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

No annotations are provided, so the description bears full burden. It mentions that Codex 'will challenge assumptions and surface risks' but does not disclose side effects (e.g., session modification), safety, or authentication needs. This is insufficient for a tool with no annotations.

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

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

The description is two sentences, front-loaded with the purpose. Every sentence adds value, with no redundancy or fluff.

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 8 parameters, no output schema, and no annotations, the description could provide more context (e.g., what the response contains, how to use optional params). The schema covers parameter details, but the tool's overall usage remains somewhat opaque.

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 additional meaning beyond the schema; it does not explain parameter usage or dependencies.

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 clearly states the verb ('Ask', 'brainstorm') and the resource ('GPT Codex'). It distinguishes from sibling tools like codex_debug, codex_plan, etc., by focusing on general questioning and critical thinking.

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

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

The description provides context (critical thinking partner, challenges assumptions) but does not explicitly say when to use this tool versus alternatives like codex_research or codex_plan. Usage is implied rather than explicit.

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 full burden. It discloses the multi-turn process ('hypotheses, tests... verdict with evidence chain') and time consumption. While it doesn't detail authentication or rate limits, it adequately describes the behavioral traits for a debugging tool.

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

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

The description is extremely concise: two sentences that front-load the core purpose and process, then add usage guidance. Every sentence adds value with no wasted words.

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

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

Given the complexity (10 parameters, multi-turn process, no output schema), the description provides adequate context for an AI agent to decide to use the tool. It explains the multi-turn nature and intended use cases, though it omits details about output format.

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 does not add significant meaning beyond the schema; it restates high-level concepts (symptoms, code, context) but does not elaborate on parameter usage or constraints.

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 clearly states it performs 'Structured root-cause analysis via GPT Codex' and specifies usage for 'hard-to-find bugs, unexpected behavior, or production incidents.' This distinguishes it from sibling tools like codex_ask or codex_plan, which have different purposes.

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 explicitly recommends use for debugging scenarios ('hard-to-find bugs, unexpected behavior, or production incidents') and notes time cost ('Can take minutes'). However, it does not explicitly state when not to use it or mention alternatives, though sibling names provide 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 provided, so description carries full burden. It describes the collaborative nature and output ('concrete approach with trade-offs and alternatives'), but lacks details on potential destructive actions, permission requirements, error handling, or session behavior. Adequate but not exhaustive.

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 extraneous words. Front-loaded with the core purpose. Perfectly concise for 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?

Tool has 8 parameters and no output schema. Description explains the purpose and high-level outcome but does not detail the return values (e.g., format of the plan), pagination, or behavior under different constraints. Adequate but incomplete for a comprehensive understanding.

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% (all 8 parameters described thoroughly). The description only mentions 'problem' and 'constraints', adding no additional meaning beyond the schema. With full coverage, baseline 3 is appropriate.

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

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

Description clearly states 'Collaboratively plan an implementation' and specifies the process: describe problem and constraints, get concrete approach with trade-offs and alternatives. The verb 'plan' and resource 'implementation' are specific, and it distinguishes from sibling tools like codex_ask and codex_debug.

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 for planning phases ('Describe the problem and constraints'), but does not explicitly state when not to use or list alternatives. It provides clear context for when to use, earning a 4.

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 reveals the multi-turn process and time trade-off, which are behavioral traits. However, it does not address permissions, side effects, or rate limits. The information is adequate but leaves gaps for mutation or state-management details.

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

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

The description is exceptionally concise: two sentences that front-load the core purpose, then explain the process and trade-offs. Every sentence adds value with no redundancy or 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?

Given the complexity (9 parameters, no output schema), the description explains the purpose and process but lacks details about return values, error handling, or how the session_id and working_dir interact. It also does not explicitly guide comparison with siblings beyond the speed-depth trade-off. It is functional but not 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 baseline is 3. The description adds no extra meaning beyond the schema; the parameter descriptions in the schema are already detailed (e.g., max_turns explains each turn deepens investigation). The tool-level description does not enhance parameter understanding.

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 clearly states the tool does 'deep-dive research via GPT Codex' with a multi-turn process that surveys, drills, and synthesizes. It differentiates from siblings like codex_ask and codex_debug by emphasizing depth over speed and listing specific use cases (architecture exploration, library evaluation, understanding codebases).

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?

While it explicitly lists use cases ('Use for architecture exploration, library evaluation, or understanding unfamiliar codebases'), it implies when not to use with 'Can take minutes — trades speed for depth,' but does not name specific alternative tools. The context is clear, but more direct exclusions would strengthen 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 provided, so description carries full burden. It mentions clearing history and stats but lacks detail on permanence, required permissions, or behavior when all_sessions is true.

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 fluff. Action and effect in first sentence, usage guidance in second. Perfectly concise and 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?

Given tool simplicity and full param schema coverage, description covers purpose and usage adequately. Lacks mention of irreversibility and all_sessions behavior, but overall complete for reset operation.

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

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

Schema description coverage is 100%, so baseline is 3. Description does not add meaning beyond schema for parameters; it does not explain session_id, working_dir, or all_sessions further.

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?

Description clearly states the verb 'reset' and the resource 'conversation session with Codex'. It distinguishes from siblings like codex_sessions and codex_stats by specifying it clears history and stats.

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 states when to use: 'Use when starting a new topic.' Does not mention when not to use or provide alternatives, but for a simple reset tool this is sufficient.

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 mentions external invocation to Codex but does not disclose side effects, rate limits, data handling, or whether the tool modifies anything. Basic behavioral context is missing.

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

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

The description is two concise sentences that efficiently convey the core purpose and scope. No extraneous text; every sentence earns its place.

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

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

The tool has 10 parameters and no output schema. The description does not explain the return format or structure of the review, leaving the agent to infer. While the input schema is well-documented, the output behavior is unclear.

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

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

All 10 parameters have descriptions in the input schema, achieving 100% coverage. The tool description adds marginal value beyond the schema, only providing a brief context for the review purpose. Baseline score of 3 is appropriate.

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

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

The description clearly states the action 'Send code for critical review' and lists specific issue types (bugs, security, performance, design flaws), distinguishing it from sibling tools like codex_ask and codex_debug.

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 for code review but does not explicitly state when not to use it or compare to alternatives. The context is clear but lacks exclusion guidance.

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

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

With no annotations, the description bears full burden. It discloses the tool's purpose (analyze diff, return risk score/categories/recommendation), which implies read-only behavior. However, it does not explicitly clarify side effects or limitations, but the description is sufficiently transparent for an analysis tool.

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

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

The description is two concise sentences, front-loaded with the main purpose and output. Every sentence adds value with no redundancy.

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

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

The tool has 5 parameters and no output schema. The description lists the return values (risk score, affected categories, recommendation) but does not specify types or structure. Given the complexity, it is adequate but leaves some detail gaps.

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

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

The input schema has 100% description coverage for all 5 parameters, so the baseline is 3. The tool's description does not add any extra meaning beyond the schema's parameter descriptions, so no bonus points.

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 clearly states the tool analyzes a git diff to identify risk zones and recommend collaboration depth, and specifies that it returns risk score, affected categories, and which sibling tool to use. This distinguishes it from siblings by being the risk analysis tool that guides towards others.

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

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

The description implies usage when analyzing a git diff, but does not explicitly state when to use this tool versus alternatives like codex_debug or codex_research. It mentions recommending siblings but lacks explicit when/when-not 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?

No annotations are provided, so the description must disclose behavior. It states 'List active in-memory sessions', implying a read-only operation, and indicates what is included in the output. This is clear, though it could be more explicit about non-destructive nature or lack of side effects.

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

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

Two sentences: first defines purpose, second provides usage context. No extraneous information. Every sentence serves a clear 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?

The tool has one optional parameter, no output schema, and no annotations. The description covers its core function and usage context adequately for a simple listing tool. Could benefit from mentioning output format or ordering, but not essential.

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% for the single parameter 'limit', with a detailed description in the schema. The tool description does not add additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.

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

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

The description states 'List active in-memory sessions with recency and activity summary' which is a specific verb (List) and resource (active in-memory sessions), and adds detail about what the output includes (recency and activity summary). It clearly distinguishes from siblings like codex_ask or codex_debug.

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 includes 'Useful when running multiple clients against one bridge process', providing clear context for when to use the tool. No exclusions or alternatives are mentioned, but no other sibling tool overlaps in functionality, so the guidance is sufficient.

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. The verb 'Get' and 'Shows' imply a read-only operation with no side effects. While not explicitly stating 'read-only', the description reasonably conveys behavioral traits for a statistics retrieval tool.

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

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

Two sentences, front-loaded with the main purpose, immediately followed by specific output details. No fluff or unnecessary information; every sentence earns its place.

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

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

For a simple tool with two optional parameters and no output schema, the description sufficiently explains the tool's purpose and outputs (exchanges, latency, per-tool usage). Complete for the given complexity.

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

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

Schema description coverage is 100% with both parameters (session_id, working_dir) having clear descriptions. The tool description does not add any additional meaning beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description clearly states the verb 'Get' and the resource 'round-trip statistics for this session's Claude ↔ Codex collaboration'. It lists specific outputs (exchanges, latency, per-tool usage), which distinguishes it from sibling tools like codex_ask or codex_debug that perform different actions.

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 context ('this session's Claude ↔ Codex collaboration') but does not explicitly state when to use this tool versus alternatives or provide any exclusions. No guidance on when not to use or alternative tools.

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