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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds context about availability ('Always available') and specifies the content returned, which is useful beyond the 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 a single, focused sentence that front-loads the verb and resource. It includes all necessary information without waste, and the URL adds provenance not cluttering the core.

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 simple informational tool with no parameters and strong annotations, the description comprehensively explains what the tool returns and that it is always available. No output schema is needed since the content is enumerated.

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 tool takes zero parameters, so the schema has no gaps. Baseline for no params is 4, and the description doesn't need to add parameter details.

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?

Description clearly states the tool's purpose: 'Show information about this MCP server' with specific details (name, version, data sources, license, original author). It distinguishes itself from sibling data tools by focusing on server metadata.

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 phrase 'Always available' provides clear context for when to use the tool, implying it can be called without prerequisites. While it doesn't explicitly name alternatives, the sibling tools are clearly different in scope, so the guidance is sufficient.

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 destructiveHint=false, so the safety profile is covered. The description adds context by specifying the types of service calls (non-emergency), the filterable fields, and the authoritative data source, which goes beyond the annotations and helps the agent understand the tool's scope.

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, front-loading the primary purpose and providing useful examples. No filler or redundant information; every sentence contributes to understanding the tool's functionality and data source.

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 moderate complexity (6 optional parameters, no output schema) and good annotations, the description adequately covers what the tool searches, what filters are available, and the authoritative source. It does not explain return format or pagination, but that is not required when no output schema exists and the annotations cover the safety profile.

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 input schema already documents all six parameters with types, defaults, and examples. The description merely restates the filterable dimensions (category, type, department, status, council district) without adding new semantics beyond the schema, resulting in the baseline score.

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 ('Search') with a clear resource ('San Antonio 311 service calls') and enumerates example categories (streets, animal services, code compliance). It also lists filter dimensions, making the tool's purpose distinct from siblings like san_antonio_nws_alerts or san_antonio_tea_schools.

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 clearly implies when to use this tool (for non-emergency San Antonio city services) by stating its scope and authoritative source. It does not explicitly name alternatives or exclusions, but the sibling tools are unrelated, so the context is sufficient for an agent to select this tool over others.

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 readOnly, idempotent, and non-destructive. The description adds valuable behavioral context: defaulting to central San Antonio when no address is supplied, the authoritative source (api.weather.gov), and the specific return fields. This goes beyond the annotation-only safety profile.

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 core purpose, then adding default behavior, return fields, and source. Every sentence earns its place with no redundancy or 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?

For a simple read-only tool with three optional parameters and no output schema, the description is complete: it states the alert types, default if no address, the exact fields returned, and the authoritative source. No critical information is missing for correct invocation.

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 100% parameter description coverage, including address default behavior. The description does not meaningfully add parameter semantics; it references the default location but that is already in the schema. No additional parameter constraints or usage details are 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 provides active National Weather Service alerts for a San Antonio location, listing specific alert types. It distinguishes from siblings (schools, 311) by focusing on weather alerts and includes the specific scope, return fields, and source.

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: for NWS alerts in San Antonio, with a default location if no address is given. It doesn't explicitly mention alternatives or exclusions, but the sibling tools are clearly unrelated (schools, 311), so usage context is 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?

Beyond the read-only/idempotent annotations, it discloses authoritative data sources (TEA 2022-2023, AskTED) and the non-mapping limitation. It also enumerates return fields, adding context helpful for an agent.

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 purpose. Three sentences cover purpose, search options, return data, examples, sources, and a caveat 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?

With no output schema, the description compensates by listing return fields (rating, sub-scores, enrollment, demographics, etc.). It also gives example districts and source context, making it sufficiently complete for selection and invocation.

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 covers 100% of parameters with descriptions; the description adds concrete examples (e.g., 'San Antonio ISD') and groups searchable fields, but doesn't explain how multiple filters combine or affect results, leaving a minor gap.

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 opens with 'Look up Texas public schools and their TEA accountability ratings,' providing a specific verb and resource. It clearly distinguishes from sibling tools about weather, 311, and general info by focusing on school accountability data.

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?

It states searchable attributes (campus name, district, county, city) and provides example district values. It explicitly notes when not to use it (address-to-school mapping) and points to ISDs for that need, though it doesn't name an alternative MCP tool.

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