name alone.

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