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

With no annotations, the description must cover behavioral traits on its own. It discloses modes and DeepThink support, but omits output format, conversation continuity defaults, and the fact that smartSearch is quick-mode-only (which is 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?

Two tightly written sentences; the main action is front-loaded and the mode summary earns its place. 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?

The description plus schema cover the input space, but there is no output schema and no mention of what the response looks like. It also doesn't route the agent to specialized siblings, so a user needing code review might still pick this 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 the baseline is 3. The description mostly restates the mode enum and adds the 'with search+upload' capability, but it doesn't clarify any parameter beyond what the schema already documents.

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 clear verb and resource: 'Send a prompt to DeepSeek and return the AI response.' It does not explicitly contrast with sibling tools like deepseek_code_review or deepseek_debug, so it falls short of a full 5, but the general-purpose intent is unmistakable.

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 mode descriptions imply use cases (quick for default, expert for reasoning, vision for image analysis), but there is no explicit guidance about when to choose this general chat tool versus the specialized deepseek_* siblings, nor any exclusions.

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. It does openly state that code is sent to DeepSeek and that analysis is returned, which is the core behavior. However, it omits important context for a tool that transmits source code: no mention of privacy/data-handling implications, auth requirements, response format, or whether the call is synchronous. These are meaningful 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?

A single, front-loaded sentence states the action and the concrete review areas without fluff. Every clause contributes essential information (send, target, analysis categories), making it appropriately sized and easy to scan.

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 stateless tool, the description plus fully-documented schema is enough to invoke it correctly: provide code, optionally language and deepthink, and expect an analysis. There is no output schema, so the described return content ('potential bugs, performance issues, and improvement suggestions') covers the response. Minor incompleteness from missing sibling differentiation and response format is already accounted for in other dimensions, so this dimension remains adequate.

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 schema already documents 'code', 'language', and 'deepthink' adequately. The tool description adds no additional meaning or usage detail for these parameters, so no reason to exceed the baseline.

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 to DeepSeek for review') and the deliverable ('analysis of potential bugs, performance issues, and improvement suggestions'). It is a specific verb+ressource+output combination. However, it does not explicitly distinguish itself from siblings like deepseek_debug or deepseek_explain, 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 context of when to use this tool is implied: an agent would use it when code review is needed. But there is no explicit guidance on when to prefer this over deepseek_debug, deepseek_explain, or deepseek_evaluate_idea, and no 'when not to use' conditions. This leaves the choice 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?

With no annotations provided, the description must carry the behavioural burden. It does disclose that the tool 'suggests' causes and fixes rather than applying them, and 'likely' conveys uncertainty. However, it does not mention limitations such as not executing code, not modifying files, or the nature of the output beyond a text suggestion.

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 entire description is a single front-loaded sentence with no filler, repetition, or unnecessary detail. Every word contributes to the core purpose, making it highly efficient for an agent to parse.

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 schema covers parameters well and the tool is conceptually simple, but the description omits sibling differentiation and does not state any behavioral constraints. An agent can infer basic invocation, but the lack of usage guidance and output expectations leaves some context incomplete.

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. The description adds minimal parameter context ('error message' maps to error, 'related code' maps to code), but it provides no additional semantic value beyond what the input schema already states.

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 uses a specific verb ('Analyse') with a clear resource ('an error message and related code') and a clear outcome ('suggest likely causes and fixes'). It is not a tautology and conveys meaning, but it does not explicitly differentiate from sibling tools like deepseek_explain or deepseek_code_review.

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 is given about when to prefer this over deepseek_explain, deepseek_code_review, or other siblings. The phrasing implies it is for error scenarios, but there are no explicit conditions, exclusions, or alternative routing.

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 present, so the description carries the burden of explaining behavior. It does state the evaluation axes, which adds some context beyond the name, but it does not disclose whether the output is a score, prose assessment, or structured report, nor does it mention potential latency or DeepThink implications.

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, compact sentence that immediately communicates the tool's purpose and evaluation criteria. There is no redundant wording or unnecessary detail.

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 definition is sufficient for basic invocation since the one required parameter is well-documented. However, with no output schema and no annotations, an agent still cannot tell what form the evaluation will take or how detailed the result will be, so the description is 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%, and each parameter already has a clear description. The tool description adds no additional parameter-level nuance, but the baseline of 3 is appropriate because the schema fully documents the parameters.

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 uses a specific verb ('Evaluate') and identifies a clear resource ('technical idea or research proposal') with explicit criteria ('innovation, feasibility, and potential impact'). This clearly distinguishes it from sibling tools like deepseek_code_review, deepseek_explain, and deepseek_chat.

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: use this tool when you want an assessment of an idea or proposal. However, it does not explicitly state when not to use it, nor does it compare against alternatives such as deepseek_chat for open-ended discussion or deepseek_code_review for code-specific feedback.

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 present, so the description must carry behavioral disclosure. It does state the primary behavior—returning an explanation at a chosen level—and the schema adds the DeepThink toggle, but it does not describe output format, length expectations, or whether any external side effects exist.

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?

One tight sentence that puts the main action and object first, with no filler or redundant boilerplate. Every phrase contributes to understanding what the tool does.

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 one required parameter and a fully documented schema, the description is mostly complete. The only real gap is the lack of explicit sibling differentiation and an output-shape statement, but these are relatively minor given the tool's low 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?

The input schema documents all three parameters with 100% coverage, including valid enum values and defaults. The description adds little 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?

Names a specific action (explain) and a clear resource scope (concept, code snippet, or text), plus a distinguishing feature (difficulty level). It is broadly clear, but it does not explicitly contrast with close siblings like deepseek_summarize or deepseek_chat, leaving some ambiguity.

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 use for explanation-style queries, which is distinct from debugging or code review, but it gives no explicit guidance on when to prefer this tool over siblings. An agent would have to infer the boundary between 'explain' and 'summarize' or 'chat'.

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 the core behavior—condensing long text into key points—and the optional max_length constraint, but it does not mention how max length is enforced or any caveats about input size 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?

A single sentence of 12 words that front-loads the primary action and outcome. Every word earns its place with 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 a simple summarization tool with no output schema, the description is mostly complete: it states what the tool does, the expected output ('key points'), and the optional constraint. The main gap is the absence of usage context relative to sibling tools, but this is minor given the tool's low 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%, so the schema already documents all three parameters. The description only adds that max_length is optional, which is already encoded in the schema, providing minimal extra 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?

The description uses a specific verb ('Summarize') and resource ('long text'), and specifies the outcome ('key points'). It is clearly distinct from sibling tools like deepseek_chat, deepseek_explain, and deepseek_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 offers no guidance on when to use this tool versus its siblings. It does not mention conditions for use, exclusions, or alternatives such as deepseek_explain or deepseek_chat.

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