mcp

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

No annotations are provided, so the description must carry the full burden. It states it resolves IDs into JSON-text results, implying a read operation, but it does not disclose any side effects, permissions needed, error behavior, or rate limits. The term 'compatibility alias' hints at possible delegation but adds no concrete behavioral detail.

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 sentence that efficiently conveys the purpose, input format, and output type. It front-loades key information and avoids 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?

The tool is simple with one parameter and no output schema. The description covers input format and output type, but it does not explain the relationship to sibling tools in enough detail, nor does it mention error handling or limitations. It is adequate but leaves room for more 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?

Schema coverage is 0%, so the description must compensate. It explains the 'id' parameter format with examples like brand:stripe and prompt:best-crm, and states the output is JSON-text with human-readable text. This adds significant meaning beyond the bare string type, though it could be more precise about the return structure.

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 it resolves fetch IDs into JSON-text results, with examples like brand:stripe or prompt:best-crm. It uses a specific verb 'resolves' and identifies the resource type. The 'compatibility alias' hint distinguishes it from sibling parse_* tools, but could be more explicit about its scope.

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 says 'compatibility alias' implying it is an alternative to other tools, but it does not provide clear guidance on when to use this tool versus the specific parse_get_brand or parse_get_prompt tools. No when-not-to-use or explicit selection criteria 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?

No annotations provided, so description carries full burden. It mentions 'public' implying read-only, and lists return contents, but lacks details on error cases, rate limits, or prerequisites.

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 are concise and front-loaded with the main action, listing key contents without 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?

Given the tool's simplicity (1 param, no output schema), the description adequately covers the purpose and return content. Minor gap: no example of slug_or_id format.

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 has one required param 'slug_or_id' with no description. The description does not explain what a slug or ID is, how to obtain it, or its format, leaving agents with 0% 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 verb 'Fetch' and the resource 'public marketing brief for one brand', and lists specific contents. It distinguishes from siblings like parse_get_prompt and parse_get_stats which serve different purposes.

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 when to use (for one brand's brief) but does not explicitly state when not to use or provide alternatives among sibling tools like search or parse_search.

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 full burden. It does not disclose behavioral traits like read-only nature, auth requirements, error handling, or rate limits. The minimal description assumes a simple fetch but leaves 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 a single concise sentence that front-loads the purpose with no wasted words. Every part serves to clarify the tool's function.

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 nature of the tool (one parameter, no output schema, no annotations), the description provides enough to understand its core purpose but lacks details about return values, errors, or edge cases that would make it 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 0%, so the description must add meaning. It mentions 'by slug' but does not explain the slug format, constraints, or how it relates to the prompt. The parameter's purpose is inferred but not elaborated.

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 specifies the action ('fetch'), the resource ('one public organic prompt'), the identifier ('by slug'), and the use case ('when the user wants to inspect the exact AI-search question behind a result'). This distinguishes it from sibling tools like parse_get_brand.

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 a clear context for when to use this tool ('when the user wants to inspect the exact AI-search question behind a result'), but does not explicitly mention when not to use it or suggest alternatives from the sibling list.

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 must disclose behavioral traits. It only states the tool 'explains,' implying a read operation, but does not specify whether it is read-only, requires authentication, or has rate limits. The description lacks depth to fully guide safe invocation.

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 concise sentence that front-loads the key action ('explain') and lists the specific data points. No wasted words. Ideal for a simple, parameterless tool.

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 parameters and no output schema, the description is the main source of context. It explains the tool provides scale and freshness metrics, but does not describe the return format (e.g., text, structured data). While sufficient for basic understanding, more detail on output would make it 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?

There are zero parameters, and schema coverage is 100% by default. The baseline for 0 parameters is 4. The description adds no parameter info, but none is needed. It correctly implies no inputs are required.

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 explains the public Parse index scale and freshness, listing specific metrics (tracked brands, organic prompts, citation observations). It distinguishes from siblings like parse_get_brand that target specific entities. However, 'Explain' is somewhat vague—does it return data or a summary? Still, the purpose is adequately 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?

No guidance on when to use this tool versus alternatives like parse_get_brand, parse_search, or search. The description does not mention prerequisites, context, or exclusions. The agent must infer usage from the tool name alone.

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 implies a read-only search operation ('Find ... for marketer research') but does not disclose behavioral traits such as pagination, result ordering, or side effects. The description is adequate but lacks extra behavioral context beyond the basic operation.

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, front-loaded with the action and resource, and includes a usage guideline. Every sentence serves a purpose with no redundant or 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?

Given the absence of an output schema and annotations, the description should provide more detail on return formats or behavior. It states what the tool finds but not how results are presented. Combined with partial parameter coverage, the description is minimally 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?

With 0% schema description coverage, the description must compensate. It adds meaning for the 'types' parameter by listing the four enum values (brands, prompts, niches, sources) in the purpose statement. However, it does not explain the 'query' or 'limit' parameters, relying on their names to be self-explanatory. This partial coverage earns a 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 clearly states 'Find brands, organic AI prompts, citation sources, and market niches for marketer research,' providing a specific verb ('Find') and resource (the four categories). It also distinguishes itself from sibling tools like 'search' and specific 'parse_get_*' tools by indicating it should be used first for broad queries.

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 says 'Use this first when the user names a brand, category, source, or AI visibility question,' giving clear context for when to invoke the tool. It implies a priority over siblings but does not explicitly state when not to use it or name specific 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?

No annotations are provided, and the description only says it is an alias. It fails to disclose behavioral traits such as read-only nature, result format, or side effects.

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 with no wasted words. It is front-loaded and easily understood.

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?

Without an output schema or parameter explanations, the description is inadequate for an agent to understand how to invoke or interpret results from this search 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 0%, and the description does not explain the query or limit parameters. The schema itself has no descriptions, so the description adds no value.

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 it is a compatibility alias for parse_search, indicating the tool performs search. However, it does not specify what resources are searched (e.g., brands, prompts).

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 to use for clients expecting a generic search tool, guiding when to use this alias. It also implies the primary tool is parse_search.

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