Orlandu's Arcade

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

With no annotations, the description must carry the behavioral burden, and it does disclose meaningful traits: the dataset is exhaustive ('every ... card'), the returned columns, the filter matching behavior against two columns, and the CC BY 4.0 license. It omits any statement that this is a static read-only lookup (no rate limits, no mutation), which would be the only remaining 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?

Two sentences, front-loaded with the dataset scope before the filter mechanics, with no filler. The clause listing the four returned fields is slightly list-heavy but each element 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?

There is no output schema, so the description correctly compensates by enumerating the columns returned and the license. Combined with a fully documented single parameter and explicit no-query behavior, an agent has enough to call it correctly; only the read-only/static nature is left implicit.

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 already 100%, so the baseline is 3, but the description exceeds it by clarifying that the filter matches against BOTH the card model and the Fits column, and by supplying example values including a chassis prefix ('BVM-A') that reveal partial/prefix matching.

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 ('every Sony BKM option card for the CRT-era BVM and PVM monitors') and enumerates the fields returned (name, function, chassis fit, source). It is clear what the tool yields, but it never names or contrasts with the related sibling sony_monitor_specs, so an agent must infer the boundary 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?

It explains the filter is optional ('omit for all') and gives concrete example query values ('BKM-129X', 'PVM-20L5', 'BVM-A'), which implies when to pass a query. However, there is no explicit when-to-use guidance versus alternatives such as sony_monitor_specs, leaving the sibling choice to inference.

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 does disclose the dataset's contents and that the filter is optional, and the CC BY 4.0 note signals a static read-only dataset. However it says nothing about pagination, result size, or completeness of coverage across consoles.

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 single front-loaded sentence defines the resource and its returned fields, followed by the filter hint and license. Nearly every clause earns its place; the CC BY 4.0 line is useful attribution rather than padding.

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 no output schema, the description compensates by naming the three things it returns (modification, video output, cable chain). For a one-optional-parameter read tool with no annotations, that plus the filter semantics is close to sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% ('Substring of a console name; omit for all'), so the schema already documents the single parameter. The description's example ('Genesis') adds a mild concreteness value but no syntax beyond what the schema states; baseline 3 applies.

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 the resource concretely: how each retro console in the collection reaches a Sony professional monitor, enumerating the returned aspects (modification, video output, cable chain). It is clearly a read of a dataset, and the subject matter implicitly separates it from sony_monitor_specs, but no sibling is named 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?

Usage is only implied: the optional console filter with an example ('Genesis') tells the agent how to narrow results, but there is no statement of when to call this over sony_monitor_specs or bkm_cards, nor any exclusion. Adequate but leaves routing to inference.

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 supplied, so the description carries the full burden. It helpfully discloses the shape of each returned entry, but says nothing about ordering, page count/limits, or whether authentication is required for the site.

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 single tight sentence that front-loads the resource and follows with the returned fields and the mental model. 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?

With no parameters and no output schema, the description adequately covers what the tool returns and its discovery purpose. Minor gaps remain around result size and ordering, but nothing essential for calling it 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?

The tool takes zero parameters, so there is nothing to disambiguate; the baseline for a no-param tool applies. No parameter semantics are needed here.

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 (list) and resource (every page on orlandu.com) and even names the returned fields (title, URL, one-line description). It does not explicitly contrast itself with the similar siblings search_orlandu or read_orlandu_page, so an agent must infer the list-vs-search-vs-read distinction.

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 phrase 'the site map for an agent' implies the discovery/overview use case, but there is no explicit when-to-use guidance or mention of alternatives like search_orlandu for filtered lookup. Usage is 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 provided, the description carries the burden and does disclose the plan's key outputs: best shared signal, step-by-step cable chain, sync and 75-ohm termination rules, and whether a BKM card, mod, adapter, or converter is needed. It does not explicitly state that the tool is advisory/read-only, but the 'plan' framing strongly implies no direct mutation.

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 the core action and then packs the supported console and monitor lists into a compact form. It is dense but purposeful, with no obvious 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 planning tool with no output schema and no annotations, the description explains what the result should contain and which devices are supported. It could say more about handling unsupported combinations or the exact output format, but it is largely complete for an agent to invoke 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?

Schema description coverage is 100%, so the baseline is 3, but the description adds useful domain enumeration by listing supported consoles and monitors. It does not explain input normalization or behavior for unsupported model names, which keeps 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?

The description states a specific verb and resource: planning a connection from a retro console to a Sony monitor. It clearly scopes what the plan includes, but it does not explicitly differentiate this tool from siblings such as sony_monitor_specs, bkm_cards, or console_video_out.

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 implied use case is clear from the phrase 'Plan the connection from a retro console to one of the Sony monitors in the collection.' However, the description gives no explicit when-to-use versus when-not-to-use guidance and does not name alternative tools for related tasks.

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?

Without annotations, the description carries the full burden, but it only adds content details (game count, photos, CC BY 4.0 license). It does not disclose read-only nature, response format, pagination, or any behavioral traits beyond the resource itself.

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

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

Two short sentences with no wasted words; the key facts (game count, photos, license) are front-loaded. It is appropriately sized for a simple dataset 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 zero-parameter dataset tool with no output schema, the description gives the count and general content but omits details about the return structure or fields (e.g., what a game record contains beyond a photo). It is adequately complete but leaves minor gaps an agent might need to inspect by calling.

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 tool has zero parameters, so there are no parameter semantics to document. The baseline score of 4 applies because the schema is trivial and the description does not need to compensate for any parameter 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 identifies the resource (52 Nintendo PlayChoice-10 games with board photos) but lacks an explicit action verb, so the agent must infer that the tool returns this dataset. It is not a tautology, but it does not clearly differentiate from siblings beyond the unique dataset name.

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 when-to-use guidance is provided; the description only states the dataset contents and license. There is no mention of alternatives or conditions under which this tool should be selected over sibling tools.

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. It does disclose two real behavioral traits — output is full page text rendered as Markdown and images are stripped — but says nothing about truncation, pagination, permissions, or rate limits, even though the max_chars parameter implies output can be cut short.

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

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

Two compact sentences: the first states what comes back, the second tells the agent exactly what to supply, with examples. Nothing is padded, though the return-format sentence leads rather than the purpose, which slightly buries the verb.

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 read tool with no annotations and no output schema, the description covers the essentials: what is returned, in what format, and what input to give. It still omits what happens when max_chars is exceeded and whether the page must first be discovered via list_orlandu_pages, which matters given zero structured behavioral coverage.

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 only 50%, and max_chars has no description anywhere, so the description has to compensate — and it does for the required parameter by giving concrete accepted forms ('pvm-20l5-buying-guide' or a full https URL) that go beyond the schema's terse 'Slug or URL'. The truncation semantics of max_chars remain unexplained, keeping it out of 5 territory.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb+resource ('The full readable text of one orlandu.com page') and pins the output format ('as Markdown (no images)'). The word 'one' implicitly contrasts with the list/search siblings, but no sibling is named explicitly, so it falls short of a 5.

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 request for a slug or URL signals 'use this when you already know which page you want,' which distinguishes it in spirit from list_orlandu_pages and search_orlandu. There is no explicit when-to-use, when-not-to-use, or named alternative, so it stays at implied 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?

With no annotations, the description carries the full burden and does well: it discloses that data is live, that serials are masked by design, that emails never leave the server, and the dataset license. It omits what happens for an unknown model (empty vs error) and any rate/response-size expectations, which keeps it short of 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?

Front-loaded with the resource and scope, then the two modes, then privacy/licensing notes. It is dense but nearly every clause adds information; the CC BY 4.0 and export URL sentences are useful 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?

There is no output schema, so the description must describe return values — and it does for both modes (counts and records vs. masked serial, plate date, hours, condition, note, provenance, serial-range points). An agent has everything needed to call it and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single parameter already documents 'omit for the census summary', so the description adds little beyond restating that plus example model values already present in the schema. Baseline 3 is appropriate when the schema does the work.

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 concrete verb and resource: a serial-number census of surviving Sony BVM/PVM monitors kept by owners, with live data. The two operating modes (no argument = aggregate census; model argument = per-model entries) are spelled out, and the resource is distinct enough from siblings like sony_monitor_specs that an agent can route correctly.

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 tells the agent when to call with no argument (counts, per-model survivor counts, records) versus with a model (entries and serial-range points), including example model strings. It also points to the export URL for the full list, though it never states when to prefer sony_monitor_specs or other siblings instead.

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 useful behavior: the search index covers pages, cards, glossary terms and gallery captions, and results come back as snippet plus URL. It says nothing about ranking, pagination, or the 20-result cap, which are relevant for a search tool.

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

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

Three short sentences, front-loaded with the operation and scope, then the return shape, then the routing advice. Nothing is 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?

For a two-parameter read-only search with no output schema, the description covers the essential gaps: what is searched, what is returned, and how it fits with read_orlandu_page. The undocumented 'limit' behavior is the only meaningful omission.

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 'query' parameter is already documented in the schema with examples, and 'limit' has no description in either place (50% coverage). The description does add meaning by stating what the query is matched against (all site content types) and that results are ranked 'best-matching', which frames query semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Full-text search over every page, card, glossary term and gallery caption on orlandu.com'), and the indexed scope is explicit. An agent can immediately distinguish it from list_orlandu_pages and read_orlandu_page.

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 explicit sequencing ('Use it first, then read_orlandu_page for the full text'), naming the alternative and the condition that selects it. It stops short of saying when not to use search (e.g. browsing via list_orlandu_pages), so it is clear but not exhaustive.

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 disclosure burden. It implies a read-only dataset query through the listed fields and optional filter, but does not explicitly state absence of side effects or describe result ordering/format beyond the field list.

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 compact sentence front-loads the resource, fields, filter behavior, and license. There is no redundant or wasteful 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?

For a simple one-parameter lookup with no output schema, the description is complete: it identifies the dataset, enumerates returned attributes, and explains filter semantics.

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 already has 100% description coverage for the single model parameter. The description adds useful concrete examples ('PVM-20L5', 'BVM') that clarify substring matching behavior beyond the schema wording.

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 (Sony PVM/BVM/LMD monitor specifications) and enumerates the exact fields returned. It is clearly distinguishable from all sibling tools, none of which cover monitor hardware data.

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?

Explains that the model filter is optional and that omitting it returns all monitors, with concrete example filters. It does not name alternatives, but none of the sibling tools are plausible alternatives for this data.

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