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

No annotations are provided, so the description carries the full behavioral burden. It states that it returns the exact API record, but does not disclose what happens for unknown or ambiguous symbols, or that max_chars may truncate 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 front-loaded and efficient: one sentence states the operation, followed by a compact args section that explains the accepted symbol formats. 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?

The output schema covers return values, and the symbol parameter is well explained. However, the lack of annotations, undocumented max_chars behavior, and absence of error/ambiguity handling leave meaningful gaps for an agent to infer.

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 a good job explaining accepted symbol formats (simple, qualified, member), but it entirely omits the optional max_chars parameter and its truncation behavior.

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 and resource: it returns the exact record for a Java API class, method, or field. This is clear enough to distinguish the operation from generic library browsing, though it does not explicitly contrast itself with the sibling anylogic_api_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?

Usage is implied by the requirement to supply a symbol name, and the examples make clear this is for known API symbols. However, there is no explicit guidance on when to use this versus anylogic_api_search or anylogic_search, nor are exclusions or alternatives stated.

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 exist, so the description carries the burden; it discloses that results are API signatures with parameters and types, which is real behavioral context. It says nothing about ranking, result truncation, whether limit narrows a broader match set, or any auth/rate considerations.

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 one-line purpose followed by a compact Args block, with no filler. Only mild redundancy between the parenthetical field list and the per-parameter notes.

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?

Having an output schema means return values need not be explained, and both params are covered. The remaining gap is sibling disambiguation: with anylogic_api and anylogic_search adjacent, the definition should clarify which surface answers which question.

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 description fully compensates for the query param with a definition plus three concrete example strings that reveal conceptual search rather than exact-name lookup. The limit param is described as the maximum number of results, though the default of 10 from the schema is not restated.

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 ("Busca clases y métodos en la API Java") plus the shape of what comes back (firmas, parámetros, tipos). It is clearly narrower than anylogic_search, but never names anylogic_api or anylogic_search to draw the contrast explicitly.

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 example queries ("seize capacity", "getEngine", "queue") imply that this is free-text conceptual lookup over the Java API, which helps the agent calibrate a query. However, there is no statement of when to prefer this over anylogic_api, anylogic_search or anylogic_examples, which matters against six siblings.

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 of behavioral disclosure. It implies a search/read operation through the verb "Busca" and explains the limit parameter, but says nothing about matching semantics, result ordering, coverage of tutorials, or safety/rate-limit behavior beyond what the schema already provides.

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 and front-loaded: the purpose is stated first, followed by clear argument notes. There is little waste, though the Args section could be slightly more formal in structure.

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?

An output schema exists, so return values need not be explained, and both parameters are covered. However, for a search tool in a family of sibling search tools, the description lacks crucial routing context: exactly what content corpus it searches and how that differs from anylogic_search or anylogic_api_search.

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 does reasonably well: query is explained as a concept or API with concrete examples like "resource pool animation" and "statechart transition," and limit is described as the maximum number of results. It adds meaningful semantics beyond the bare parameter names.

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 ("Busca") and resource ("ejemplos y fragmentos de código en tutoriales y modelado avanzado"), making the tool's domain clear. It is distinguishable from generic sibling searches by its emphasis on examples and snippets, though it does not explicitly name the sibling tools it differs from.

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?

Usage is implied by the mention of tutorials and advanced modeling, suggesting it is for finding illustrative examples rather than general documentation. However, there is no explicit guidance on when to use this instead of anylogic_search, anylogic_api_search, or anylogic_toc, and no exclusions are given.

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 burden. 'Lee' conveys a read-only operation, and it usefully discloses heading partial-matching and max_chars truncation behavior. It does not mention what happens on an unknown path (error vs empty) or result pagination, which for an unannotated tool is a gap.

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?

One-sentence purpose is front-loaded, followed by a compact per-argument list. No filler. The Args block is standard and each 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?

An output schema exists, so return-value explanation is unnecessary, and all three parameters are documented in prose. For a simple read tool this is nearly complete; only edge-case behavior (missing path, heading not found) is left unstated.

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 document all three parameters with meaning beyond the schema: path format with concrete examples, heading as a partial-match section filter, and max_chars as an output truncation cap. It omits the max_chars default (12000) and valid ranges, keeping it from a 5.

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: 'Lee una página o una sección concreta del corpus.' Clear what it does and that it operates on a path within the corpus. It distinguishes itself implicitly from anylogic_search by referencing the paths that search returns, but never names siblings explicitly.

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?

'path: ruta relativa devuelta por anylogic_search' implies the workflow (search first, then read). However, there is no explicit 'use this when' guidance and no differentiation from anylogic_api, anylogic_examples, or anylogic_toc for similar content retrieval.

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 does disclose reasonable behavioral detail — best results from exact identifiers, scope/section narrowing, and a 1–25 result cap — but says nothing about how results are ranked, whether snippets are returned, or cost/latency characteristics.

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 purpose line is front-loaded and the Args block is compact, one line per parameter. Slight redundancy in restating parameter names, but nothing 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?

As a read-only search tool with an output schema, the description covers what an agent needs to construct a call. The notable gap is disambiguation across five sibling tools (anylogic_api_search, anylogic_examples, anylogic_toc, anylogic_read), none of which is referenced.

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 largely does: it gives query examples ('Seize', 'take()', 'ResourcePool'), enumerates scope values with meaning, shows a section path prefix, and bounds limit to 1–25 (the schema only supplies a default of 8). Only the default value for limit is left unstated.

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: search the AnyLogic documentation (user guide and Java API). Clear what it does, but with siblings named anylogic_api_search and anylogic_examples the description never says how this differs from them, so an agent must guess which search surface to pick.

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?

Usage is only implied through the scope/section parameters ('guide' vs 'api', empty = everything). There is no explicit statement of when to use this versus anylogic_api_search or anylogic_toc, nor any exclusion 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?

No annotations are provided, so the description carries the burden. It discloses useful behavior — an empty scope returns a combined summary of guide and API — and implies a read-only enumeration, but says nothing about output size, whether the API map is paginated, or how deep the tree goes, which matters for a documentation index that can be large.

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?

A one-line purpose sentence followed by tight per-parameter notes; the most important framing (what the tool returns) is front-loaded and every line earns its place. The Args block leans on an indented list rather than prose, which is efficient but slightly less scannable.

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?

An output schema exists, so return-value explanation is unnecessary, and both of the two parameters are documented semantically. For a low-complexity, zero-required-parameter index tool with an output schema, this is essentially complete; only the section/scope interaction is left unstated.

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 largely does: it gives the allowed values for scope ('guide' or 'api'), explains the empty default, and clarifies that section is a section prefix for the guide but a package name for the API — a distinction the schema does not carry at all. Minor gap: it does not say whether section is ignored or errors when scope is empty.

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 resource: an index of the documentation containing sections, packages and available pages — an agent immediately understands this is a table-of-contents enumeration rather than content retrieval. It does not explicitly name the sibling tools it replaces (anylogic_read, anylogic_search), so the distinction is inferable but not stated.

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?

Usage is only implied: the tool is a map, so an agent infers it should be called first to discover structure before reading. There is no explicit 'use this instead of anylogic_search when...' statement, nor any prerequisite or coverage caveats beyond the default-behavior note that an empty scope returns a summary of both.

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 behavioral burden. It discloses the returned metadata fields, implying a safe read-only operation, but it does not explicitly state side effects, auth needs, or freshness guarantees. The output schema covers return shape, which lowers the bar.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence with no filler. It front-loads the key returned values and is appropriately sized for a zero-parameter metadata tool.

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 zero-parameter metadata tool with an output schema, the description is nearly complete because it identifies the returned metadata. The only small gap is the lack of explicit usage context relative to sibling tools.

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?

This tool has zero parameters, so there are no parameter semantics to explain. The baseline for a 0-parameter tool is 4, and the description adds no parameter-related confusion.

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 three specific metadata outputs: corpus version, document count, and build date. The resource is clear, though it does not explicitly distinguish itself from sibling tools such as anylogic_search or anylogic_toc.

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 only lists returned fields and gives no guidance on when to use this tool versus alternatives. An agent must infer that this is a corpus metadata lookup rather than a content retrieval or search operation.

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