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

No annotations are provided, so the description carries the full burden. It usefully discloses that the tool returns a structured prompt rather than a direct answer, and enumerates the fields (hypothesis, evidence, severity, priority, fix, prevention). It stops short of stating permissions, side effects, or whether the analysis is read-only.

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

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

Two tight sentences, front-loaded with the purpose and immediately followed by the return behavior. Every clause earns its place with no redundancy.

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 helpfully explains what the tool returns, and parameter coverage is complete via the schema. The main missing piece is usage/routing guidance against similar siblings like generate_rca.

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 each of the five parameters is already documented in the schema. The description lists the same input types (symptoms, stack traces, logs, source) without adding format or syntax detail, 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 (analyze) and resource (bug) plus the input types it consumes (symptoms, stack traces, logs, source). However, it does not differentiate itself from the sibling 'generate_rca', which is a closely related root-cause-analysis tool an agent could easily confuse with this one.

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 its use case ('to find the root cause of a bug') but gives no explicit when-to-use, when-not-to-use, or alternative tool guidance. With a sibling like generate_rca present, the absence of routing guidance is a real gap.

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?

No annotations are provided, so the description carries the full burden. It does disclose one important behavioral trait: the tool returns a "structured prompt" rather than executing a check itself, implying a read-only, non-mutating operation. However, it omits permissions, whether it inspects live systems, and any side effects or rate limits.

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

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

Two tightly written sentences: the first states the action, the second the return. Zero filler and the essential purpose is front-loaded.

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

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

There is no output schema, and the description compensates by characterizing the return (a structured prompt assessing risk, rollback, observability, and go/no-go gates). With only one required parameter and well-documented optional ones, an agent has enough to call it correctly; only the absence of precondition/permission detail keeps 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 80%, so the schema already documents changeSummary, blastRadius, observability, and rollbackMechanism. The description adds no per-parameter meaning beyond what the schema supplies (the enum riskLevel is self-explanatory), so the baseline of 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?

The description gives a specific verb+resource: "Perform a pre-deployment readiness check," and the second sentence enumerates what the check covers (risk, rollback, observability, go/no-go gates). This is clear. It does not name or contrast any sibling tool, so it falls short of 5, but no sibling (review_cicd_pipeline, review_infra_config) plainly overlaps in function.

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

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

"Pre-deployment" implies the usage window, but there is no explicit when-to-use statement, no prerequisites, and no guidance on when a different tool (e.g. review_cicd_pipeline, review_infra_config) would be a better fit. Usage is implied rather than stated.

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

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

With no annotations, the description carries the burden, and it does disclose the key non-obvious behavior: this does not produce a finished ADR, only a structured prompt the host LLM must complete. That is genuinely useful. It does not mention determinism, side effects, or that the output format is MADR-prescribed content.

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 verb and format, then immediately the crucial distinction that the return value is a prompt rather than a document. No waste.

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 prompt-emitting generator with no output schema, the description covers the essentials: format, inputs by schema, and output shape. It is silent on how the prompt should be used (templating, tool chaining, expected host LLM behavior) and on edge cases like missing consequences, which for a generation tool aimed at a host LLM is a meaningful 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 83%, so the schema describes five of six parameters well, including the enum for status and minItems=2 for options. The description adds only the 'MADR-style' framing and none of the parameters; baseline 3 applies when the schema does the heavy lifting.

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 (Generate) and resource (ADR in MADR style), and adds what it returns: a structured prompt for the host LLM. It doesn't name a sibling, but it is distinguishable from generate_documentation and recommend_architecture by its ADR/MADR framing.

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 context ('generate an ADR'), and the two-stage nature (prompt the host LLM completes) hints at a workflow. There is no explicit when-to-use vs generate_documentation, recommend_architecture, or generate_implementation_plan, and no guidance on 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?

No annotations are provided, so the description carries the full burden. It does disclose a genuinely non-obvious trait — the tool returns a structured prompt the host LLM executes rather than writing code itself, implying a read-only/analysis posture — but says nothing about permissions, whether any files are written, or invocation limits.

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

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

Two sentences, front-loaded with the core action and then the return behavior. 'clean, scalable, production-ready' is mild marketing padding, but nothing is redundant enough to waste 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?

With no output schema and no annotations, the description does explain the return value (a structured prompt), which is the most important missing piece. It still leaves the agent unsure how this differs from scaffold_service or whether constraints/includeDocs actually alter the prompt content.

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 six parameters, including defaults for includeTests and includeDocs. The description adds no syntax, format, or constraint-interpretation guidance 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?

The description names a specific verb ('Generate') and resource ('code for a feature') plus the artifacts produced (tests, logging, error handling, docs). However, it never distinguishes itself from close siblings like scaffold_service, generate_test_cases, or generate_documentation, which an agent would need to route between.

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 explicit when-to-use, when-not-to-use, or alternative named anywhere in the description. Given the crowded sibling set (scaffold_service, generate_test_cases, generate_documentation, generate_implementation_plan), the absence of routing guidance is a real gap.

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?

No annotations are provided, so the description carries the behavioral burden, and it discloses a genuinely non-obvious trait: the tool returns a structured prompt for the host LLM to complete rather than the finished document. This prevents a wrong mental model of the return value. It does not mention token/length limits or permissions, which is why 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 core action front-loaded and the return-behavior caveat immediately after. Every clause 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?

With no output schema, the description must explain what comes back, and it does so clearly via the structured-prompt return. Combined with 100% schema coverage for a four-parameter tool, an agent has what it needs to invoke it correctly.

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

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

Schema description coverage is 100%, so every parameter is already documented in the schema; the description adds only loose references ("doc types", "source material") that map to existing fields. Baseline 3 applies when the schema does the heavy lifting.

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 (generate) and resource (technical documentation) and enumerates the five concrete doc types matching the docType enum. It stops short of differentiating from siblings like generate_adr or generate_implementation_plan, which also produce documents, so an agent must infer the boundary.

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?

"From supplied source material" implies the precondition, and the listed doc types hint at scope. There is no explicit when-to-use vs. alternatives guidance and no exclusions telling the agent when to reach for a sibling generation tool 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?

No annotations are provided, so the description carries the disclosure burden. It usefully reveals that the tool returns a 'structured prompt' containing milestones, dependencies, risks, and a definition of done, which is real behavioral value. It says nothing about permissions, side effects, or whether any state is created, leaving 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 zero filler, front-loading the action and then the return content. Slightly under-elaborated for the number of concerns it could address, but nothing is wasted.

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 compensates by summarizing the return structure (milestones, dependencies, risks, definition of done), and all four parameters are documented in the schema. The remaining gap is the absence of any usage/disambiguation guidance against the many sibling generators.

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 four parameters (feature, context, constraints, knownUnknowns) with clear descriptions. The description adds no syntax or format detail beyond what the schema provides, 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 ('Generate') and a distinctive resource ('phased implementation plan for a feature or change'), which separates it from siblings like generate_migration_strategy and recommend_architecture. It does not, however, explicitly route the agent away from any named sibling, 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?

There is no explicit when-to-use guidance, no prerequisites, and no mention of alternative tools such as recommend_architecture or generate_adr that also produce planning artifacts. Usage must be inferred entirely from the resource 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?

No annotations are provided, so the description carries the full burden. It usefully discloses the return shape (structured prompt yielding stages, dual-write/dual-read plans, validation, cutover, rollback), but says nothing about side effects, permissions, whether output is persisted, or determinism of the generation.

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. The purpose is front-loaded and the return-shape detail follows, with every clause earning 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 four-parameter generation tool with a fully documented schema and no output schema, the description covers what it does and what it returns. The remaining gap is routing guidance relative to the other planning/architecture generators, which limits it from being fully 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 the schema already documents all four parameters, including constraints like downtime tolerance and deadlines. The description adds no syntax, format, or example guidance beyond the loose 'one technology to another' framing, 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 ('Generate a migration strategy from one technology / version / pattern to another'), which is clearly distinct from plan/architecture siblings. However, it never names or contrasts with the nearest alternatives such as generate_implementation_plan or recommend_architecture, so sibling differentiation is left to inference.

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 explicit when-to-use, when-not-to-use, or prerequisite guidance. An agent cannot tell from this text whether this tool should precede or follow generate_implementation_plan, or under what circumstances a migration strategy is the right artifact versus an implementation plan.

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

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

With no annotations, the description carries the full burden. It does disclose a genuinely important trait – it returns a structured prompt the host LLM completes, rather than the finished document itself – which is valuable beyond structured fields. However, it omits permission requirements, how optional params (8 of 9 are optional) are handled when absent, and any limits.

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

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

Two sentences, front-loaded with the core action, with no filler. The second sentence is long but each listed section earns its place by telling the agent what the generated document will contain.

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, so the description must characterize the return value – and it does, describing the structured prompt and its component sections. For a 9-parameter generation tool this is largely complete, with only optional-parameter behavior 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 89%, so the schema already documents nearly every parameter. The description lists the output sections (summary, timeline, root cause, impact, resolution, corrective actions, preventive measures, lessons learned), which loosely maps to the input fields but adds little syntax or format meaning beyond the schema.

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 ("Generate") and resource ("detailed Root Cause Analysis report") scoped to "a production incident," and no sibling tool overlaps with this capability, so an agent can distinguish it immediately.

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 context "for a production incident" implies post-incident usage, but the description never states when to reach for this versus e.g. analyze_bug or jira_create_issue, nor any preconditions or exclusions. Usage is inferable but not 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?

No annotations are provided, so the description carries the full behavioral burden. It discloses output conventions (@DisplayName on every class, nested group, and method; copyright header; package layout), which is useful, but never says whether the generated test is returned as text or written into the repository, nor whether it overwrites an existing file. For a code-generation tool that ambiguity matters.

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, front-loaded with what is produced, then conventions, then invocation cadence. Every sentence carries distinct information with no repetition of the name or 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?

There is no output schema, so the description stands in for the return contract, and it does describe the structure of the generated artifact reasonably well. It stops short of stating the delivery mechanism (returned string vs. written file) and any write-side effects, which is the one thing an agent still has to guess.

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 description adds real value on top: it explains when coverageTargets is needed (when a branch isn't obvious from the source) and confirms the default package/IT behavior. It does not add syntax detail beyond what the schema strings already say.

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 a JUnit 5 + Mockito test class for one Java class. It also names the framework stack and header conventions, so an agent can distinguish it from generate_code or scaffold_service without reading 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?

Explicitly says 'Call once per class' and prescribes the exact alternative workflow for the project-wide case (glob sources, invoke per file). It also flags that integration tests are off by default and most classes only need unit tests, which is real when-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?

With no annotations, the description carries the full behavioral burden. It does valuable work by disclosing that the return value is a 'structured prompt' covering four named sections (rather than a finished analysis), which is important and not in any schema. However, it omits any detail about permissions, execution cost, or how the four sections are framed.

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: the first states purpose, the second enumerates the return structure. The function is front-loaded and everything present 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?

There is no output schema, so the description must characterize the return, and it does so by naming the four sections of the produced prompt. Combined with the fully documented inputs, this is nearly complete, though it stops short of explaining how the four sections relate or what an agent should do with the returned prompt.

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 (hotspots, symptoms, codebaseSummary) are already documented in the schema. The description adds no parameter-level syntax, format, or interaction detail beyond what the schema provides, 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 ('Identify technical debt in a codebase') plus the secondary action ('propose a refactoring strategy'). It does not explicitly distinguish itself from nearby siblings like review_code or review_architecture, but the purpose is unambiguous 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?

The description never says when to reach for this tool versus alternatives such as review_code, review_architecture, or recommend_architecture, nor does it state any prerequisites or exclusions. Usage must be inferred entirely from the tool name and 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It says nothing about auth/permission requirements, whether the comment triggers notifications, whether Jira markup is supported, or what the call returns, all of which are relevant for a write operation.

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 purpose and followed by the use cases. No filler, no redundancy; every clause 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 simple two-parameter tool with no output schema, the description covers purpose and usage scenarios adequately. It stops short of the behavioral specifics (permissions, response, formatting) that would make the definition fully self-sufficient given the absence of annotations.

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%: 'comment' is documented in the schema and effectively restated here, while 'issueKey' has no description in either place. The description does not compensate for that gap or add format/syntax guidance beyond what the schema already supplies.

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+resource: 'Add a comment to a Jira issue.' That is unambiguous and easy to distinguish from siblings like jira_create_issue or jira_update_issue, though it never explicitly contrasts itself with 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 second sentence offers concrete use cases (investigation notes, RCA summaries, implementation updates), which is implied guidance on when the tool fits. However, it names no alternatives and sets no exclusions or prerequisites, so an agent still has to infer that this is the tool to use whenever a comment is needed.

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

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

With no annotations, the description carries the full behavioral burden. 'Attach' implies a mutation, but nothing is said about required permissions, idempotency, duplicate-link handling, or reversibility.

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 efficient sentence, front-loaded with the action and resource. No wasted words, though the terseness contributes to the documentation gaps elsewhere.

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?

A mutation tool with no annotations, no output schema, and 0% parameter coverage needs more than one sentence. An agent lacks the permission, duplicate-handling, and per-parameter context required to invoke it confidently.

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 0%, so the description must compensate. The parenthetical examples add meaning for `url`, but `issueKey` and `title` remain completely undocumented in both schema and description, leaving two of three params unexplained.

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 (Attach) and resource (remote link) with a clear target (a Jira issue), and the URL examples clarify it handles external URLs. This implicitly distinguishes it from the sibling jira_link_issues, but the description never names that sibling to make the contrast 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 use this versus alternatives, despite the closely related jira_link_issues sibling. The URL examples imply the external-link use case, but there are no exclusions or prerequisites stated.

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?

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing beyond the word 'Create'. It does not mention that 'summary' is the only required field, that a project key falls back to a default, what the call returns (the new issue key, needed for follow-ups), or any permission/auth requirements. For a mutation tool with zero annotation coverage this is a significant 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?

Two short sentences, purpose front-loaded, with the use cases earning their place by routing the agent from analysis siblings to this action tool. No filler or redundancy.

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 mutation tool with no annotations and no output schema, the description is under-specified: it omits the required-field constraint, the default project fallback behavior, and any indication of the return value. An agent could call it, but not confidently or correctly in edge cases.

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 50% schema description coverage, half the parameters (labels, summary, description, assigneeAccountId) have no documentation in either the schema or the description. The description adds no parameter guidance at all, so it 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 states a specific verb and resource ('Create a Jira issue'), which is unambiguous and clearly distinguishable from siblings like jira_update_issue or jira_get_issue. It does not explicitly name those siblings, but the verb+resource pairing is precise enough that the agent knows what the tool does.

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 concrete triggering scenarios: filing bugs after analysis, opening tickets for RCA action items, and creating stories from implementation plans. This connects well to sibling tools like analyze_bug and generate_rca, giving clear context for when to reach for it, though it names no exclusions or explicit 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?

With no annotations, the description carries the full behavioral burden. 'Fetch' implies a read with no side effects, but the description says nothing about auth requirements, error behavior for missing keys, or what the call retrieves.

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 padding; the verb and resource lead. It is efficient but 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 simple two-parameter read tool with a full-coverage schema this is minimally adequate, but with no annotations and no output schema the description could reasonably state what is returned or note the read-only nature.

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 (issueKey with an example, and the optional fields allowlist) are already documented. The description adds no syntax or format detail beyond the schema, 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 ('Fetch a Jira issue') and adds the key-based scope, which implicitly separates it from the sibling jira_search_issues. It does not, however, explicitly name that sibling or clarify what makes this distinct from other jira_* reads.

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 when-to-use guidance, no prerequisites, and no mention of alternatives such as jira_search_issues for when the key is unknown. The agent must infer usage entirely 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?

No annotations are provided, so the description carries the full burden. It discloses that link types are project-defined with common examples, which is useful, but says nothing about permission requirements, whether links are reversible/removable, or what the inward/outward direction semantics mean in practice. That last omission is significant for a link-creation API.

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 action front-loaded and the link-type caveat second. No filler, though the second sentence is a list rather than a constraint 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?

For a simple 3-parameter mutation tool with no annotations and no output schema, the description is roughly adequate but omits the direction semantics of inward vs outward issues, which is the main ambiguity in this API. A bare-minimum-viable definition.

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 baseline is 3. The description adds a marginally broader list of common link types ('is blocked by', 'Duplicate') beyond the schema's examples, but does not clarify inwardIssue vs outwardIssue semantics, which is the parameter detail most likely to trip an agent up.

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: 'Link two Jira issues', which is unambiguous about the operation. It doesn't differentiate itself from the sibling jira_add_remote_link, so an agent could confuse the two, but the core purpose is 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 description never says when to use this tool versus jira_add_remote_link or when not to link. No prerequisites, no exclusions, no alternative routing — only implied usage 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?

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: no read-only confirmation, no mention of the 100-result hard cap, no pagination behavior, no statement of what happens when JQL is malformed. A search tool with zero annotation coverage needs far more than one sentence.

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, so it is efficient in form. The brevity is arguably under-specification rather than conciseness, but as structure it is clean and the purpose is stated 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 three-parameter query tool with no output schema, no annotations, and only one-third of parameters documented, the description should explain result shape, pagination, and field selection. None of that is present, leaving the agent to guess at core behavior.

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 33% — the lone documented parameter, jql, is described tautologically as 'JQL expression.' The description adds no meaning for fields (which fields are returned, whether it defaults to all) or maxResults (the schema caps it at 100, which is worth calling out). It does not 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?

States a specific verb (Search) and resource (Jira issues) with the mechanism (JQL expression), which is enough to distinguish it from jira_get_issue or jira_create_issue in the sibling list. However, it never explicitly contrasts itself with those siblings or states the scope of what it returns.

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 use this versus jira_get_issue for a known key, nor any prerequisites such as required Jira permissions or project scope. The agent must infer that this is the broad-query tool 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.

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the side effect that a comment may be posted, but says nothing about required permissions, that transitionName must match an actual workflow transition, or what happens on an invalid transition.

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, purpose and optional side-effect front-loaded. Every clause 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?

With no annotations and no output schema, the description covers the core purpose and the comment side effect, but leaves the agent without permission prerequisites, workflow-validation behavior, or any indication of the result. Adequate but visibly incomplete for a mutation 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 67%; transitionName is well documented in the schema and the description reinforces the 'as shown in Jira workflow' nuance. issueKey is undocumented in both places, and the description adds no format detail beyond the schema.

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 ('Transition') and resource ('a Jira issue') with a target ('a new status') and concrete examples. It is distinguishable from jira_update_issue in principle because it names status transition rather than field editing, though it never explicitly says so.

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 use this versus jira_update_issue or jira_add_comment, even though both overlap (status change vs field update; the optional comment vs the dedicated comment tool). Usage is only implied by the verb choice.

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

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

With no annotations, the description carries the full behavioral burden, yet it discloses nothing beyond the tautological implication that a mutation occurs. It does not say whether omitted fields are preserved or cleared, what permissions are needed, or how failures behave for a 7-parameter write operation.

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 single sentence is front-loaded and free of filler, but for a 7-parameter mutation tool it is under-specified rather than concise. Brevity here costs the agent needed information instead of saving 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?

A write tool with no annotations, no output schema, nested/raw fields (extraFields), and near-zero parameter documentation requires substantially more description than one sentence. Nothing about field-merge behavior, permissions, or error handling 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 description coverage is only 14% — just the pass-through extraFields property is documented. The description compensates for none of this, leaving labels, summary, priority, description, and assigneeAccountId with no semantic guidance in either place.

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 (Update) and resource (a Jira issue's fields), so an agent knows it is a field-mutation tool rather than a workflow tool. It does not, however, distinguish itself from sibling mutators like jira_transition_issue or jira_add_comment, which also modify an issue.

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 jira_transition_issue, jira_add_comment, or jira_link_issues, all of which also change an issue. No prerequisites, no exclusions, no routing information.

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

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

With no annotations, the description carries the full burden, and it does disclose the substantive behavior: the tool returns a structured prompt containing architecture decisions, a text component diagram, data flow, scaling strategy, failure modes, and a decision log. It does not state whether anything is persisted or what permissions are needed, but for a generative, side-effect-free tool the disclosed output shape is meaningful context beyond the schema.

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 zero filler; the purpose is front-loaded and the return contents follow immediately. Nothing is repeated from the schema or name.

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 usefully enumerates what the structured prompt will contain, and all five input params are covered by the schema. The only material gap is routing guidance against sibling design/review tools, which does not affect call correctness.

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 (goal, cloud, currentState, nonFunctional, scaleRequirements) are already well documented with examples. The description adds no parameter-level syntax or format guidance, 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+resource ('Recommend a scalable system design') with the scope qualifier 'for a stated goal'. It clearly distinguishes design recommendation from the sibling review_architecture, 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?

Usage is only implied: the tool takes a design goal plus optional current-state/constraints and produces a design. There is no explicit when-to-use guidance and no exclusion pointing to review_architecture for existing systems or generate_implementation_plan for execution detail.

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

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

With no annotations, the description carries the full behavioral burden. It does disclose the key behavioral trait — the tool returns a structured prompt rather than an analysis result — which is unusual and valuable. However, it says nothing about permissions, side effects, determinism, or output shape, leaving notable gaps for a tool with no annotation coverage.

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 most important and surprising fact (it returns a prompt, not findings) is placed second where it is easily read. Every clause 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?

No output schema, no annotations, and the description does not specify what the returned structured prompt looks like or how to consume it, nor does it distinguish the tool from its many review-adjacent siblings. The 'returned prompt' behavior is disclosed but not elaborated, leaving an agent under-informed for routing and follow-through.

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 three parameters including the enum for componentType and examples for concerns. The description adds no parameter-level detail beyond what the schema provides, 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+resource ('Review an existing architecture') and scopes the domains checked (scalability, reliability, security, operability). It doesn't explicitly differentiate from siblings like review_code or review_infra_config, which overlap as 'review' tools, but the enumeration of architecture types and concern dimensions makes 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?

No when-to-use guidance and no routing to alternatives. An agent cannot tell from this description when to pick review_architecture over review_infra_config or review_code, despite obvious surface overlap among the sibling review 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?

With no annotations, the description carries the full burden. It usefully discloses the output shape ('severity-tagged findings and recommended fixes'), which matters because there is no output schema. But it says nothing about whether the tool only reads (no mutation of the pipeline), size limits on config input, or how findings are scoped, leaving the safety profile unstated.

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; the purpose and validation dimensions are front-loaded, with the return behavior second. Nothing is restated from the title or 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?

For a three-parameter analysis tool with no output schema, the description covers what it does and what it returns, which is the key missing structured information. It stops short of stating input constraints (e.g., supported config formats, size limits) or how it relates to the other review_* tools.

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 pipelineType, pipelineConfig, and context are already documented, including the platform enum. The description adds no syntax or format detail 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?

States a specific verb (validate) and resource (CI/CD pipeline configuration) and enumerates the review dimensions (correctness, security, efficiency, best practices). The resource is narrow enough to be told apart from review_code, review_architecture, and review_infra_config, though no sibling is named 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 implied by the narrow resource — an agent can infer this applies to pipeline configs rather than general code or infra. However, there is no explicit when-to-use guidance, no exclusions, and no routing advice against the neighboring review_* and review_infra_config 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?

With no annotations, the description carries the full burden. It usefully discloses that the return is a structured prompt producing severity-tagged, file:line-anchored findings across five areas — a genuinely non-obvious behavioral trait. However, it says nothing about permissions, mutation/reversibility (a review tool is implicitly read-only but this is unstated), or any limits, leaving part of the safety profile undocumented.

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 followed by the return shape. Every clause earns its place 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?

There is no output schema, so the description correctly compensates by describing the return value (a structured prompt with severity-tagged findings), and all parameters are schema-documented. The only meaningful omission is when to choose this over the other review_* 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 all five parameters are already documented. The description's enumeration of review dimensions maps onto the focusAreas enum but adds no format or syntax detail beyond what the schema provides, 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 (review) and a precise resource (pull request or diff), and enumerates the dimensions it covers. This is cleanly distinguishable from siblings like review_architecture, review_cicd_pipeline, and review_infra_config, which target different artifacts.

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 artifact named (a PR or diff), but there is no explicit when-to-use framing, no exclusions, and no routing to alternatives such as review_architecture or analyze_bug. An agent can infer intent but gets no decision 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?

With no annotations, the description carries the full burden. It discloses that the return is a structured prompt containing anchored findings plus remediation, which is genuinely useful output-shape context for an analysis tool, but it says nothing about determinism, permission/auth requirements, size limits, or how the review is performed.

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. The capability scope leads and the return contract follows, so an agent gets the essentials in the first sentence.

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 three-parameter, no-annotation, no-output-schema analysis tool, the description covers supported inputs and the shape of the result adequately. It falls short only on operational constraints such as input size limits or multi-file handling, which an agent might need before invoking it.

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 all three parameters (config, configType, targetEnv) carry their own descriptions and an enum, so the schema does the heavy lifting. The description adds no format, size, or syntax guidance beyond what the schema already 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?

Specific verb (review) plus a precisely enumerated resource scope: Kubernetes, Docker, Terraform, Helm, and CloudFormation configurations, across four named quality dimensions (correctness, security, reliability, cost). This distinguishes it from generic siblings like review_code and review_architecture 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?

The scope implies when the tool applies (you have an IaC/config file to audit), but there is no explicit when-to-use, when-not-to-use, or routing guidance versus review_code, review_cicd_pipeline, or deployment_readiness_check. Usage is inferable rather than stated.

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

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

With no annotations, the description carries the full burden, but it does disclose an important trait: the tool 'returns a structured prompt' rather than writing files directly, which prevents a common misuse assumption. It stops short of stating side effects, permissions, or whether anything is persisted.

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 scope, followed by the return value. The enumerated output list earns its place by telling the agent exactly what a call produces.

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 9-parameter generator with no output schema, the description does a solid job explaining the deliverable (file tree with full source) and the standards applied. It omits usage routing and prerequisite context, but the core contract is 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 coverage is 67%, so the schema already documents most parameters. The description maps its standards list (packaging, logging, Kafka, Consul config, OpenAPI, error handling) to the capabilities concept, adding modest value, but it doesn't explain requirements like the serviceName pattern or how capabilities drive boilerplate.

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 ('Scaffold a new Comviva Spring Boot microservice') with an explicit scope of company standards. It is clearly distinguishable from the generic generate_code sibling by naming the artifact and the standards it follows, though it never explicitly differentiates itself.

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 when-to-use, when-not-to-use, or alternatives are given. The scenario (creating a new service) is inferable, but there is no guidance on how this differs from generate_code, recommend_architecture, or generate_implementation_plan.

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