premium-data-mcp-server

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description doesn't need to restate safety. However, it adds useful behavioral context by specifying the data sources (Google Shopping and direct URL monitoring) and the argument behavior, such as max_results defaulting to 20. This supplements the annotations without contradicting them, providing clarity on how the tool operates.

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 a clear purpose sentence followed by structured argument definitions. Every sentence earns its place, and there is no redundant or overly verbose content. The front-loading of the main action improves quick comprehension.

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 covers the essential functionality and all parameters, but it does not explain the return value or output format. Since there is no output schema, the description should ideally state that the tool returns price comparisons or a list of prices across platforms. This omission leaves a gap for an agent expecting to know what to do with the result. Overall, it is minimally viable but missing output details.

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 compensate. It does so with an 'Args' section that explains each parameter: 'product_name: Product name to search', 'product_urls: Direct product page URLs to check', 'max_results: Max results (default 20)'. This fully defines the parameters, making the tool usable without needing additional schema 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's purpose: 'Compare product prices across e-commerce platforms using Google Shopping and direct URL monitoring.' This is a specific verb+resource construction that distinguishes it from sibling tools like search_amazon_products, which focuses on a single platform. The tool's scope is immediately evident.

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 by describing the mechanism (Google Shopping and direct URL monitoring) but does not explicitly state when to use this tool over alternatives. It lacks explicit 'use when' or 'use instead' guidance, though the purpose itself suggests a use case of multi-platform price comparison. The absence of explicit exclusions or alternative references makes this adequate but not exceptional.

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, so the read-only nature is covered. The description adds that it searches global registries and returns records/officers/filings, but does not disclose additional behavioral details such as response format, pagination, or potential data variability. It does not contradict 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 compact and front-loaded with the purpose. The 'Args:' section cleanly lists parameters without fluff. Every sentence contributes useful 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?

With no output schema, the description should clarify what the tool returns. It mentions the categories (records, officers, filings) but does not describe the return structure, pagination, or error cases. The parameter details and read-only annotation are helpful, but the return format is a clear gap for a lookup 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 coverage is 0%, so the description carries the full burden for parameter meaning. It explains all three parameters with clear definitions, including jurisdiction examples ('us_de', 'gb') and a default value for max_results. This adds meaningful context beyond the bare 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 function: 'Look up company registration records, officers, and filings from global business registries.' This provides a specific verb, resource, and scope, and clearly distinguishes it from sibling tools that deal with products, recalls, and contracts.

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 guidance is provided on when to use this tool versus alternatives. The description only states what it does; there are no when-not-to-use conditions, prerequisites, or mentions of alternative tools for different needs.

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 openWorldHint=true, covering the safety profile. The description adds the ability to directly look up ASINs and the default result count, but does not disclose behaviors like pagination, rate limits, or search matching semantics. It provides minimal extra context 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?

The description is concise and front-loaded with the primary purpose. The Args section is efficient and to the point, with no redundant content, though it could be slightly more 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?

The description is adequate for a simple read-only search tool, but it lacks clarity on return structure, pagination, and edge cases. With no output schema, the description does not fully convey what the agent can expect beyond generic categories like details, pricing, reviews, and ratings.

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 description maps each parameter to a brief explanation, such as 'List of ASINs to look up directly' and 'Max results (default 15),' adding some meaning beyond the bare schema. However, explanations are succinct and partly redundant with schema defaults, and do not delve into formats or constraints.

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 explicitly states 'Search Amazon for product details, pricing, reviews, and ratings,' using a specific verb plus resource and outcome. This clearly distinguishes the tool from sibling tools like compare_product_prices, search_fda_recalls, and search_government_contracts.

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 Amazon product lookups but gives no explicit guidance on when to use this tool versus alternatives. No exclusions or alternative recommendations are mentioned, making the usage context unclear despite the clear purpose.

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 openWorldHint=true, so the agent knows this is a safe read operation that may return varying results. The description adds the scope (across drugs, food, and devices) but does not disclose additional behavioral traits such as pagination, rate limits, or return format. This is adequate but not rich.

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: a one-sentence purpose followed by a clean Args list. Every line is informative and there is no redundant or fluff content.

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 search tool with four parameters, the description covers the essential scope and parameter meanings. It does not describe the return format, but since there is no output schema and the tool is a fairly straightforward search, this is a minor gap. Overall, it is complete enough for an 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?

The schema provides only types and defaults with 0% description coverage. The description compensates fully by explaining each parameter: search_term as product/drug/company name, category with allowed values (drug, food, device, all), severity with classifications (Class I, II, III), and max_results with default. This adds significant 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 searches FDA product recalls, safety alerts, and enforcement actions across drugs, food, and devices. This is a specific verb+resource combination and is distinct from sibling tools which focus on prices, business registration, Amazon products, and government contracts.

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 FDA recall and safety information. However, it does not explicitly mention when not to use it or alternative tools, though the sibling tools are topically distinct enough that the intended usage is fairly unambiguous.

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 openWorldHint=true, so the read-only, open-ended nature is covered. The description adds the data source (USASpending.gov) and list of award types, but it does not disclose behavior such as result ordering, pagination, or how 'open world' search behaves. Given the annotations, the description provides adequate but not rich supplemental transparency.

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 introductory sentence followed by a concise, bulleted 'Args:' block. Every line is purposeful, front-loaded with the tool's core purpose, and the parameter documentation is scannable without unnecessary prose.

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 does not explicitly state what the search results look like (e.g., list of awards with dates/amounts), but it does specify the source, filters, and defaults, which is adequate for a read-only search tool. The parameter documentation is thorough and the read-only/open-world annotations fill the safety and matching-expectation gaps. A slightly richer return-value hint 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?

Schema descriptions are absent (0% coverage), and the description fully compensates by explaining each parameter with examples and semantics: keyword ('cybersecurity', 'cloud computing'), agency ('Department of Defense'), award_type ('contracts, grants, loans, or all'), min_amount ('in USD'), and max_results ('default 20'). This adds substantial meaning beyond the bare type/default fields in 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 a specific action ('Search') and resource ('US federal government contracts, grants, and spending data from USASpending.gov'), distinguishing it from sibling tools like search_amazon_products or search_fda_recalls. It also lists the key search dimensions (keyword, agency, award type, amount, result count), making the tool's scope 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 implies usage for federal spending searches (via 'federal government contracts, grants, and spending data') and the parameter examples illustrate common use cases, but it does not explicitly state when to use this tool over alternatives or when not to use it. There is no direct comparison with sibling tools or exclusion criteria, so guidance is implied rather than explicit.

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