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

No annotations are provided, so the description must carry the burden. It describes a query operation with no side effects, but does not explicitly state read-only behavior, authorization needs, 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?

The description is a single sentence that efficiently conveys the tool's purpose and returns, with no wasted words. It is front-loaded with the core action.

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 input schema (two optional parameters) and no output schema, the description adequately explains what the tool does and what it returns. It could mention that results are specific to this MCP server, but that is inferred.

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 does not add new meaning beyond the schema; it mentions return types but does not elaborate on parameters.

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: querying usage analytics and revenue data for the MCP server, and lists specific return values (call volumes, revenue, success rates, latency, dynamic pricing). This distinguishes it from sibling tools which cover different domains like compounds or GPU prices.

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 implicitly indicates use for analytics queries; sibling tools are in distinct domains, so context is clear. However, there is no explicit guidance on when to use or avoid this tool, nor mention of 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, so the description carries the full burden. It discloses data sources (PubChem, ChEMBL), lists returned properties, and notes that including bioactivity data is slower. This is good transparency for a read-only tool, though it does not mention rate limits or authorization requirements.

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 that front-loads the core purpose and lists key outputs efficiently. 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 the absence of an output schema, the description adequately explains what properties and data the tool returns. However, it does not describe the exact output structure or format, which would be helpful for full contextual completeness. The mention of optional bioactivity and clinical trial phase data adds necessary 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?

The parameter descriptions in the schema are detailed, with 100% coverage. The description adds value by explaining the output context (e.g., Lipinski rule-of-5 drug-likeness assessment, synonyms, clinical trial phase data) which is not in the schema, enriching the understanding of what the tool returns.

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: looking up chemical compound properties from PubChem and ChEMBL, and lists specific outputs like molecular weight, SMILES, LogP, etc. This distinguishes it from sibling tools (e.g., literature_search, patent_prior_art_search) which focus on different domains.

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 retrieving compound properties but does not explicitly state when to use this tool versus alternatives, nor does it provide conditions for use or exclusions. The context from sibling names suggests differentiation but is not explicit.

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 bears full responsibility for behavioral disclosure. It correctly indicates a read-only operation (returns data), mentions live prices, interruption probabilities, on-demand comparisons, and optional forecasts. This is sufficient for an agent to understand the tool's behavior, though it could be slightly more explicit about the real-time nature.

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: first states purpose and scope, second lists key features, third provides usage guidance. No redundant or extraneous information. Every sentence is informative and 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?

The description covers the main points: providers, interruption probabilities, comparisons, and optional predictions. No output schema exists, but the description hints at the return format (prices, etc.). A slightly more detailed account of what exactly is returned (e.g., sorted list, structured data) would improve completeness, but it is still adequate.

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 each parameter described. The description adds context beyond the schema (e.g., 'optional 1hr/4hr price forecasts with buy/wait recommendations') but largely reiterates the schema. The baseline of 3 is appropriate given 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 it returns live GPU spot prices across multiple providers, with specific additional features (interruption probabilities, on-demand comparisons, forecasts). It ends with a clear usage directive: 'Use this to find the cheapest available GPU slot.' This distinguishes it from sibling tools like analytics or compound_lookup, which are unrelated domains.

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 the tool ('find the cheapest available GPU slot') but does not explicitly mention when not to use it or provide alternatives. However, the sibling tools are in very different domains (analytics, literature, patents), so the context is clear enough for an AI agent to select this tool appropriately.

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. It discloses the return structure (titles, abstracts, authors, etc.) and filtering capabilities, but does not mention rate limits, pagination, or behavior under no results. It adds value beyond the tool name but lacks comprehensive disclosure.

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: the first covers the primary function and sources, the second lists return fields and filtering options. No redundant information; every sentence adds value.

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, the description mentions return fields. It covers the main use case and filtering, but does not specify pagination or error handling. With 6 parameters, all are explained in schema and description, making it fairly complete for a 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 coverage is 100% with good parameter descriptions. The tool description adds additional meaning by stating that the query supports natural language or Boolean operators, that max_results is per source, and that year_from/year_to are filters for date range. It also describes the return fields, providing context 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 verb (Search), resource (scientific literature), and specifies the sources (PubMed, arXiv, Semantic Scholar). It lists return fields (titles, abstracts, authors, etc.), distinguishing it from sibling tools like analytics or compound_lookup.

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 searching scientific literature and mentions supported filters (date range, field-specific queries), but does not explicitly state when to use this tool vs alternatives or when not to use it. 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, the description must fully disclose behavior. It states it returns ranked patent results with specific fields, which is adequate for a search tool. However, it does not mention rate limits, cost implications beyond a marketing claim, or any side effects (though unlikely).

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 efficient with four sentences, each serving a purpose: purpose, inputs, outputs, and value proposition. The final sentence is slightly promotional but does not harm clarity. No 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?

The description covers inputs and outputs comprehensively, listing returned fields. Without an output schema, this is necessary. It lacks usage guidance but is otherwise complete for a read-only search tool. Annotations would add safety context but are absent.

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 repeats parameter categories ('technical description, optional keywords, CPC classification, date range') but adds no new meaning beyond the schema's descriptions. It does not clarify the input format or provide examples.

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 USPTO and EPO patent databases for prior art, specifying the resource (patent databases) and action (search). It differentiates from siblings like literature_search by focusing on patents, but doesn't explicitly contrast with scientific_data or others.

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 for patent prior art searches but provides no explicit guidance on when to use this tool versus alternatives like literature_search. No when-not-to-use or prerequisites are mentioned.

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 disclosure. It states the tool fetches data and returns structured data but omits details about rate limits, authentication requirements, or any potential side effects. It adequately describes the retrieval nature but lacks depth.

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 well-structured sentences. The first sentence defines the tool's purpose, and the second adds key details (datasets, filtering, output type). It is efficient but could be slightly tighter by integrating the dataset list into the first sentence.

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 (9 parameters, no output schema), the description covers the main capabilities: datasets, geographic filtering, and data readiness. However, it lacks details on the return format for each dataset, which would be helpful given the absence of an output 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?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema by summarizing capabilities like geographic filtering, but does not elaborate on parameter formats or dependencies. The schema already provides clear parameter 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 fetches real-time scientific data from major APIs and lists specific datasets (earthquakes, air_quality, etc.), providing a specific verb and resource that distinguishes it from sibling tools like analytics or compound_lookup.

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 fetching scientific data with geographic filtering but lacks explicit guidance on when to use this tool versus alternatives or any when-not-to-use conditions. No exclusions or prerequisites are mentioned.

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