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Scored across 4 tools
get_current_date and list_sources are clearly distinct utilities, but marketing_data and research are both broad 'query anything' catch-alls whose boundaries blur around competitive intelligence, social media, and competitor research. Descriptions attempt to separate them by domain (configured sources vs. open web), which helps but doesn't fully eliminate overlap.
Two tools follow a verb_noun pattern (get_current_date, list_sources) while marketing_data is a noun phrase and research is a bare verb/noun. The set is still readable, but no single predictable convention holds across all four tools.
Four tools is thin for a platform billed as a full AI marketing agent covering analytics, SEO, ads, CRM, social, research, and visualization. The mega-tool design absorbs that breadth internally, but the surface feels borderline minimal.
The read/analysis side is covered broadly by the routing mega-tools, yet there are no tools for acting on results — creating content, exporting reports, scheduling, or any write operation. For a marketing agent that is a notable gap, though the query surface itself is coherent.
Get the current date, day of week, and timezone. Useful for constructing date ranges.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, closed-world), but the description adds useful context about the output format ('date, day of week, and timezone') and its application ('constructing date ranges'), enhancing understanding beyond annotations without 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?
The description is front-loaded with the core purpose in the first sentence and adds a brief, relevant usage note in the second. Both sentences earn their place by providing essential information without waste, making it highly efficient.
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 low complexity (0 parameters, no output schema) and rich annotations, the description is mostly complete, covering purpose and usage. However, it could slightly improve by hinting at the return format or error cases, though this is minor given the context.
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 0 parameters and 100% schema description coverage, the baseline is high. The description appropriately does not discuss parameters, as none exist, and instead focuses on the tool's output and utility, adding value without redundancy.
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 specific action ('Get') and resources ('current date, day of week, and timezone'), making the purpose explicit. It distinguishes itself from sibling tools like 'list_sources' or 'marketing_data' by focusing on date/time retrieval rather than data listing or 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 provides clear context for usage ('Useful for constructing date ranges'), indicating when to use this tool. However, it does not explicitly state when not to use it or name alternatives among sibling tools, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available data sources and configured domains. Call this to discover which services and domains are available before querying. If exactly one domain exists, use it automatically without asking.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | Domain to list sources for. If omitted, lists all configured sources. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context beyond annotations: it explains the tool lists sources and configured domains, and includes the automatic behavior with single domain. 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 consists of two clear sentences. The first sentence states the purpose, and the second provides usage guidelines. No superfluous words; every sentence earns its place. Front-loaded with 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?
With no output schema, the description should help the agent understand the return format. It says 'list available data sources and configured domains,' which implies a list, but does not specify details like structure or field names. However, the tool is simple and the guidance on automatic behavior completes the context. Slightly lacking in return detail, but overall 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?
The input schema has one optional parameter 'domain' with a description. The description does not add any additional meaning or examples beyond what the schema provides. With 100% schema description coverage, baseline is 3; no extra value is added.
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 available data sources and configured domains, with a specific verb and resource. It also provides usage context by saying 'call this to discover services and domains before querying,' distinguishing it from unrelated siblings like get_current_date, marketing_data, and research.
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 tells the agent to use this tool before querying to discover services and domains. It also provides a rule: if exactly one domain exists, use it automatically without asking. This is clear, actionable guidance for when and how to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query marketing data and analyze any website — analytics, SEO, advertising, e-commerce, CRM, social media, site health & brand identity, competitive intelligence, content creation, and data visualization. Always use a single call, even when the question spans multiple data sources or channels (e.g., GA4 + Google Search Console + Google Ads + CRM). The server auto-routes internally to all needed sources and returns a combined response with the same depth and granularity as individual queries — do NOT split multi-source or multi-channel questions into separate calls.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | Domain to query (e.g., 'example.com'). Required for analytics, ads, search, CRM, and e-commerce queries. Not needed for image generation or data visualization. If no domain is established in context, call list_sources first — if multiple domains exist, ask the user which one. | |
| request | Yes | Natural language question. Include everything you need in one question — all channels, metrics, date ranges, and data sources. For example, "Give me website traffic from GA4, organic search performance from GSC, and paid search results from Google Ads for March 2026" is a single valid request. Never break a multi-part question into separate calls. | |
| end_date | No | End date: YYYY-MM-DD or relative ('today', 'yesterday'). Defaults to yesterday. | |
| start_date | No | Start date: YYYY-MM-DD or relative ('30daysAgo'). Defaults to 30 days ago. | |
| format_hint | No | Optional — leave unset for almost all requests. The default synthesized answer is the correct, recommended output for anything a person will read. Only set this when the output will be parsed by software rather than read by a human (e.g. a chart, dashboard, or artifact needs structured data), never for a conversational answer. Example: "Return only valid JSON, no prose." Requesting JSON for a normal answer produces worse, more verbose results. | |
| source_hints | No | Preferred data sources. If omitted, server auto-selects via semantic routing. | |
| reference_images | No | URLs of reference images (logos, product photos, style references) for image generation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral insight: the server auto-routes internally to all needed sources and returns a combined response with same granularity as individual queries. This goes beyond annotations and helps the agent understand internal routing.
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 key instructions, but contains some redundancy (e.g., the single-call rule is mentioned twice). However, it is well-structured with clear sections for each parameter. A small reduction in repetition would make it more concise, but overall it is efficient for the amount of information conveyed.
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 (7 parameters, no output schema, multiple data sources), the description addresses usage guidelines, parameter details, and edge cases (e.g., missing domain). It does not explain return format, but annotations and the 'combined response' hint partly compensate. Overall, it provides sufficient context for an AI 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?
The input schema has 100% coverage, so baseline is 3. However, the description significantly augments each parameter with practical usage details: domain includes when required and fallback advice, request emphasizes including everything in one question, dates include defaults and relative formats, format_hint explains when to set vs leave unset, source_hints describes auto-selection, and reference_images clarifies usage. This adds substantial meaning 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 tool's purpose: to query marketing data and analyze websites across multiple domains including analytics, SEO, advertising, etc. It distinguishes itself from siblings by emphasizing that a single call covers multiple data sources, and it contrasts with list_sources and research which have different scopes.
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 guidelines are provided: always use a single call, do not split multi-source questions, call list_sources first if no domain is established, and ask the user when ambiguous. It also warns against setting format_hint for normal answers. These clearly indicate when and how to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Research any topic across the open web and 100+ sources — web search, news, and trends; marketplaces, shopping, and reviews; video and social; community and forum discussions; and competitor and ad research. Read specific web pages and extract video transcripts. Works without a domain.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | Optional domain for context (e.g., 'example.com'). Helps focus competitor research. Not required for general queries. | |
| request | Yes | Natural language research question |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context such as the ability to read specific web pages and extract video transcripts, and that it works without a domain. These traits go beyond annotations, though rate limits or return format are not mentioned.
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. The first sentence states the core purpose, followed by a list of source categories, then specific capabilities, and ends with a clarifying statement about domain requirement. Every sentence adds value without 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 that there is no output schema, the description does not explain return values or format. It also does not clarify the relationship with sibling tools like list_sources (e.g., whether list_sources is needed first). However, for a research tool with good annotations, the description covers the main functionality adequately.
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 minimal new meaning beyond the schema: it repeats that domain is optional and helps competitor research, and that request is a natural language question. No additional semantic detail is provided.
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 researches any topic across the open web and lists specific source categories (web search, news, video, etc.). It also mentions reading web pages and extracting transcripts. However, it does not explicitly distinguish itself from sibling tools like list_sources or marketing_data, though the purpose is clear.
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 usage for general research and competitor analysis but does not provide explicit guidance on when not to use it or alternatives. For example, it does not mention that get_current_date is for dates, list_sources for enumerating sources, or marketing_data for marketing-specific queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
marketing_data1 field changedInput schema / properties / format_hintAdded value: +{
+ "description": "Optional — leave unset for almost all requests. The default synthesized answer is the correct, recommended output for anything a person will read. Only set this when the output will be parsed by software rather than read by a human (e.g. a chart, dashboard, or artifact needs structured data), never for a conversational answer. Example: \"Return only valid JSON, no prose.\" Requesting JSON for a normal answer produces worse, more verbose results.",
+ "type": "string"
+}list_sources1 field changedInput schema / additionalPropertiesRemoved value: -falsemarketing_data1 field changedInput schema / additionalPropertiesRemoved value: -falseresearch1 field changedInput schema / additionalPropertiesRemoved value: -falseget_current_datelist_sourcesmarketing_dataresearch
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