Go-Live Check

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds real context beyond that: unknown fields are ignored, no URL is fetched, nothing is stored, and the return shape matches POST /v1/analyze. It does not discuss auth or rate limits, so 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?

Five short, front-loaded sentences with no bloat; the key scope constraint (you already operate the deployment) leads. 'Nothing is for sale' is an unusual note but it does carry meaning about no upsell/monetization.

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 a rich output schema and fully documented parameters, the description only needs to frame purpose and boundaries, which it does. The reference to 'the same object as POST /v1/analyze' presumes knowledge of a separate endpoint, a minor 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% and the per-parameter descriptions are extremely detailed, so baseline is 3. The description only adds marginal clarification ('A url field is not fetched', 'Unknown fields are ignored') that lightly reinforces the headers_raw 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?

States a specific verb and resource: checks raw HTTP response headers from a deployment the caller operates. That is clearly distinct from the sibling tools (explain_finding, get_fix, report_feedback), though the description never names or contrasts them 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?

The phrase 'from a deployment you already operate' implies when the tool applies (you have the headers, you are not scanning third parties), but there is no explicit when-to-use/when-not guidance and no routing to alternatives among the siblings.

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 readOnly, idempotent, non-destructive and closed-world behavior, so the bar is lower. The description still adds real context: a url field is not fetched (relevant for an agent worried about SSRF side effects), nothing is stored, and the conditional null vs. page behavior of answer_page. The 'Nothing is for sale' line is filler rather than disclosure.

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 key facts (return shape, conditional answer_page) are front-loaded and the fragment style is compact. However, 'Nothing is stored. Nothing is for sale.' is an unexplained aside that does not earn its place, and the sentences are choppy enough to read as accumulated 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?

An output schema exists, so return values need not be re-explained, yet the description usefully clarifies the one non-obvious output case (answer_page is populated only for GLC-CSP-MISSING and GLC-FRAME-MISSING). For a single-enum-parameter lookup tool this is essentially complete; only the sibling routing is missing.

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?

There is a single parameter with 100% schema description coverage and an explicit enum, so the schema carries the semantics. The description only restates the id set and notes unknown fields are ignored, which the schema also states. 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 (explain) and resource (one finding id from a fixed set of seven) and enumerates the return shape (id, severity, why, answer_page). This is clearly distinguishable from get_fix in intent, though the description never names or contrasts the sibling tools 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: 'one finding id from the seven' tells the agent this operates on ids surfaced by analyze_headers, and the enum constrains it to that set. But there is no explicit when-to-use versus get_fix or report_feedback, and no statement of prerequisites or when not to call 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 readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds genuinely useful behavior beyond that: omitted/unknown platform yields fix null, unknown fields are ignored, and the url/source_url fields are never fetched (no network side effect).

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 opening sentence is well front-loaded and the platform list is useful. But "A url field is not fetched. source_url is not fetched." duplicates both itself and the schema, and the trailing "Nothing is stored. Nothing is for sale." is noise that does not earn 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?

An output schema exists, yet the description still sketches the return shape (id and fix, with platform/snippet/source_url/last_verified/note) and covers the error path. Given the simple two-parameter surface, nothing an agent needs to invoke it correctly is missing.

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 parameters are fully documented there, so the baseline is 3. The description largely restates the schema (platform list, null fallback, url not fetched) rather than adding new syntax or format 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?

States a specific verb and resource ("Return the registry snippet for one finding id and one platform") and enumerates the exact platform values that produce a result. This is clearly distinguishable from siblings like explain_finding, which would explain a finding rather than hand back a registry snippet.

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 constrains the input ("the platform whose config the caller can edit") and defines the null-result case, which implies usage. However, it never states when to call get_fix versus explain_finding or analyze_headers, so the agent gets no explicit routing 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?

Goes well beyond the annotations by declaring that nothing is persisted ('no file, no counter, and no log line'), that extra fields (note, url, headers) are ignored and urls are not fetched, and that the server does not infer platform from the Server header. This meaningfully clarifies the no-side-effect profile, though some sentences ('Nothing is for sale') add noise rather than behavioral clarity.

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 core constraint is front-loaded, but the prose is choppy and uneven: 'The result is id, outcome, and stored false' is awkward, and sentences like 'This server does not guess the platform from the Server header' and 'Nothing is for sale' do not clearly earn their 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 an output schema present and full annotation coverage, the description supplies enough behavioral context (no writes, extra fields ignored, url not fetched) for an agent to call it confidently. Only the missing usage routing keeps 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 coverage is 100% with both parameters documented via enums, so the schema carries the semantics. The description restates the outcome values and notes that unused fields are ignored, but adds no syntax or format detail beyond the schema, matching the 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 resources: 'Report one finding id and one outcome,' and names the three valid outcomes. The function is clearly distinct from analyze_headers, explain_finding and get_fix, though the description never explicitly differentiates itself from 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?

There is no when-to-use guidance, no prerequisites, and no mention of the sibling tools or when one should prefer this over them. The agent must infer context from the tool name alone.

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