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

With no annotations, the description carries the full behavioral burden, and it does disclose one important trait: 'read-only sandbox mode,' which reassures the agent no mutations occur. It adds nothing about cost, latency, model-tier implications, or that astra requires confirmation, so the disclosure is only partial.

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, stating the action and its scope immediately. It is efficient, though arguably too terse given the tool's complexity.

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 6-parameter tool with no annotations and no output schema, the description omits the astra confirmation requirement, guidance on choosing a model, and anything about what the analysis returns. An agent could call it, but not with full understanding of the parameters' significance.

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. The description adds no parameter-level meaning beyond the schema, e.g., nothing about the user_confirmed/astra gating or how reasoning_effort affects analysis depth.

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 scopes the resource precisely: architectural, dependency, and structural code analysis. However, it never distinguishes itself from the close sibling codex_review_code, so an agent cannot tell them apart on description 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?

There is no when-to-use, when-not-to-use, or named alternative. The phrases 'deep' and 'architectural' hint at the intended scope but leave the routing decision against codex_review_code/codex_consult to inference.

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, and it discloses almost nothing behavioral: no cost/latency expectations, no mention that the top-tier 'astra' model requires a mandatory user_confirmed flag, and no statement about what the call returns. For a tool that proxies an external model with tiered, gated options, this is a significant transparency 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 front-loaded sentence with zero filler; the verb and the evaluative scope land immediately.

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 100% schema coverage the inputs are adequately covered, but there is no output schema and the description says nothing about the shape or nature of the returned second opinion, nor about the astra confirmation flow that the schema implies. Adequate but with clear gaps for a 6-parameter 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 all six parameters are already documented in the schema, and the description adds no parameter-level detail beyond what is there. 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 (Consult) and resource (OpenAI Codex) plus the class of artifact it evaluates (architecture plans, refactoring strategies, trade-offs). This distinguishes it from codex_review_code and codex_debug_error reasonably well, but it never names a sibling explicitly, so the boundary against codex_analyze 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?

The description implies a usage context — you consult it for a 'second opinion' on plans and trade-offs — which suggests it is a higher-level advisory call rather than a code-level review. However, it offers no explicit when-to-use/when-not guidance and never contrasts itself with the five sibling 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 never states that this is a read-only external consultation that returns suggestions rather than applying fixes, nor does it disclose that data is sent to OpenAI Codex, nor any confirmation/auth requirement (the astra-tier confirmation lives only in 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?

A single front-loaded sentence with no filler; the purpose and outcome land immediately. It is slightly under-specified rather than bloated, which is not a conciseness failure.

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 7-parameter tool with no annotations and no output schema, one sentence is thin: the description says nothing about the returned diagnosis format, latency/cost implications of the model tiers, or when the user_confirmed gate must be set. The schema covers parameters, but behavioral context is largely 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 the model tiers, reasoning_effort default ("xhigh"), file_paths, context, and user_confirmed gate are all already documented in the schema. The description adds no parameter 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 gives a specific verb ("diagnose") plus the resource class (errors, exceptions, stack traces, unexpected test/build failures) and the outcome (suggest solutions). It is clearly more than a restatement of the name, but it never distinguishes itself from overlapping siblings such as codex_analyze or codex_consult.

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 noun list ("when you have an error"). There is no explicit when-to-use, no exclusion, and no routing to alternatives even though siblings codex_analyze and codex_consult plausibly overlap with debugging-by-consultation.

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 does disclose one key behavioral trait: it operates in read-only sandbox mode. However, it omits other important traits such as return format, whether it writes files, or synchronous/asynchronous 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?

A single, front-loaded sentence with no wasted words. Every part of the sentence contributes to understanding the tool's function.

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 code-synthesis tool with no annotations and no output schema, the description is minimal. It conveys the core action and sandbox mode, but lacks details about the return value, execution model, or how context files are used.

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. The description adds no parameter-specific information, 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 (synthesize) and resource (complex code, algorithms, structural refactorings, boilerplate), making clear it is a code-generation tool. However, it does not explicitly distinguish itself from siblings like codex_analyze or codex_review_code, so it falls 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?

The description implies usage by listing what can be synthesized, but offers no explicit when-to-use or when-not-to-use guidance relative to the sibling tools. No alternatives are mentioned.

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

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

With no annotations provided, the description carries the full burden. It does disclose one meaningful behavioral trait: the run is read-only, so the agent knows the repository will not be mutated. It says nothing about cost, latency, external service invocation, or auth requirements.

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 that front-loads the action and packs purpose, target scope, engine, and execution mode without a wasted clause. Appropriately sized for the purpose statement it delivers.

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 invokes an external model with six parameters and no output schema, yet the description never says what a review returns or that it may be slow or costly. The parameter surface is fully covered by the schema, but the operational context around an AI-backed review is thin.

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, enums, and the astra user_confirmed gate. The description adds only the loose mapping of 'uncommitted changes or a specific git diff' to the scope parameter, which is baseline-level value.

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 resource (uncommitted repository changes or a specific git diff) and names the underlying engine. It does not explicitly distinguish itself from the close sibling codex_analyze, so an agent must infer the boundary between 'review' and 'analyze'.

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 phrase ('uncommitted repository changes or a specific git diff') implies when this tool applies, which is useful. However, there is no explicit when-not-to-use guidance and no mention of the siblings a caller should prefer for status, debugging, or implementation work.

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 disclosure burden. The verb "Get" implies a safe read and it does surface an unusual behavioral element (Astra governance/approval rules being part of the returned state), which is genuinely useful. It does not state whether the values are live or cached, or whether reading has any cost or 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?

A single sentence that front-loads the verb and the primary resource, then enumerates the returned fields with a parenthetical example. No filler, though the enumeration makes it slightly dense for one 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?

There is no output schema, so the description must convey what comes back; it does so by naming the configuration, active model, reasoning effort, and governance policies. Combined with the empty input schema, an agent has enough to call it correctly, only missing staleness/live-state semantics.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a 0-param tool 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?

Clear verb ("Get") plus a specific resource (active Codex CLI configuration) with an enumeration of what is returned: model, reasoning effort, governance policies. It is obviously distinct from the action-oriented siblings (codex_implement, codex_review_code, etc.), but it 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?

Usage is implied by the name and no-arg read nature — an agent inspects current state before invoking the sibling actions. However, the description states no conditions, prerequisites, or alternatives, and does not say when a status check is warranted versus just running an action tool.

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