CiteRank MCP

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

With no annotations, the description carries the full burden. It implies a non-destructive audit by saying 'Returns...' but does not explicitly mention permissions, rate limits, error handling, or side effects. It provides some transparency about the output content but lacks depth on 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?

A single, well-structured sentence that front-loads the verb and resource, then summarizes the output categories. No wasted words or redundant information.

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?

No output schema exists, but the description enumerates the main return categories (schema analysis, citation score, E-E-A-T signals, agentic readiness, prioritized issues). It could improve by mentioning that there are sibling tools for specific aspects or by describing response format, but it is adequate for a straightforward audit 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?

The input schema covers both parameters fully (100% coverage) with clear descriptions for 'url' and 'focus'. The description reinforces the meaning by listing audit areas, but does not add extra semantic detail beyond the schema.

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 ('Run a full AI visibility audit') and the resource ('any public URL'), and lists the main output categories. The word 'full' helps distinguish it from more focused sibling tools like citerank_check_agentic_readiness or citerank_check_brand_citations.

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 explicit 'when to use' or alternative guidance is provided. The description implies use for comprehensive URL audits rather than specific checks or WP page audits, but this is not stated. The distinction from citerank_analyze_wp_page is only implicit via 'any public URL'.

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 of behavioral disclosure. It does well by listing what the tool returns: recommended schema type with confidence and reason, extracted signals (phone, address, price, FAQ pairs, WooCommerce data), author info, and full content. It implies a read-only analysis operation, though it does not explicitly state side effects or error handling.

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 long, with the first sentence stating purpose and the second enumerating outputs. It is front-loaded, concise, and every word 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 one parameter, no output schema, and no nested objects, the description is quite complete. It explains the purpose, the specific WordPress context, and lists all key return values in detail. It lacks explicit mention of limitations or error conditions, but for such a focused analysis tool, it covers the essentials well.

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 already fully describes the single parameter (post_id) with 100% coverage. The description adds little beyond 'specific page' context, and does not enrich the understanding of the parameter 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 tool analyzes a specific WordPress page, using a specific verb ('Analyze') and resource ('page on your connected WordPress site'). It distinguishes from siblings by specifying the WordPress context and enumerating concrete outputs (schema type, signals, author info, content), making the purpose 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 description gives clear context for when to use this tool: when you need to analyze a page on your connected WordPress site to generate schema. It does not explicitly name alternatives or exclusions, but the 'specific page on your connected WordPress site' provides enough context to differentiate from sibling tools like citerank_analyze_url.

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 transparency burden. It discloses that the tool actively 'tests for' specific signals, implying network requests and analysis. Yet it does not mention whether it is read-only, any rate limits, or what happens with errors, leaving some uncertainty.

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 very concise: two sentences, starting with the primary purpose and then listing specific checks. Every word adds value, and it is neither verbose nor under-specified.

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 single-parameter tool with no annotations or output schema, the description covers the core functionality and key checks. It does not specify the output format (e.g., score, report), but the tool name and description imply a readiness assessment. Minor gaps exist around how results are returned.

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 accurately describes 'url' as 'The URL (or domain) to check'. The tool description adds no extra parameter nuance 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 tool checks website readiness for AI agent interactions, enumerating specific tests (MCP endpoint, WebMCP, potentialAction, A2A card, llms.txt, pricing.md). This distinguishes it from siblings like citerank_check_brand_citations or citerank_analyze_url, which focus on other aspects.

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 clear context that this tool is for assessing agentic readiness, and the list of tested features implies when to use it. However, it does not explicitly mention alternatives or exclusions, so it falls short of a 5.

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. 'Check' implies a read-only operation, and the list of returned metrics gives some behavioral insight. However, it does not disclose potential rate limits, data freshness, or whether the tool performs live lookups, leaving moderate 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?

The description is two concise sentences. The first states the core purpose, and the second lists key outputs. No wasted words 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 tool with two parameters and no output schema, the description clearly explains the purpose and return values. The schema fills in parameter details, making the tool well-specified overall. Minor gaps like auto-derived keyword behavior are covered by the schema, not the description.

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 already covers both parameters with descriptions, and schema coverage is 100%. The description adds no additional parameter-level context beyond the schema, so it defaults to the 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 uses a specific verb ('Check') and resource ('brand/domain cited in AI-generated answers'), and clearly lists return metrics. It differentiates from siblings like citerank_analyze_url by focusing on brand citation presence rather than URL-specific analysis.

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 is clear: use this to check brand citations in AI overviews and Gemini. There is no explicit mention of alternatives or when not to use it, but the purpose is narrow enough that the intended use case is evident. The lack of explicit sibling comparisons prevents a 5.

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 discloses that schema is injected into the page head and logged, which is useful, but it generalizes this to all supported types, though robots_txt, llms_txt, etc. may not inject into the head. It also omits the 'markdown_file' type listed in the schema and uses inconsistent names ('robots' vs 'robots_txt', 'entity' vs 'entity_schema'), providing partial and potentially misleading behavior.

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 three concise sentences, front-loaded with the main action, and each sentence adds relevant information: purpose, behavioral detail, and supported types. No unnecessary 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?

The description conveys the core purpose and one behavioral detail, but the tool is complex (4 params, nested object, multiple deployment types) and the schema carries most of the parameter context. The description omits the full type list (markdown_file) and doesn't explain return values, so it is not fully complete despite the rich schema.

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?

Input schema coverage is 100% with detailed descriptions for each parameter. The description adds no additional parameter semantics beyond what the schema already provides, which matches the baseline for high schema 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 'Deploy JSON-LD schema markup directly to a specific page on your connected WordPress site', using a specific verb (deploy), resource (schema markup/page), and scope. This distinguishes it from siblings like citerank_generate_schema (generation) and citerank_analyze_* (analysis).

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 deployment usage, saying it deploys to a WordPress page and supports multiple deployment types, but doesn't explicitly contrast with alternatives or state when not to use it. The context is clear enough for a deployment action, but no exclusions are given.

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 full burden. It discloses that the tool analyzes page content and produces ready-to-implement markup, suggesting a read-only generation operation. However, it does not mention authentication needs, rate limits, or any potential side effects, leaving important behavioral 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?

The description is two sentences long, front-loads the core action, and contains no redundant information. Every word contributes to understanding the tool's purpose and output.

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 no output schema and no annotations, and the description only gives a high-level outcome ('schema markup ready to implement'). Given the simple two-parameter schema, this is minimally adequate, but it lacks detail on return structure, error behavior, or auto-detection nuances, making it incomplete for a more complex scenario.

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 both parameters ('url' and 'schemaType'), so the schema already documents them fully. The description adds general context like 'any URL' and 'schema markup' but does not provide parameter-specific details beyond what the schema already offers, meeting the baseline of 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 uses a specific verb 'Generate' and a clear resource 'JSON-LD structured data for any URL,' making the tool's function explicit. It also distinguishes itself from sibling tools like citerank_analyze_url and citerank_deploy_schema by focusing on schema generation rather than analysis or deployment.

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 the tool should be used when schema markup is needed for a URL, but it does not explicitly state when to use it over alternatives such as citerank_analyze_url or citerank_deploy_schema. No exclusions, prerequisites, or alternative tool references are provided.

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 clearly indicates the tool checks and returns statuses, and the non-destructive nature is implied by 'Check' and 'Returns'. It also discloses what is checked (specific files and status types), adding useful behavioral context beyond the name.

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, front-loaded with the primary action and followed by specific output details. No filler words, 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 zero-parameter tool with no output schema, the description fully covers what the tool does and what it returns, including status values (active/missing/modified/deleted) and the specific files checked. No critical information is missing.

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?

There are zero parameters, so the baseline is 4. The description adds meaning by explaining the scope of the check (all deployments, specific files like llms.txt and robots.txt), which enriches the tool's semantics beyond the empty input schema.

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 specific verb ('Check the health') and resource ('all deployments on your connected WordPress site'), and specifies the exact outputs (post-level schema status and file-level checks). This clearly distinguishes it from sibling tools like deploy_schema or generate_schema.

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 clear context for when this tool is appropriate (checking deployment health) and implies it is a read-only diagnostic. However, it does not explicitly name alternatives or state when not to use it, but the purpose is distinct from siblings.

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 burden of behavioral disclosure. It discloses what is returned (URL, post type, word count, schema status) but does not mention pagination behavior, despite the 'all pages' wording and the pagination parameters. This is a notable transparency gap.

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, focused sentence that front-loads the action and resource, then lists the return fields. Every word contributes 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?

The description lists the return fields, which is helpful since there is no output schema. However, it fails to mention that to truly list 'all' pages, the agent must handle pagination (per_page max 100). This omission could lead to incomplete results if the agent makes a single call.

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% for the two parameters (page, per_page), and the description adds no additional parameter-level meaning. The baseline of 3 is appropriate because the schema already documents the parameters adequately.

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 ('List') and clearly identifies the resource ('all pages and posts on your connected WordPress site') and the distinguishing scope ('with their current schema deployment status'). This distinguishes it from siblings like citerank_analyze_wp_page, which likely targets a single page.

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 when you need an overview of pages/posts and their schema deployment status. It does not explicitly mention alternatives or exclusions, but the context is clear enough for a listing tool. No when-not-to-use guidance is given.

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 burden of disclosing behavioral traits. It explicitly says 'simulate', implying no real-world side effects, and discloses what the tool returns ('step-by-step results and specific fixes'). This conveys key behavioral information, but it lacks details on any limitations or prerequisites, so a 4 is appropriate.

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 sentence that front-loads the core action ('Simulate an AI agent attempting to complete a task'), lists the specific journey types in parentheses, and then states the value proposition. Every word earns its place 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?

Given the tool's straightforward parameter set (URL and journey type) and the absence of an output schema, the description adequately covers the tool's purpose and return value ('steps-by-step results and specific fixes'). It does not elaborate on edge cases or detailed output structure, but for a simulation tool with two params, it is sufficiently 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 schema already documents both parameters ('url' and 'journeyType'). The description adds no extra parameter semantics beyond the schema; it merely lists the enum values which are already present. Hence, it meets the baseline of 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 action ('Simulate an AI agent') and the specific scope of what is simulated (completing tasks like book, quote, contact, buy, subscribe on a website). It also mentions the output (step-by-step results and fixes), and this tool is distinct from sibling analysis/schema tools, making its purpose 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 clear context for when to use this tool: when you need to simulate an agent journey and identify failure points. However, it does not explicitly mention when not to use it or name alternative sibling tools, which would elevate it to a 5.

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