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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations by noting the tool is always available and by specifying the informational payload (name, version, data sources, license, author), which helps the agent anticipate the response.

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: one clear functional sentence plus a short availability guarantee and provenance note. Every sentence adds value, and the core purpose is front-loaded.

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 parameterless read-only metadata tool, the description is complete: it states what information will be returned, confirms availability, and the annotations cover safety and idempotency. No output schema exists, but the description enumerates enough of the return content to set expectations.

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, so there is no parameter semantics to document. The description appropriately focuses on what the tool returns rather than inputs, and the 100% schema coverage is trivially satisfied.

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 ('Show') with a clear resource ('information about this MCP server') and enumerates the exact fields returned: name, version, data sources, license, and original author. This clearly distinguishes it from the sibling data-access tools, which all involve specific Dallas civic datasets.

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 'Always available,' which tells the agent this tool has no prerequisites and can be called in any context. It does not explicitly name alternatives or when not to use it, but the content makes it obvious that sibling tools are for domain data, not server metadata.

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 and idempotent. Description adds 'authoritative source' context. No contradictions; behavioral traits are sufficiently covered.

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, zero waste. Information is front-loaded and efficiently presented.

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 7 optional parameters, no output schema, and no nested objects, description covers tool's purpose and filters well. Missing pagination info but not critical 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 descriptions. Description lists filter options but does not add extra meaning beyond schema. Baseline score is appropriate.

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 uses specific verb 'Search' and resource 'City of Dallas 311 service requests' with explicit categories. Clearly distinguishes from sibling tools (about, weather alerts, 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?

States when to use (searching 311 requests) and lists filter options. Does not explicitly mention when not to use, but context with siblings implies scope. Adequate for selection.

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 and idempotentHint, so the description adds meaningful extra context: excluded record types (sexually-oriented offenses and juvenile-involved records), the data source (RMS data, 2014-present), and the authoritative publisher (City of Dallas Open Data). This goes beyond the safety profile already captured by 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 well-structured: it opens with the core purpose and time range, then covers filter modes, default behavior, data omissions, and source. Every sentence contributes useful information with no 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?

For a read-only search tool with zero required parameters and no output schema, the description provides sufficient context for an agent to decide when and how to call it, including scope, exclusions, and source authority. It could optionally mention response structure, but the functional semantics are clear.

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 all four parameters are already well-documented. The description adds only a high-level mention of filtering dimensions (offense, council district, address) without providing details beyond the schema. This matches the baseline for fully covered schemas.

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 names a specific verb and resource: 'Search Dallas Police reported incidents (RMS data, 2014-present).' It clearly differentiates this tool from the sibling tools like dallas_property or dallas_311 by specifying the crime dataset, geographic scope, and time range.

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 usage context: filtering by offense/keyword, council district, or address, and the option to pass no filters for the most recent reports. It does not explicitly name alternatives or state when not to use this tool, but the guidance is still concrete and actionable.

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 indicate read-only, open-world, idempotent, non-destructive. The description adds detailed return fields (severity, urgency, headline, description, expiration) and authoritative source, fully disclosing behavior.

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 front-loaded purpose and essential details. No 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?

Despite no output schema, the description specifies return fields, making it complete for a simple read operation. Covers purpose, parameters, defaults, and data source.

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?

All parameters have schema descriptions, and the description adds context on default behavior (central Dallas) and geocoding skipping when using lat/lng, enhancing understanding beyond 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 retrieves active NWS alerts for a Dallas location, lists alert types, and describes returned fields. It distinguishes itself from sibling tools by being weather-specific.

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 explains how to use parameters (lat/lng or address) and notes the default to central Dallas. It provides clear usage context without needing explicit exclusions.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the authoritative source and permit categories, but does not disclose return shape, pagination, or dataset update behavior beyond what annotations imply.

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 short, front-loaded with the main action and resource, and every clause adds value. No filler or redundant restatement of the tool name.

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 read-only search tool with a rich schema and strong annotations, this description is nearly complete. The only minor gap is that the prose does not mention the date-range filter or output shape, though the schema documents the former and the latter is less critical 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 description coverage is 100%, so the baseline is 3. The description reinforces the key filters (permit type, status, council district, address) but adds no detail beyond the schema; limit and days_back are left entirely to 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?

Uses a specific verb and resource: 'Search City of Dallas building permits,' with concrete permit categories and filter dimensions. This clearly distinguishes it from sibling tools about crime, property, schools, 311, and weather.

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 this is the tool for Dallas permit searches and notes the authoritative data source, but it does not explicitly state when to prefer it over siblings or when not to use it. Context is clear, but exclusionary guidance is absent.

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 establish readOnly, idempotent, openWorld, and non-destructive behavior. Beyond that, the description honestly discloses a key limitation: the layer contains no appraised value or payment status, only a link to the external source. This prevents an agent from promising data the tool cannot return.

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 front-load the core purpose, then add the crucial limitation and redirect. There is no filler, and each sentence 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 search inputs, return fields, source provenance, domain boundary, and a fallback path for missing data. It is slightly light on output shaping or how limit/partial matching behaves, but no output schema exists and the essential invocation context is present.

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 parameters already carry full documentation. The description adds a brief framing ('street name or account number') but does not materially go beyond the schema's own parameter descriptions. Baseline 3 is appropriate.

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 ('Look up') with a clear resource ('Dallas-area tax parcel'), identifies the key search paths ('street name or account number'), and lists the returned fields. It clearly distinguishes this tool from its data-domain siblings like dallas_permits, dallas_crime, and dallas_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 states when the tool is appropriate: when you need parcel data by street name or account number. It also tells the agent what this layer does NOT provide (dollar values, tax bills) and where to find that information (county appraisal district page), which helps route the request appropriately even though it doesn't explicitly name sibling 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?

The description fully aligns with annotations (readOnlyHint, idempotentHint, etc.) and adds valuable context: authoritative sources (TEA 2022-2023), fuzzy search behavior, and the limitation regarding attendance zones. It discloses the read-only nature and data recency, exceeding annotation coverage.

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 well-structured with a clear first sentence stating core purpose, followed by search options, returned fields, examples, source citation, and a limitation note. It is informative without excess; however, some sentences could be slightly shorter.

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 7 optional parameters, full schema coverage, no output schema, the description adequately covers expected returns (ratings, sub-scores, enrollment, demographics, etc.), sources, and data freshness. It sets proper expectations for what the tool does and does not do, making it reasonably complete 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 100%, but the description adds meaning beyond parameter descriptions by providing specific examples (e.g., 'Dallas ISD', 'Booker T Washington HSPVA'), clarifying that searches are 'fuzzy contains', and explaining the 'rating' parameter as '2022-2023 overall rating'. This adds practical guidance.

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 ('Look up'), resource ('Texas public schools and their TEA accountability ratings'), and scope ('Search by campus name, district, county, or city'). It lists what is returned and distinguishes itself from siblings by focusing on schools and ratings, while sibling tools cover about, alerts, and 311 services.

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 when to use the tool (looking up Texas public schools and ratings) and explicitly states what it does NOT do (address mapping to attendance zones). While it does not mention alternatives, sibling tools are unrelated, so explicit when-not guidance is less critical.

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