Dichiarazione-MCP

by roccoangelella

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools target distinct concerns: reading drafts, reading evidence, validating, mutating, reverting, and preparing review. However, read_declaration_draft and read_normalized_evidence both read bounded session-bound content and could be confused in scope. The naming clearly differentiates them by object type, so ambiguity is low.
Naming Consistency4/5
Tools follow a consistent verb_noun pattern (read_, validate_, mutate_, revert_, prepare_). The main deviation is session_status, which uses noun_status instead of a verb_noun form, though it's still readable and predictable alongside the pattern.
Tool Count4/5
Seven tools is a reasonable, slightly tight count for a declaration workflow server. Each tool earns its place and there's a clear lifecycle: read, validate, mutate, revert, review. It feels appropriately scoped with room to grow.
Completeness4/5
The set covers the core declaration workflow: read draft, validate, mutate, revert mutations, and prepare review. Minor gaps exist—there's no obvious create/get initial draft or final approval/submission tool—but the stated purpose (working with normalized synthetic drafts and evidence) appears largely covered.
Average 3/5 across 7 of 7 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
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This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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}
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden of behavioral disclosure. It adds one useful negative constraint (cannot issue or consume approval), which is valuable. However, it does not disclose what side effects occur, whether the operation is idempotent, what it returns, or what the 'prepare' step does internally to the review data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one clause), which is concise, but it may be under-specified rather than efficiently concise. There's no clear front-loaded benefit since there's very little content to begin with, and it reads more like a fragment than a structured tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description provides almost no operational context. For a tool that 'prepares' review data with version expectations and an external action enum, the description should explain prerequisites, what happens to the declaration state, and how this relates to the mutation/revert siblings. The single sentence is far too thin for the tool's apparent complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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, but it provides zero information about the single 'request' parameter containing ref, action, and expected_version. The schema's $defs are detailed, but the description adds no semantic meaning about what these fields mean in practice or how they map to review preparation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'prepare' plus resource 'declaration_review' makes the action somewhat clear, but it's vague about what 'preparing review data' actually produces or accomplishes. It does distinguish itself from siblings by clarifying it cannot issue or consume approval, which helps differentiate it from approval-related tools among the siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The statement 'this cannot issue or consume approval' provides minimal context about what this tool does NOT do, which indirectly hints at its narrow scope. However, there is no explicit when-to-use guidance, no exclusions beyond approval, and no mention of how it fits with the sibling tools like validate_declaration_draft or read_normalized_evidence.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Validate' suggests a read-only operation, but it's unclear whether this triggers side effects, requires a persisted session (via session_status), consumes server resources, or produces a deterministic result. The behavior around expected_version matching/mismatch is not disclosed. For zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short ('Validate one exact draft version'), which could be interpreted as concise. However, it borders on under-specification rather than efficient conciseness. It doesn't front-load any role of the tool's behavior or scope. It's not bloated, but it earns little.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has an output schema and a single required parameter, which slightly reduces the documentation burden. However, with no annotations, 0% schema description coverage, multiple sibling tools with overlapping domains, and a ref-based identity model that needs explanation, the description is notably under-specified. The description doesn't explain the role of expected_version, the validation criteria, or the relationship to other draft operations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the schema only provides type/constraint info for the 'ref' and 'expected_version' parameters, but not semantics. The description name 'expected_version' hints at version comparison, but the description doesn't explain behavior when the current version differs from expected. The nested DeclarationRef's taxpayer_alias, tax_year, and declaration_id are not explained. The 0% coverage means the description should compensate, but it doesn't.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description says 'Validate one exact draft version' which uses a specific verb (validate) and a resource (draft version), but it's very terse. It doesn't clarify what validation entails (schema checks? business rules?), nor does it distinguish from siblings like read_declaration_draft or prepare_declaration_review. The purpose is clear enough but lacks differentiating detail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this vs. alternatives. The sibling tools suggest related workflows (reading, mutating, reverting, preparing drafts), but the description gives no hint about when validation is appropriate, whether it's a prerequisite for submission, or how it differs from prepare_declaration_review. Usage context is entirely implied from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral disclosure burden. The description only restates that it reads a draft and gives no information about behavior—whether it's a pure read, what state it expects, what happens if the draft doesn't exist, or what the return value contains. There is a substantial gap in behavioral transparency for a tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short—a single sentence with efficient wording and no filler. However, it's under-specified rather than well-structured for clarity; brevity is achieved by omitting useful context rather than by tight editing of rich content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one parameter, no annotations, and no sibling differentiation. An output schema exists (so return values aren't the description's job), and the parameter schema is self-describing. For a simple single-ref lookup, the coverage is mostly adequate, but the lack of any usage context or relationship to sibling tools (especially mutate_declaration_draft and validate_declaration_draft) leaves a notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only 1 parameter (request) wrapping a DeclarationRef object with taxpayer_alias, tax_year, and declaration_id, all required and schema-constrained. Schema description coverage is 0%, but the schema is fully self-describing with patterns and constraints. The description ('read one draft') adds minimal semantics beyond the schema—it implies a lookup-by-reference pattern that's fairly clear from the DeclarationRef structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it reads 'one normalized synthetic declaration draft' with a specific verb (read) and resource (declaration draft). It distinguishes somewhat from siblings like mutate_declaration_draft and validate_declaration_draft, but the terms 'normalized' and 'synthetic' are jargon that don't clarify what the tool actually returns or does beyond the name itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this vs. siblings like read_normalized_evidence or validate_declaration_draft. The description doesn't explain whether this should be called before mutation, after normalization, or what scenarios call for it. Sibling tools with related purposes ('validate_declaration_draft', 'mutate_declaration_draft') suggest a workflow, but no workflow context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Exactly once' hints at idempotency semantics (potentially destructive/reversible-of-reversible), but the description doesn't state what the outcome is, whether the operation is reversible, concurrency implications (expected_current_version parameter implies optimistic locking), or what failure states look like. The 'exactly once' point is useful but leaves major behavioral gaps for a reverting/mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that establishes the core purpose in an efficient manner. It's appropriately short given the tool complexity is largely captured in the schema, though the ambiguous 'exactly once from its receipt' phrasing could arguably be clearer with one more modifier.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a complex mutation/revert tool with an output schema, nested input with concurrency semantics (expected_current_version), and no annotations. The description is too sparse given this complexity: it doesn't explain the revert process state machine, what happens to versions, or how the 'exactly once' guarantee is enforced. The output schema exists but the tool's behavioral semantics around versioning and receipts are under-documented.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and there is a single nested parameter 'request' with substantial structure. The description mentions 'receipt' but doesn't explain the expected_current_version parameter's role in optimistic concurrency, nor what the receipt input should contain. The schema defines the fields but the description adds no additional semantic guidance about how to construct the request or what the relationship between receipt and version is.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Revert one confirmed draft mutation exactly once from its receipt' uses a clear verb (revert), specific resource (confirmed draft mutation), and a distinctive qualifier ('exactly once from its receipt') that differentiates it from the sibling mutate_declaration_draft. It's clear this is an undo operation, but the phrase 'exactly once from its receipt' is slightly opaque without context on what 'receipt' refers to.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this vs. alternatives. The description implies usage (revert a draft mutation) but doesn't state prerequisites, such as the need to have a confirmed mutation receipt from a prior mutation, or any exclusions/constraints relative to sibling tools like mutate_declaration_draft or prepare_declaration_review.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With NO annotations provided, the description carries the full burden for behavioral disclosure. It conveys that results are 'bounded' (max_items capped at 64 in schema) and that it requires 'exact session binding' (security posture). However, it doesn't disclose read-only safety explicitly, response format/return shape, or whether the read mutates session state. For a read tool with zero annotations, it adds some value but leaves behavioral detail unexplained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is an efficient single sentence with no wasted words. Every element ('read', 'bounded', 'normalized synthetic evidence', 'exact session binding') carries meaning. It's slightly abstract in vocabulary but structurally tight. Not worth a 5 because it uses compressed jargon ('normalized synthetic', 'exact session binding') rather than plain language, which slightly hurts legibility.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a complex single request object with nested schemas (DeclarationRef, SessionIdentityBinding, field enum), an output schema, and no annotations. The description hints at bounded reads and session binding but doesn't explain the request composition, the meaning of 'normalized synthetic evidence', or the return semantics. Given the schema and output schema carry structural detail, a 3 reflects that the description is adequate but leaves meaning-level understanding to inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds essentially no parameter-level meaning beyond the schema. It mentions 'bounded' (mapping to max_items cap) and 'session binding' (mapping to session_instance_id/identity_binding), but schema description coverage is 0% for the single 'request' argument. The single request object is structurally complex (DeclarationRef, SessionIdentityBinding, field enums), and the description does nothing to explain how to compose it. Baseline 3 for coverage is undermined because zero coverage plus a single complex param shifts burden to the description, which it fails.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Read bounded normalized synthetic evidence with exact session binding' clearly states a verb (read) + resource (normalized synthetic evidence) plus a key qualifier (bounded, exact session binding). It distinguishes from siblings like mutate/revert/prepare, and the session binding element differentiates from read_declaration_draft (which reads the draft, not evidence). Loses a point for not naming the filter/scope mechanism explicitly in prose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states exact session binding as a constraint but provides no explicit guidance on when to use this vs read_declaration_draft, or vs the mutation tools. The sibling list includes closely related read/mutate/validate operations, yet the description offers no alternatives or when/when-not guidance. 'Bounded' and 'exact session binding' imply restrictions but don't instruct the agent on selection strategy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses two key behaviors: it is a mutation (write) operation and returns an exact rollback receipt (suggesting revert capability). However, with no annotations provided, the description carries the full burden, and it doesn't mention expected_version's optimistic-concurrency role, required evidence, or any rules about field-specific value types.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single clean, front-loaded sentence with zero waste. It states action, scope, and return value compactly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema presumably documents the rollback receipt, so the description needn't detail that. However, for a mutation tool with no annotations, key contextual details are missing: the expected_version concurrency mechanism, the need for evidence_ids (up to 16), the field-value-type coupling, and what the rollback receipt enables (revert usage). It's adequate but leaves consequential gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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. The description clarifies the single top-level `request` parameter (a MutateDraftInput) and reinforces that exactly 'one typed draft mutation' is applied, which adds meaning to the nested structure. However, it doesn't explain that only the relevant value field (text_value, boolean_value, decimal_value) should be set per the DraftField enum, which the schema itself doesn't clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource ('Apply one typed draft mutation') and clearly indicates the tool produces a rollback receipt. It distinguishes from siblings by focusing on mutation application, though it doesn't explicitly contrast with revert_declaration_mutation which is its natural counterpart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is the write operation for editing a draft, but offers no explicit guidance on when to use mutate_declaration_draft versus revert_declaration_mutation or validate_declaration_draft. No exclusions are stated, but the tool family makes the purpose reasonably inferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly notes it returns state 'without session secrets', which is valuable behavioral disclosure for a status tool (signals the agent won't get sensitive data). However, with no annotations, the description carries the full burden, and it doesn't specify what 'state' includes—whether results are structured, whether it's a snapshot vs live check, or any polling/reload behavior. It's minimal but does add some transparency value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence, zero waste, front-loaded with the core function ('Return coarse browser/workflow state'). This is appropriately terse for a tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There are no parameters, no annotations, and no sibling-tool overlap issues; the description covers the main function. The 'without session secrets' qualifier is the only behavioral note, and for a status-checking tool with zero params and no output schema concerns, this is reasonably complete. A slightly richer statement of what 'coarse' state means would help, but the tool is simple enough that this is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no schema burden to compensate for. Description coverage is 100%. There is nothing for the description to add regarding parameters since none exist—baseline 4 applies per the rubric's no-params rule.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'Return' plus resource 'coarse browser/workflow state' gives a specific, clear purpose. It effectively distinguishes itself from the sibling tools (which are read/mutate declaration operations) by focusing on session/browser state rather than declaration drafts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit when-to-use guidance or statement of alternatives. The sibling tools are all declaration-focused (read/mutate/validate/revert), so the purpose implicitly separates them, but the description doesn't explicitly say 'use this to check state before mutating' or give any decision context. The guidance is implied only.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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