Parallel Task MCP

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

The description adds significant behavioral context beyond annotations: it explains the multi-turn research capability with interaction_id inheritance, specifies that the tool returns a URL to share, and instructs not to poll results unless instructed. Annotations cover basic hints (readOnlyHint=false, etc.), but the description enriches this with practical workflow details like stopping after calling and context inheritance for follow-ups.

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 and front-loaded with key usage guidelines. Each sentence adds value, such as contrasting with alternatives, post-call instructions, and multi-turn research details. It could be slightly more concise by integrating some details more tightly, but overall, it avoids waste and is appropriately sized 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?

Given the tool's complexity (multi-turn research, multiple parameters) and lack of output schema, the description is highly complete. It covers purpose, usage guidelines, behavioral traits, parameter semantics, and workflow instructions. It compensates for the missing output schema by explaining what to do with the response (share URL) and how to handle follow-ups, making it sufficient for an agent to use the tool effectively.

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?

With 100% schema description coverage, the baseline is 3. The description adds value by clarifying parameter usage: it explains that 'input' should be 'specific and detailed for better results,' advises on 'processor' usage ('Only specify if user explicitly requests'), and details how 'previous_interaction_id' enables multi-turn research with context inheritance. This provides semantic context beyond the schema's technical descriptions.

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 'Creates a Deep Research task for comprehensive, single-topic research with citations,' specifying both the verb (creates) and resource (Deep Research task). It distinguishes from siblings by explicitly contrasting with 'Parallel Search MCP for quick lookups' and 'batch data enrichment,' making the purpose specific and differentiated.

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 this tool ('USE THIS for analyst-grade reports, NOT for batch data enrichment') and when not to ('Use Parallel Search MCP for quick lookups'). It also includes detailed instructions on post-call behavior ('share the URL with the user and STOP') and multi-turn usage scenarios, offering clear alternatives and 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?

The description adds valuable behavioral context beyond annotations. While annotations indicate this is a non-destructive, non-idempotent write operation, the description specifies that it returns a URL to share with users and instructs not to poll for results. This provides crucial workflow guidance that isn't captured in the structured 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 extremely concise and well-structured. The first sentence establishes purpose, the second provides usage criteria, and the third gives clear post-call instructions. Every sentence serves a distinct purpose with zero 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 batch processing tool with comprehensive schema coverage and annotations, the description provides exactly what's needed: clear purpose, specific usage criteria, and important behavioral guidance about the URL sharing and non-polling approach. The absence of an output schema is compensated by the description's instruction to share the returned URL.

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?

With 100% schema description coverage, the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific details beyond what's in the schema. It focuses instead on usage context, which is appropriate given the comprehensive schema documentation.

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: 'Batch data enrichment tool' with specific context of processing 'a LIST of items' to get 'same data fields for each'. It distinguishes itself from siblings by focusing on batch processing rather than deep research or result retrieval.

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 usage instructions: 'USE THIS when user has a LIST of items and wants same data fields for each.' It also gives clear post-call guidance: 'After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed.' This directly addresses when to use and what to do afterward.

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 cover safety (readOnlyHint, destructiveHint) and idempotency, but the description adds critical behavioral context: it warns about untrusted web-sourced data and instructs not to follow instructions within returned content. This goes beyond annotations, though it doesn't detail rate limits or auth needs.

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 concise sentences, each serving a distinct purpose: stating the tool's function, providing usage timing and alternatives, and adding a security warning. No wasted words, front-loaded with core purpose.

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 annotations cover safety and idempotency, and the schema fully documents parameters, the description adds valuable usage timing, alternatives, and security warnings. However, without an output schema, it doesn't describe the markdown structure or potential errors, leaving 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%, so parameters are fully documented in the schema. The description adds no specific parameter semantics beyond implying taskRunOrGroupId is for completed tasks, which is minimal added value. 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 resource ('final task results as markdown'), specifying the output format. It distinguishes from siblings by contrasting with getStatus for polling and implying completion vs. creation tools like createDeepResearch and createTaskGroup.

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 ('Only call once task is complete') and when not to use ('If polling, use getStatus instead'), naming the alternative tool. This provides clear, actionable guidance for the agent.

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 read-only, non-destructive, and idempotent behavior. The description adds valuable context: 'Lightweight status check (~50 tokens)' discloses performance characteristics, and the polling guidance clarifies practical usage beyond 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?

Two sentences with zero waste: first states purpose and performance, second provides critical usage guidelines. Perfectly front-loaded and appropriately sized.

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 (1 parameter, good annotations), the description is nearly complete. It lacks output details (no schema), but covers purpose, performance, and usage well for a status-check 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%, fully documenting the single parameter. The description adds no parameter-specific information beyond what the schema provides, meeting the baseline for high coverage.

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 performs a 'status check' for tasks, which is specific (verb+resource). However, it doesn't explicitly differentiate from sibling 'getResultMarkdown' beyond usage guidance, missing direct comparison of what each returns.

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 'Use this for polling instead of getResultMarkdown' and provides clear when-not guidance: 'Do NOT poll automatically unless specifically instructed.' This directly addresses alternatives and usage constraints.

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