mcp-server

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds the return format (up to 10 scans with specific fields) and limits, which is valuable behavioral context beyond the raw 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 a single, well-structured paragraph. Every sentence serves a purpose: defining the tool, specifying when to use, and guiding the flow. No unnecessary 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 simple tool with 1 parameter and no output schema, the description is fully complete. It covers purpose, usage context, and integration with sibling tools. No gaps remain.

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 the description does not need to explain parameters. The email parameter already has clear format and description in the schema. The description adds no additional parameter semantics beyond what the schema provides.

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 'look up' and resource 'recent SEO scans by email'. It provides specific context for returning users without a scan_id, distinguishing it from siblings like get_remediation_plan or purchase_report.

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: for returning users without a scan_id, in a fresh session. Provides clear instructions to ask for email first, then call this, and outlines subsequent steps (get_remediation_plan or purchase_report).

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 declare readOnlyHint, idempotentHint, destructiveHint. Description adds behavioral context: lightweight (doesn't download plan), polling interval, state transitions. No contradiction.

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, front-loaded with purpose. Every sentence adds value: purpose, usage pattern, state machine explanation. No 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?

Given simple parameter set, no output schema, and sufficient annotations, the description covers all needed context: polling flow, state transitions, and next steps. Complete for agent usage.

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 `scan_id` description already present. Description does not add additional meaning beyond the schema for the parameter, but implicitly notes source of scan ID. 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 tool is a lightweight poll to check if a paid remediation report is ready, explicitly distinguishing it from downloading the full plan. It specifies the verb and resource (check status of scan's paid report).

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 guidance: use after `purchase_report`, poll every 15 seconds until both `paid` and `runbook_ready` are true, then call `get_remediation_plan` once. Also explains the meaning of status fields (`paid`, `runbook_ready`).

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 declare readOnlyHint=true and idempotentHint=true. The description adds important behavioral context: the plan content (priority issues, fixes, ASK THE OWNER stops, verification step), the need to save to file, and that the tool may return 'generating' or 'paid=false' states. No contradiction with 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 somewhat long but front-loaded with the main purpose. Every sentence provides useful information, though it could be slightly more concise without losing content. Well-structured.

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?

Despite having no output schema, the description fully explains the possible return states (paid, unpaid, generating) and the expected agent behavior (save file, execute steps, ask owner, verify). It covers all necessary context for the tool's usage.

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% coverage for the single required parameter scan_id, with a clear description. The tool description adds minimal extra meaning beyond the schema, only noting the scan_id should be from a purchase. 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 tool retrieves a paid remediation plan for a purchased scan as a Markdown runbook. It distinguishes from sibling tools like purchase_report and submit_scan by specifying when to use those alternatives.

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 instructs the agent to use this tool only after a scan is purchased, and provides guidance for edge cases: if not purchased (call purchase_report), if generating (retry later), and what to do with the output (save and execute).

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 (readOnlyHint, etc.) are supplemented with detailed behavioral context: free vs paid tier truncation, truncated flag, upgrade flow, and output field usage recommendations. 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?

Description is lengthy but well-structured with numbered steps. Every sentence adds value for the agent. Slightly verbose but not excessive.

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 no output schema, description fully explains return structure, tier behavior, truncation, upgrade handling, and user presentation guidance. Complete for the tool's 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?

Single parameter (scan_id) is well-documented in schema. Description adds no additional param-level details beyond schema. Schema coverage is 100%, 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?

Title 'Get SEO Scan Results' and description clearly define it fetches structured findings. Distinguishes from siblings by focusing on retrieving results rather than submitting scans or checking status.

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 (after scan completes) and provides detailed instructions on how to summarize results. Lacks explicit mention of when to use alternatives like get_scan_status.

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 declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds valuable behavioral context: polling interval (3-5 seconds), expected completion time (5-15 seconds), and status-based next steps. It does not contradict annotations. Slightly more could be said about rate limits or error handling, but it's already strong.

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, with two sentences that front-load the purpose and immediately provide operational details. Every sentence adds value, and there is no 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?

Given the simple tool (one required parameter, no output schema), the description covers all necessary context: how to use it, what to expect, and what to do with the results. It is complete for the agent to 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 coverage is 100% with a clear description for `scan_id`. The description adds context that `scan_id` comes from a prior `submit_scan` call and specifies the polling usage pattern. This enriches the parameter semantics 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 the tool checks whether a queued scan has completed. It distinguishes itself from siblings like 'get_scan_results' (which returns structured findings) and 'submit_scan' (which queues a scan). The verb 'check' and resource 'scan status' are specific and unambiguous.

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: as a lightweight polling endpoint called every 3-5 seconds with the `scan_id` from `submit_scan`. It also tells what to do upon completion or failure, and directs to `get_scan_results` for structured findings. This effectively differentiates it from alternative actions.

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 declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that it takes no arguments and returns a guide, and notes that content is same as the 'getting_started' prompt. 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?

Two sentences, front-loaded with the main purpose, no wasted words. Perfectly concise.

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 tool with no parameters, no output schema, and a straightforward return of a usage guide, the description provides all necessary context. Complete and self-sufficient.

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?

No parameters; baseline is 4. Description reaffirms 'takes no arguments', which is helpful for confirmation.

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 returns a plain-English usage guide for the server, including example requests, what it asks the user, and available tools. Distinguishes itself from sibling tools by being a meta-tool for orientation.

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 says 'Call this if the user asks how to use Abby SEO, or to orient yourself before starting.' Provides clear when-to-use and context 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 indicate openWorldHint=true (side effects) and non-idempotent, which the description confirms by detailing that it initiates a purchase and returns a checkout URL. It adds critical context: payment must be done externally, the tool does not collect payment, and the autonomous flow for polling and follow-up. No contradictions with 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 front-loaded with purpose and proceeds logically to detailed instructions. While it is relatively long, every sentence adds value, and the numbered flow improves clarity. Minor conciseness improvement could be achieved by trimming redundant phrasing, but overall well-structured.

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 (purchase initiation, external payment, polling, conditional behavior), the description fully covers all necessary information. No output schema exists, but the description explains return values (checkout_url and already_purchased flag) and the entire workflow.

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 a clear description for scan_id. The tool description does not add further parameter semantics beyond what the schema provides. 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 ('Start a purchase'), specific resource ('the full paid SEO Remediation Guide'), price ($8.99), and context ('for a COMPLETE scan'). It distinguishes from sibling tools like get_purchase_status and get_remediation_plan by focusing on initiating payment.

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 explains when to call ('scan must be complete'), that payment cannot be collected through the tool, and provides a numbered autonomous flow with polling logic, condition handling (already_purchased), and next steps. No ambiguity about when to use this tool versus 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?

Annotations already indicate destructive behavior and non-read-only. The description adds that the scan is queued, the API rejects without email, and results are emailed. This provides useful behavioral context beyond the annotations, though it could mention potential delays or rate 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?

Three sentences, each essential. The first sentence states the core action, the second warns about email, and the third outlines the follow-up workflow. No redundancy or unnecessary details.

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 only two required params and no output schema, the description adequately explains the input requirements and the expected behavior. It integrates the tool into the broader workflow with sibling tools, providing a complete picture for the agent.

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 schema covers both parameters with descriptions, achieving 100% coverage. The description adds emphasis on the email requirement, instructing the agent to ask the user, and clarifies that missing URL scheme will be added. This adds meaning beyond the schema, earning above 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 tool queues an SEO scan and returns a scan_id. The verb 'queue' and resource 'SEO scan' are specific. It distinguishes from sibling tools like get_scan_status (polling) and get_scan_results (retrieving findings) by outlining the post-submit workflow.

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 and the required workflow: collect email, submit, then poll get_scan_status until complete, then get_scan_results. It does not explicitly state when not to use it or compare to alternatives beyond the workflow, but the context is clear.

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