Nalegalu MCP

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

Annotations already declare readOnlyHint=true and destructiveHint=false, signaling safe read operations. The description adds behavioral context by noting the lookup is deterministic and that users must have a trusted source for parameters. This goes beyond the annotations, though it stops short of explaining error handling or return edge cases.

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 the core purpose stated in the first sentence. Subsequent sentences add necessary detail about usage and parameters without any filler. Every line 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?

Given the tool's moderate complexity (3 params, output schema present) and read-only nature, the description fully covers purpose, usage, and all parameter semantics. It also explains relationships to sibling tools, addressing the context in which the tool should be invoked. The output schema handles return-value details.

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 schema provides zero description coverage, so the description must compensate—and it does thoroughly. It explains the address with an example (WDU19740240141), the article key with formats ('133' or 'Art. 133.'), and the optional paragraph key ('1' or '§ 1.'), including its narrowing purpose.

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 begins with a specific verb and resource: 'Fetch the exact text of one article (or paragraph) of a Polish legal act.' This clearly distinguishes it from sibling tools like search_law, which searches, or get_case, which fetches cases.

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 explicitly instructs when to use the tool: 'Use after search_law or another trusted source supplied the address and article key; never guess them.' This is direct usage guidance that also serves as a safety check, and it differentiates the tool from search_law.

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 readOnlyHint annotation, the description reveals essential behaviors: paging with has_more and offset=next_offset, bounded text excerpts, and fallback to fetching saos_url when ok=false. These details are not captured in annotations and meaningfully inform agent 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?

The description is three sentences, front-loaded with purpose, and every sentence carries essential operational detail (source restriction, paging, fallback). There is no filler or 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 that an output schema exists to document return values, the description covers all necessary contextual nuances: ID provenance, pagination, excerpt bounds, and graceful handling of SAOS unavailability. It is sufficient for an agent to invoke the tool correctly without further assumptions.

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?

With 0% schema description coverage, the description must add parameter meaning. It explains saos_id (must come from related_cases or search_case_by_signature) and offset (used for paging), but max_chars is not mentioned at all. This partial coverage is adequate but leaves a clear gap for one of the three 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: 'Fetch one SAOS court judgment (metadata + bounded text excerpt)'. The verb 'Fetch' and resource 'SAOS court judgment' are specific, and the bounded excerpt detail distinguishes it from sibling tools like get_article (articles) and search_case_by_signature (search results).

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?

Explicit guidance is given: 'Use only with a saos_id returned by related_cases or search_case_by_signature'. This narrows usage to a specific workflow. It also provides paging instructions and a fallback action when SAOS is unavailable, fully addressing when and how to use the tool.

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 readOnlyHint, the description details important behavioral caveats: related-case links may not be textually confirmed, uncertain relevance should be discounted, and mentions=0 or missing context means the link is unverified. It also explains how results are ordered by relevance score, which is valuable runtime behavior not stated in 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 main purpose is first sentence. All subsequent sentences carry substantive caveats or detail about result interpretation, and the warning about not treating counts as exhaustive is placed where it will not be missed. It is watchfull but not padded.

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 about return fields, sorting, data provenance differences between richer and older data, how to interpret mention_match and mentions, and the local non-exhaustive boundary of the dataset. Given the output schema exists for return structure, the tool-level description leaves no meaningful calling or interpretation gap.

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?

With schema description coverage at 0%, the description compensates by explaining address and article_key with a concrete example (WDU19740240141) and by mentioning paragraph-level detail implicitly through 'one statutory provision.' It does not explicitly describe paragraph_key or limit, but limit is simple and paragraph_key is optional with a null default, so the important calling semantics are 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 opens with a specific verb-resource statement: 'List SAOS cases related to one statutory provision.' It further defines the lookup as deterministic by act address and article key, which clearly separates it from siblings like get_case or research_cases. The scope is precise and actionable.

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 explicitly says 'Use after identifying a relevant provision,' giving a clear precondition. It also warns against treating related-case links as proof and directs the agent to inspect the judgment with get_case before asserting holdings. It further clarifies that a zero count is not an exhaustive SAOS or case-law search, providing both when-to-use and when-not-to-conclude guidance.

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?

Goes far beyond the read-only annotation by disclosing the internal refinement first search of then follow provision-to-case links then deduplicate by SAOS ID. Also reveals exact runtime behavior for rerank=false versus rerank=true, including satellite conditions such as Cohere reorderer config and the rerank_fetch_limit cap. This is strong behavioral transparency.

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?

Every sentence carries unique and decision-relevant information, and the opening line front-loads the primary purpose. The three-block structure moves cleanly from what to how to important cautions, with no purely ornamental text.

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 an output schema present, the description does not need to detail return values. It covers the discovery pipeline, rerank behavior, and behavioral limits. Slightly less complete for the full parameter matrix, but overall sufficient for an agent to invoke the tool with reasonable 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?

Schema parameter coverage is 0%, so the description must be nearly alone in explaining the 9 params. It meaningfully explains rerank, rerank_fetch_limit, and implicitly provision_limit via 'several provisions', but leaves candidate_limit, context_max_chars, case_text_max_chars, cases_per_provision, and rerank_result_limit undocumented. The star-heavy parameter semantics only partially compensates.

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 action and object: 'Discover related-case candidates for a natural-language legal issue'. It goes beyond the name by describing the discovery scope and clarifies that the tool does not decide holdings, which separates it from judgment-deciding tools like get_case.

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?

Provides clear situational guidance: this is a discovery stage, rerank=false is the inexpensive deterministic path, and successors should call get_case for any judgment used in an answer. It does not explicitly contrast itself with the sibling related_cases tool, so the guidance is not complete across all 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?

Goes well beyond the readOnlyHint: it discloses the local-first then live SAOS query behavior, explains the meaning of each verdict value, and highlights the implication of has_text=false for quote verification. This is rich, non-obvious behavior that directly affects interpretation of results.

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 dense yet compact, front-loaded with the purpose and example, then organized around verdict semantics and caveats. Every sentence adds needed information; there is no filler or 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 tool's moderate complexity and existence of an output schema, the description fully covers input expectations, output interpretation, edge cases (outside coverage, SAOS unreachable), and limitations (has_text=false). It leaves no significant question unanswered about when and how to use the 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?

The schema provides zero descriptions for parameters, but the description compensates by giving a concrete example for signature and enumerating the accepted court_type values. It does not explicitly explain limit, but its role as a result count is inferable from the name and default, making the gap minor.

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 a specific verb-resource pair, 'Verify a cited case signature', and provides an example format ('II CSK 448/14'). It clearly distinguishes itself from siblings like verify_quote and search_law by focusing on signature verification rather than quote checking or legal search.

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?

Explicitly states when to use the tool ('Use before relying on a judgment cited by a user or another agent') and provides decision rules for each verdict state, including directing users to CBOSA for administrative courts and warning against treating 'unverified' as nonexistent. It also references the sibling tool verify_quote in the context of has_text=false, effectively covering 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?

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description adds useful context: 'Results are primary-source material, not legal advice' and 'current Polish statutes,' clarifying scope and limitations. It does not describe pagination or rate limits, but the annotations cover safety traits.

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. Each sentence earns its place: purpose, when to use, example, filter guidance, and disclaimer. No redundant 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?

With 12 parameters and no schema descriptions, the description covers the common path well but leaves many tuning parameters unexplained. The output schema exists, so return structure is available, but the description alone is not enough to fully understand all 12 parameters.

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 the query parameter with a Polish example and clarifies when not to use the filter parameters (address, domain, etc.). However, advanced parameters like retrieval_mode, case_law_boost, and candidate_multiplier remain undocumented, leaving gaps.

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 'Search current Polish statutes and return article-level hits,' which uses a specific verb, resource, and output granularity. This clearly distinguishes it from sibling tools like get_article, get_case, and search_case_by_signature, which target specific documents or case searches.

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 explicitly says 'Start here' for natural-language issues or direct citations, and instructs not to add domain, address, article, act_type, or status filters unless the user provided them. This gives strong when/when-not guidance, though it does not name alternative tools explicitly, keeping it from a 5.

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 readOnlyHint and destructiveHint, the description discloses the exact verdict types ('exact', 'near_match', 'not_found'), the normalization behavior, and the fallback hint logic. This gives the agent a clear picture of what to expect without contradicting 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, front-loaded, and every sentence earns its place—purpose, usage prerequisites, verdicts, and failure handling. No fluff or 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 tool with an output schema and moderate complexity, the description covers what it does, when to use it, how to use it, expected verdicts, and fallback behavior. It is complete enough for an agent to invoke correctly without further 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?

Schema coverage is 0%, so the description carries the burden. It explains two parameters directly: 'Pass the saos_id... and the fragment as quoted.' It also implies the role of min_similarity via 'near_match (best passage plus similarity score)'. However, it doesn't explicitly explain how min_similarity is used or how to set it, which is 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 clearly states the tool's purpose: 'Check whether a supplied quote appears in a SAOS judgment.' It specifies the resource (SAOS judgment) and the action (verify quote), and distinguishes it from siblings by focusing on verification rather than retrieval or search.

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?

Explicit guidance is provided: 'Use only after resolving the judgment to a saos_id' and 'Pass the saos_id (from search_case_by_signature or related_cases)'. It also tells when not to retry: 'If ok=false with a fallback hint, fetch saos_url with your own web tools instead of retrying.' This clearly defines usage context and alternatives.

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