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

With no annotations, the description carries the full burden and does well: it discloses the two validation phases (format + checksum), the offline/no-network behavior, and the exact shape of the result including the failure `reason` field. Minor gaps remain (e.g., error/exception behavior is unspecified), but the safety-relevant profile is 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?

The one-line purpose is front-loaded before the Args/Returns blocks, and every sentence carries information. The docstring-style Args/Returns formatting is somewhat verbose for a single-parameter tool but not wasteful.

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 one-parameter, deterministic, offline validator, the description covers purpose, mechanism, input normalization rules, and output semantics; an output schema also exists. Nothing needed for correct invocation is missing.

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% and the bare `piva: string` schema says nothing, so the description must compensate — and it does, spelling out that the 'IT' prefix may be present or omitted and that 11 digits are expected after stripping prefix and whitespace. It even documents the optional `ctx` parameter that is absent from the input 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?

States a specific verb+resource ('Validate an Italian VAT number') and adds the distinguishing mechanism (official MEF checksum, fully local computation), which separates it from the network-based verify_piva_vies sibling. It stops short of naming the sibling explicitly, so it is clear but not maximally differentiated.

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 network call — fully local computation' implies this is the offline/format-check path as opposed to a VIES lookup, which is genuine usage signal. However, it never states when to prefer this tool over verify_piva_vies or the other siblings, nor any exclusion conditions.

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 carries the full burden and does so well: it discloses that XML content is never logged or persisted, that missing fields return None instead of raising, that file_path is checked against FATTURAPA_ALLOWED_ROOTS before any read, and it enumerates the exact exception types. This is materially richer than a bare 'extract' claim.

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?

Front-loads the one-line purpose, then uses conventional Args/Returns/Raises sections, so it is skimmable. The Raises block is slightly heavy for a read-only parse, but every sentence conveys contract detail rather than filler.

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 two-parameter parse tool with an output schema, the description covers input sourcing, the allow-roots security gate, None-on-missing semantics, and all failure modes. An agent has everything needed to call it correctly; only the dual-input precedence edge case is unaddressed, and the output schema handles the return shape.

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, and it defines both parameters (raw validated XML string; optional path validated against allowed roots) plus a ctx argument that appears only in the prose. It stops short of stating precedence when both xml_content and file_path are supplied.

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?

States a specific verb and resource: 'Extract key fields from a validated FatturaPA XML document,' scoping output to header + first body section. The word 'validated' hints that validate_invoice is a prerequisite, but no sibling is named explicitly, so the agent must infer the pipeline position.

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?

Gives a real usage fork: pass xml_content to skip file I/O, or file_path to read from disk. However it never states when to prefer this tool over find_invoice_anomalies or generate_invoice_report, and the validation prerequisite is only implied by the adjective 'validated' rather than stated as a condition.

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 that XML is never logged or persisted, that file_path is checked against FATTURAPA_ALLOWED_ROOTS, and it lists PermissionError, ValueError, and XMLSyntaxError, which is valuable operational context. It does not explicitly state read-only semantics, but the behavioral body is strong.

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?

Front-loads purpose, then uses Args/Returns/Raises sections efficiently. The Returns block duplicates the existing output schema, and the Args block includes a non-schema ctx parameter, but the description is well organized and not bloated relative to the tool's complexity.

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?

Covers purpose, parameter usage, security constraints, error cases, and a return summary, which is sufficient for correct invocation. The main gap is sibling routing: nothing tells the agent when this tool is preferable to validate_invoice or check_piva.

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 explains xml_content as a raw XML string and file_path as an optional path checked against allowed roots and skippable by passing XML directly, adding real meaning beyond the bare schema. It also lists a 'ctx' parameter that is not in the schema, a minor mismatch.

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?

States a specific verb ('Detect') and resource ('inconsistencies and anomalies in a FatturaPA XML document') and enumerates eight anomaly categories, so the agent knows exactly what it does. However, it does not distinguish itself from sibling tools like validate_invoice or check_piva.

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 choose this tool over validate_invoice, check_piva, or other siblings. The only usage notes concern file_path versus xml_content input, not tool 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?

With no annotations to lean on, the description carries the full burden and delivers: unparseable documents are counted in an errors list and excluded from monetary totals, XML content is never logged or persisted, and progress notifications follow a defined 1..N plus N+1 sequence. These are exactly the behavioral traits an agent needs and cannot get from structured fields.

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?

Front-loaded with the purpose sentence, followed by behavior, then Args/Returns. The structure is scannable and every section earns its place, though the Args/Returns prose partially repeats what the output schema already conveys.

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 two-parameter batch tool with an output schema, the description supplies error semantics, privacy guarantees, progress-notification behavior, and a parameter rundown. Nothing material an agent needs to invoke it correctly is missing.

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, and it does: xml_contents is described as a list of raw XML strings, title is documented as optional with the 'Invoice Report' default, and ctx is explained as an optional MCP context for logging (not even present in the schema). It could add more on expected XML validity or size limits, but coverage is solid.

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?

States a specific verb (aggregate) and resource (FatturaPA XML documents) and the output (structured report). It distinguishes itself from siblings by explaining it composes extract_invoice_data and find_invoice_anomalies rather than performing those jobs itself.

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 makes clear this is the batch aggregation entry point built on top of the extraction and anomaly siblings, so an agent can infer when to reach for it versus the single-invoice tools. It stops short of explicitly naming when NOT to use it or listing alternatives by name.

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 and does well: it discloses the local static data source, that no network call is required, the exact return keys, and that a ValueError is raised when the code is unknown. It stops short of details like case sensitivity or input format constraints, keeping it from a 5.

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 front-loaded with purpose and structured into Args, Returns, and Raises sections. It is somewhat verbose, and the Returns section partly duplicates what an output schema already provides, but the structure remains clear and useful.

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 one-parameter lookup tool with no annotations and 0% schema description coverage, the description covers the essential behavior, return shape, and failure mode. Minor gaps around error-code formatting and the unexplained ctx parameter prevent a full 5.

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 add meaning for error_code with examples ('00001', '00002'), but it omits format constraints such as exact digit length, and it mentions a 'ctx' parameter that does not appear in the input schema, creating ambiguity.

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?

States a specific verb and resource: 'Look up an SDI error code and return its human-readable description.' This clearly distinguishes it from all invoice-processing siblings, which perform validation, extraction, or reporting rather than error-code resolution.

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 it by specifying the SDI error-code lookup and noting it uses a local static table with no network call. However, it does not state when to prefer alternatives or when this tool would not apply, leaving usage context only implied.

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 carries the full burden and does so thoroughly: it discloses auto-detection of the schema version, that no XML content is logged, that file_path is validated against FATTURAPA_ALLOWED_ROOTS before reading, and the exact exceptions (PermissionError, ValueError) and their triggers.

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 docstring format is front-loaded with purpose, then behavioral notes, then Args/Returns/Raises. It is slightly verbose and the Returns block partially duplicates the output schema, but every paragraph adds information an agent needs.

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 two-parameter validation tool with an output schema present, the description covers the full picture: version detection, error semantics, permission constraints, privacy behavior, and input-source selection. Nothing needed to invoke it correctly is missing.

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 prose must compensate, and it largely does: xml_content is described as a raw XML string and file_path as an optional path subject to allowed-root checking. It also documents the mutual-exclusion rule (ValueError when neither is given) that the schema's empty defaults do not convey.

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 first sentence gives a precise verb+resource+target: 'Validate a FatturaPA XML document against the appropriate XSD schema.' This clearly separates it from siblings like extract_invoice_data or find_invoice_anomalies, which transform or analyze rather than schema-validate.

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 clearly explains the two invocation modes (file_path reads from disk, xml_content skips I/O), but gives no explicit guidance about when to choose this tool over sibling tools such as extract_invoice_data or find_invoice_anomalies. Usage is implied rather than routed.

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 behavioral burden. It discloses that the tool calls the official EU VIES REST endpoint, supports a configurable timeout, and returns a degraded response with source='unavailable' on timeout or service unavailability rather than raising an exception. This is valuable non-obvious behavior. It does not discuss rate limits or authentication requirements, but for a public verification endpoint those are less critical.

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 front-loaded with purpose, then behavioral caveats, then Args and Returns sections. The structure is logical and readable. It is slightly verbose because it repeats return-value details that an output schema already provides, but every sentence is informative and there is no filler.

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 that no annotations are provided and output schema exists, the description does a good job of covering purpose, key behavioral traits (timeout, degraded response), and parameter formats. It is nearly complete for an agent to call the tool correctly. The main gap is the lack of routing guidance versus sibling tools like check_piva, which leaves an agent to infer when this tool is preferable.

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 documents both real input parameters: country_code as a two-letter ISO 3166-1 alpha-2 code with an example ('IT'), and vat_number as the VAT number without the country prefix with an example ('12345678901'). It also mentions a 'ctx' argument that is not present in the input schema, which is a minor inconsistency, but the semantics for the actual parameters are well covered.

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 a specific verb and resource: 'Verify a VAT number against the EU VIES (VAT Information Exchange System).' It is clear what the tool does. However, it does not distinguish this tool from the similar-sounding sibling 'check_piva', nor does it mention any alternative verification tools, so sibling differentiation is absent.

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 what the tool does and includes behavioral caveats about timeouts, but it gives no explicit guidance on when to use this tool instead of alternatives such as 'check_piva'. There is no when-to-use or when-not-to-use instruction, and no mention of prerequisites or comparison to related tools.

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