Japan AMeDAS Observations MCP

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

No annotations, so the description carries the whole behavioral burden, and it discloses little. 'PRO' vaguely signals a paid/subscriber tier and the schema's access_token implies auth, but the description never confirms the requirement or explains what happens without it. 'Latest map' is undefined — how stale, how many stations, what ranking direction — and there is no word on return shape or limits.

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 front-loaded sentence with no filler; the tier marker leads and the core statement follows. Its brevity is efficient rather than rambling, though the terseness edges toward under-specification.

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 annotations and no output schema, the description has to cover auth expectations, the meaning of 'latest map', and result structure — none of which appear. A caller knows roughly what they get but not what they need to supply or how to interpret the response.

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 50%: access_token is documented in the schema but limit has no description anywhere. The description adds nothing about parameters — not whether limit controls how many warmest/coldest entries come back per side, nor its default or maximum, which is the single most likely misuse for this tool.

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?

Names a specific resource and result set: warmest and coldest AMeDAS stations from the latest observation map, ranked by temperature. That distinguishes it from sibling station (individual station data) and latest_obs_time (observation timestamp). The verb is only implied ('returns'), but the outcome is 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?

Intended use is inferable — fetch top/bottom temperature stations for the latest map — but there is no explicit statement of when to pick this over station or latest_obs_time, and no preconditions are spelled out. The 'PRO' prefix hints at a gated tier without explaining what that means for the caller.

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 behavioral burden. It usefully discloses that the tool is free, that the timestamp is published by JMA, and that station samples are conditional ('if present'), but it omits freshness/update cadence, timestamp format/timezone, and return 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 one compact sentence after the 'FREE' marker. It is front-loaded with the core resource and contains no redundant or wasted phrasing.

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 read tool, the description gives enough to understand the broad purpose, but without an output schema it leaves the return shape vague: it does not say what the timestamp looks like or what station fields are included in the sample.

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 zero parameters, so there are no parameter semantics to explain. The baseline for a no-parameter tool is 4, and the description does not introduce confusing parameter-related claims.

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 resource and scope: the latest AMeDAS observation timestamp from JMA, plus a conditional sample of Tokyo-area stations. It does not explicitly distinguish this tool from the sibling tools `extremes` and `station`, which keeps it 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?

It implies the tool is for checking the latest observation time, but gives no explicit guidance on when to use it instead of `extremes` or `station`, and no prerequisites or exclusions are 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 are provided, so the description must carry behavioral context. It states the scope but omits return shape, authentication requirements, data freshness, error behavior, and whether the observation is read-only in a meaningful way beyond the noun 'observation'.

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 efficient sentence with the resource and timestamp scope front-loaded. The unexplained 'PRO.' prefix is slightly ambiguous, but overall there is little wasted 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 no annotations and no output schema, the description should explain more about the returned observation and operational requirements. It gives only the basic scope, leaving important context for a data-retrieval tool unspecified.

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 50%, with access_token documented in the schema but station_id only typed as a string. The description partly clarifies that station_id is an AMeDAS station ID, but it gives no format, examples, or constraints for that required parameter.

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 identifies the resource and scope: an observation for one AMeDAS station at the latest timestamp. It is specific enough to separate this from latest_obs_time and extremes in practice, though it does not explicitly name or contrast those siblings.

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 'one station at latest timestamp' scope implies when the tool is appropriate, but there is no explicit guidance on when to use it instead of siblings such as latest_obs_time or extremes. Usage is inferable but not directed.

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