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

With no annotations, the description fully bears the burden. It clearly states Claude is advisory only (read-only, never modifies files), and that results include a session_id for continuation. This gives a complete behavioral picture.

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?

Single paragraph of 6 sentences, front-loaded with purpose and usage, then behavioral disclosure, then sibling references. Every sentence adds value; no wasted words.

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

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

Given 5 parameters and no output schema, the description covers all essential aspects: purpose, usage, behavior, parameter hints, and continuation mechanism. It adequately references siblings for alternative scenarios.

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 descriptions for all 5 parameters. The description adds value by explaining the purpose of parameters in context (e.g., session_id continues conversation, workspace_dir should be reused). Slightly above baseline due to contextual hints.

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 ('ask') and resource ('Claude') with specific purposes: co-analysis, second perspective, or knowledge. It distinguishes from siblings by mentioning alternatives like claude_review_files for file-specific questions and claude_panel for structured multi-perspective reviews.

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 describes when to use: architecture trade-offs, tricky bugs, unfamiliar APIs, or independent expert view. Provides clear exclusions: prefer claude_review_files for file-specific questions and claude_panel for structured multi-perspective reviews.

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 discloses that incorrect workspace_dir causes session not found, and that answering questions unblocks better conclusions. However, does not mention side effects, session lifetime, or what the response looks like. Adequate but not rich.

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, each provides necessary information without redundancy. Front-loaded with primary purpose, then key constraints, then special use case. 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?

Handles core usage well: how to continue session and handle questions. No output schema, so description could elaborate on expected return, but for a continuation tool the behavior is fairly straightforward. Misses guidance on session persistence or error cases beyond workspace mismatch.

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 descriptions, so baseline 3. Description adds context: explains the relationship between session_id and workspace_dir, and the purpose of answering questions. For model and stance, it doesn't elaborate beyond schema, but overall adds 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?

Clearly states it continues an existing Claude conversation, distinguishing it from siblings like ask_claude (starts new), claude_debate_* (debates), and claude_review_* (reviews). The verb 'continue' plus resource 'session' is specific.

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?

Provides explicit when-to-use: when you have a session_id and follow-up message. Mentions workspace_dir must match original, otherwise session not found. Also advises using this tool to answer questions from previous response. Lacks explicit 'do not use if' scenarios but is clear enough.

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 that Claude independently verifies verifiable items, returns per-claim rulings and counter-claims, never modifies anything, and includes a specific note about the result footer's format field. Minor omissions: no mention of authentication needs or rate limits, but overall transparent.

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 fairly long but information-dense. It front-loads the primary purpose and then provides necessary details. Every sentence adds value, though some minor trimming could be done. No wasted words.

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

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

For a complex tool with no output schema and multiple parameters, the description is remarkably complete. It explains the debate flow, evidence submission, verification process, output parsing instructions, and cost considerations. It covers all essential aspects for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the purpose of each parameter in context (e.g., 'workspace_dir' becomes Claude's working directory) and elaborates on the evidence items (types, claim, ref). It does not repeat schema details but augments them.

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's purpose: 'Open a structured, evidence-based debate with Claude about a significant decision.' It specifies the action (open a debate), the resource (Claude), and the context (evidence-based). It distinguishes from siblings by mentioning 'claude_debate_reply' for continuation.

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 states when to use this tool: 'architecture decisions, risky changes, and security-sensitive work, not routine questions.' It also notes that it is 'Expensive and slow' and directs users to 'Continue rounds with claude_debate_reply.'

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 key behavioral traits: verification of evidence before rebutting, convergence criteria, and handling of result format (json vs prose). This goes beyond basic expectations and aids correct invocation.

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 well-structured, front-loading the core action and then providing step-by-step guidelines and a warning. Every sentence is informative without being verbose, 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?

Given the tool's complexity (multiple response actions, convergence logic, format handling) and no output schema, the description covers most necessary aspects. Minor gaps exist (e.g., error handling, structure of remaining_disputes), but overall it provides sufficient context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, providing descriptions for each parameter. The description adds workflow context (e.g., 'session_id from claude_debate_open or the previous debate round') and clarifies usage of the responses parameter ('Accept or rebut Claude's prior claims.'), enhancing understanding beyond the schema alone.

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 'Continue an open evidence debate' using a specific verb and resource. It distinguishes from siblings like claude_debate_open (which starts a debate) by focusing on continuation and accept/rebut 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 provides explicit guidance on when to use the tool (to accept or rebut counter-claims) and includes convergence rules (stop when remaining_disputes empty or after three rounds). It does not list exclusions or alternative tools, but the context is clear given the 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 provided, the description effectively explains behavioral traits: each perspective is a separate full Claude run, concurrency, latency scaling, and non-modification guarantee. It could add error handling or edge cases but is sufficient for safe usage.

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

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

The description is a single, well-structured paragraph. It front-loads the main purpose, then adds essential behavioral and usage details without redundancy. 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?

Given the complexity (6 parameters, no output schema, no annotations), the description covers most critical aspects: operation, concurrency, file reading, non-modification. It lacks information about the returned report format, but this is minor.

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 each parameter having a clear description. The tool description reinforces the 'paths' instruction but does not add substantial new meaning beyond the schema. Baseline of 3 is appropriate.

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

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

The description explicitly states the tool's purpose: 'Run a multi-perspective Claude review panel in ONE call' detailing how it aggregates multiple expert analyses. It distinguishes from sibling tools like ask_claude and claude_second_opinion by emphasizing the multi-perspective aspect.

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 clearly specifies when to use: 'when the user asks for verification or review from multiple perspectives or with sub-agents'. It also notes that Claude 'only advises, never modifies files' and instructs to 'provide absolute paths when the panel should read code from disk'. However, it lacks explicit mention of when not to use or direct references to 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?

Given no annotations, the description carries full burden. It clearly states that the tool runs read-only git commands and that Claude never modifies anything. It also discloses behavioral traits such as the 'deep' depth mode being slower and higher usage. However, it does not mention error handling or performance 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?

The description is concise and well-structured, front-loading the primary purpose and then elaborating on key parameters and usage context. 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?

While the description covers usage well and mentions the result footer for session continuation, it does not explicitly describe the output format (e.g., what the review looks like) since no output schema exists. A bit more detail on the output would improve completeness.

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 context about default behavior and the purpose of the 'base' parameter, but does not significantly enhance understanding beyond what the schema already provides for each parameter.

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's purpose: reviewing actual code changes via read-only git operations. It specifies the verb 'review', the resource 'code changes', and distinguishes from the sibling tool 'claude_review_files' by noting that this tool requires git context.

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 mentions when to use ('after implementing something'), explains default behavior (review uncommitted changes against HEAD), describes how to use the 'base' parameter for comparing against a ref, and recommends an alternative tool for non-git reviews. Also states that Claude only advises and never modifies anything.

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 full burden. It explicitly states read-only behavior: 'read-only: it can Read, Glob, and Grep within the granted paths, and research the web, but never modifies anything'. It also explains the depth parameter's delegation behavior. This fully discloses behavioral traits.

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

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

The description is four sentences, each earning its place: first sentence states purpose and read-only nature, second gives requirements, third compares to sibling, fourth gives use case. Front-loaded with key information, 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?

Given 6 parameters (2 required), no output schema, and no annotations, the description covers purpose, usage, behavioral constraints, parameter details, and examples. It addresses the tool's complexity adequately, providing enough context for an AI agent to select and invoke 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 baseline is 3. The description adds substantial meaning beyond the schema: explains 'depth' effect, 'model' override, requires 'absolute paths that exist', gives example question, and explains 'session_id' and 'workspace_dir' for continuing conversations. It enriches parameter understanding significantly.

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's purpose: 'Have Claude read and analyze specific files or directories agentically'. It specifies the verb (read and analyze) and resource (files/directories), and distinguishes from sibling tool ask_claude by noting it's better for larger scopes and verification workflows.

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 explicit context for when to use: 'Better than pasting file contents into ask_claude for anything larger than a snippet' and 'For verification workflows, this gives Claude an independent read'. It implies when not to use (small snippets) by contrasting with ask_claude. No explicit when-not list, but adequate 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 provided, the description fully discloses behavioral traits: Claude is instructed to find flaws, assumptions, missed edge cases; it never modifies files; it explains the result format (JSON vs prose) and how to parse the footer. No contradictions.

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 comprehensive but slightly lengthy; however, every sentence provides essential information (purpose, usage, result format). It is well front-loaded with the core function and structured logically. Could be marginally trimmed but is effective.

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 (5 parameters, no output schema), the description covers all necessary aspects: how to invoke, what to expect in return, how to handle different result formats, and even how to continue sessions. It prepares the agent for both JSON and prose responses, ensuring complete usability.

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 each parameter. The description adds value by explaining the adversarial purpose, the importance of providing full reasoning, and how session_id/workspace_dir are used for continuity. This extra context elevates it above 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?

The description clearly states the tool provides an adversarial review of the user's own analysis, explicitly distinguishing it from siblings like ask_claude and claude_debate_open by emphasizing it hunts for flaws rather than agrees. It also specifies it never modifies files, reinforcing its unique role.

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

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

Explicit guidance on when to use (risky changes, migrations, security-sensitive edits, low confidence) and how to integrate with sub-agents. It contrasts with other tools by positioning itself as an independent cross-model reviewer, and explains when to treat disagreements as high-signal.

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?

Discloses a key behavioral trait: the ledger is in-memory and resets on server restart, which is critical for understanding session persistence. With no annotations provided, the description carries the full burden and delivers clear, honest behavior beyond the basic listing function.

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 concise sentences: first explains output and ordering, second provides usage guidance and a behavioral note. No wasted words, front-loaded with core functionality.

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 tool's simplicity (2 optional params, no output schema, no annotations), the description covers purpose, output fields, usage context with sibling tools, and the transient in-memory nature. Complete for effective agent invocation.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds no new meaning to the parameters beyond what the schema already provides (e.g., limit defaults to 10, workspace_dir is an optional filter). No extra context 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 the tool lists recent Claude conversations with specific fields (session_id, originating tool, workspace_dir, topic excerpt) and orders them newest first. It distinguishes itself from sibling tools like claude_continue and ask_claude by focusing on listing existing sessions.

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 tells when to use: when you lost a session_id or want to continue earlier work. Also provides guidance on next steps: pick the session and resume with claude_continue using the same workspace_dir. This contrasts with starting new sessions via ask_claude.

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