renkeimap-info

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

Beyond the annotations (readOnlyHint, destructiveHint), the description adds meaningful behavioral details: the output structure (each cell = tags + lead + source URL + survey date), optional marks derived from a declared rule file, and the fact that it shares a generator with /compare pages. It also clarifies that no ranking or scoring is produced. This adds context beyond the 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 concise and front-loaded with the main purpose. It packs useful details without redundancy, though the list of ledger items and the output format sentence are somewhat dense. Overall, it's efficient with 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?

For a simple tool with one array parameter, the description covers the essential details: the comparison criteria, the cell structure, the marks behavior, and the 'single throat' generator consistency. No output schema is provided, but the description explains the return format well enough. Missing are error handling or edge cases (e.g., duplicates), but these are minor for such a 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 only parameter 'slugs' is fully documented in the schema (description, minItems, maxItems), so schema coverage is high. The tool description adds a small nuance by saying '2-5 published systems/tools', which clarifies the requirement of being published, a slight enhancement over the schema but not substantial. Baseline of 3 is appropriate.

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 a specific action ('side-by-side comparison') and a specific resource ('published systems/tools on ledger items'), with concrete examples of ledger items. It does not explicitly name sibling tools but differentiates by noting 'No ranking, no score' and 'Same generator as the /compare pages', which helps disambiguate.

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: use when you need a side-by-side comparison of 2-5 systems on predefined criteria. However, there is no explicit 'when not to use' or mention of alternatives (e.g., if you need ranking, use another tool). The 'No ranking, no score' note hints at exclusions but does not direct the agent to a specific sibling.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description is not required to restate safety. It adds useful behavioral context: returns at most 10 candidates with page URLs, and it operates on a 'published ledger.' No contradiction with annotations, and the added details are valuable for an agent.

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 with zero waste. The primary purpose is front-loaded, the result limit and URL output are stated, and the alternative tool is mentioned succinctly. Every sentence 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?

For a simple search tool with one required parameter and no output schema, the description is complete. It tells the agent what to search, what to expect (max 10 candidates with URLs), and points to the sibling for deeper facts. Nothing an agent needs to call it correctly is missing.

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

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

The input schema covers 100% of the parameter documentation, including examples ('kintone', 'マネーフォワード', '会計'). The description repeats the search dimensions (name/vendor/category) which aligns with the schema but does not add new semantic details beyond what the schema already provides. Baseline 3 is appropriate.

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 action ('Search the published RenkeiMap ledger'), the resource (Japanese business systems), and the search dimensions (name/vendor/category). It also distinguishes itself from the sibling renkeimap_system_facts by noting that tool is for facts of one system, so the agent can tell them apart without opening schemas.

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 names the alternative tool (renkeimap_system_facts) and the condition that selects it ('for the facts of one system'). It also specifies the search criteria (name/vendor/category) and the result limit (at most 10 candidates), giving clear when-to-use 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?

Annotations already mark the operation read-only and non-destructive, and the description adds meaningful behavioral context: the data is static, sourced from public vendor pages, entries carry sources, and no user data is involved. This goes beyond the annotation baseline and helps the agent understand reliability and privacy expectations.

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: it opens with the tool's content scope, then adds sourcing, privacy, and citation notes. Every sentence contributes useful information without repetition or 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?

For a simple one-parameter static lookup, the description covers what results contain, their source, the lack of user data, and the need to cite. With no output schema, it could add a bit more about the response shape, but the stated content types and 'entries' references are sufficient for an agent to select and invoke it.

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% for the single slug parameter, including an example ('kintone'), so the parameter semantics are already fully documented. The description does not add param-specific detail, but no further clarification is needed at this complexity level.

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 title ('エラー文の対処を返す') and description both specify that the tool returns the meaning/handling for vendor error messages for a single system, along with support URLs and login method shape. The 'one system' scope plus the concrete content list clearly distinguishes it from siblings like renkeimap_compare or renkeimap_status.

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 does not state when to prefer this tool over its siblings, nor does it name alternatives. It only implies usage through 'static public knowledge for one system' and adds 'no user data,' but there is no explicit routing or when-not-to-use 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?

Beyond the read-only/destructive annotations, the description adds meaningful behavioral context: results are measured from public primary sources, unmeasured pairs are explicitly reported as such rather than guessed, and the included citation line must be cited when used. These details are non-obvious and useful for trusting and attributing outputs.

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 yet information-dense: three sentences cover purpose, output style, data provenance, honesty guarantees, and attribution requirements. It is front-loaded with the core purpose and contains no filler or redundant restatement of the title.

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 two simple slug parameters Wang and annotations confirming safe read-only behavior, the description covers the essential context well. It explains what the tool returns, adds caveats about unmeasured pairs, and includes the citation requirement. It does not spell out exact output serialization, but for this low-complexity tool that is not a critical 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?

The input schema fully documents both parameters ('a' and 'b' as system slugs), so the description does not need to repeat their meaning. The description only refers to 'two systems' generically and adds no additional syntax, constraints, or examples beyond what the schema already provides. Baseline 3 is appropriate given 100% schema coverage.

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: it determines whether and how two systems can exchange data, with per-method verdicts and field-name overlap. The title reinforces this by saying it returns whether two systems connect. It is specific enough to be distinguished from general system-fact lookups, though it does not explicitly name sibling tools.

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 intended use is implied: call this tool to check data-exchange compatibility between two systems. However, it gives no explicit guidance about when to prefer this over sibling tools like renkeimap_compare, and it does not state any exclusions or alternative scenarios.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it's an external observation (not vendor official), returns a closed-vocabulary conclusion, and includes a critical requirement to relay the disclaimer and citation. This goes beyond the annotations and informs the agent of expected output semantics and user-facing obligations. It does not contradict 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 two sentences with zero fluff. The first sentence front-loads the purpose and output components, and the second sentence delivers a critical user-facing instruction. Every word earns its place; it is concise without sacrificing necessary detail.

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 is largely complete for a read-only status tool with one parameter and no output schema. It specifies the return contents (conclusion sentence, time, state bar, maintenance) and the mandatory disclaimer relay. It does not detail the exact format of the state bar or error behavior, but these are minor gaps given the annotations and the simplicity of the tool. An agent can call it correctly and interpret the output sufficiently.

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 input schema has 100% description coverage for the single required parameter 'slug', with an example. The tool description does not add any additional parameter semantics beyond what the schema already provides. Since the schema fully documents the parameter, a baseline of 3 is appropriate; no compensation is needed.

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: 'returns current operational status of one Japanese business system'. It clarifies the external third-party nature and distinguishes from vendor announcements, which helps differentiate from sibling tools like renkeimap_system_facts. The output components (conclusion sentence, last-observed time, state bar, maintenance) are enumerated, making the tool's purpose unambiguous.

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 clearly implies when to use it (when operational status is needed) and even clarifies that it's not the vendor's official announcement, which is a useful context. However, it does not explicitly name sibling tools or state when NOT to use this tool (e.g., when official status is required, use vendor API). The 'Always relay disclaimer' instruction is a usage requirement but not a selection guideline. With no explicit alternatives mentioned, the guidance is adequate but not fully explicit.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds the citation requirement ('Every response carries a citation line; when you use these facts in an answer, cite it') and specifies the data provenance (survey-dated, first-hand). This is useful behavioral context beyond the 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?

Two sentences with no waste. The primary purpose is stated first, then the citation instruction. Every sentence 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?

The description lists the content categories and mentions sources and citation, which is sufficient for an agent to know what it will receive. Since there is no output schema, this compensates well. It doesn't detail error cases or pagination, but for a read-only facts tool this is adequate.

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%, so the slug parameter is already documented in the schema. The description does not add any additional meaning to the parameter, just repeats the tool's purpose. 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 clearly states it returns 'First-hand, survey-dated facts about one system' and enumerates the types (auth method, rate limits, CSV surfaces, pricing pages). This is specific and distinguishes it from siblings like renkeimap_compare which implies multiple systems. The title is redundant but the description is clear.

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 implies usage by requiring a slug from renkeimap_find_system, indicating a prior step. However, it does not explicitly state when to use this versus alternatives like compare or find, nor does it state when not to use it. It gives context but no exclusions or explicit routing.

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