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

No annotations are provided, so the description carries the burden. The description is honest about what it does (explains) and doesn't claim any side effects. However, it doesn't disclose what the output will look like (e.g., a text summary, a list of sources, etc.) or any limitations. It's a simple informational tool, so a 3 is appropriate.

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 sentence, front-loaded with the verb 'Explain' and the resource. It is concise and to the point, with no wasted words. It earns a 5 for efficiency.

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 the tool has no parameters, no output schema, and no annotations, the description is quite complete for its purpose. It tells the agent what the tool does and hints at its role in the broader MCP (combining sources). It could be slightly more explicit about the output format, but for a zero-parameter informational tool, it's 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?

The tool has zero parameters, and the schema description coverage is 100% (vacuously, since there are no parameters). The description doesn't need to explain parameters. The baseline for 0 params is 4, and the description adds value by explaining the tool's purpose, so a 4 is justified.

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: to explain what the MCP is useful for and how an agent should combine its sources. It uses a specific verb ('explain') and identifies the resource (the MCP's utility and source combination). It distinguishes itself from sibling tools, which are all data-fetching tools, by being a meta/guidance tool.

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 implies when to use this tool: when an agent needs an overview of the MCP's capabilities and how to combine sources. It doesn't explicitly state when not to use it or name alternatives, but the context of sibling tools (all data-fetching) makes the usage context clear. It could be improved by explicitly stating 'use this first to understand the MCP before using other 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 must carry the transparency burden. It describes a fetch operation (implying read-only), but does not disclose error behavior for invalid source_key, any limits (e.g., rate limits), or that the content is curated. This is adequate for a simple read tool but lacks depth, meriting a mid-range score.

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, front-loaded with the action, and every word is purposeful. The first sentence states the tool's function; the second gives a crucial usage hint. Zero fluff, ideal conciseness.

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 simplicity (2 params, no output schema, non-nested), the description fully covers what an agent needs: what it does, how to obtain the key, and a default for excerpt length. No ambiguity remains about invocation or expected result.

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 covers 100% of parameters with descriptions. The description adds value for source_key by specifying it can be a number, title keyword, or URL fragment from get_sources, which enriches the schema. For max_chars, it adds no extra info, but the overall parameter guidance is clearer than the schema alone.

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: 'Fetch a short text excerpt from one curated source URL.' It uses a specific verb ('fetch') and resource ('source excerpt'), and distinguishes itself from siblings like get_sources (which lists sources) and search tools. The purpose is immediately obvious.

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 provides clear context on how to use the tool: 'Use source_key as a number, title keyword, or URL fragment from get_sources.' This implies a workflow (get sources first), which orients the agent. It doesn't explicitly mention when not to use it or alternatives, but the context is sufficient given the sibling list.

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 burden of disclosing behavioral traits. It mentions it uses the official public API, which implies a network call and possibly rate limits or versioning, but it doesn't specify if the tool is read-only, what happens if the dataset is not found, or whether it requires authentication. The information provided is minimal but not misleading.

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 sentence with no filler. It is concise and front-loaded with the key information: what it does (inspect), on which resource (data.gouv.fr dataset), and how to identify it (slug or id). This is excellent conciseness.

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?

This is a simple tool with one parameter and no output schema, so the description could be minimal. However, given the lack of annotations and output schema, it could have added a bit more about expected return value or error behavior. Still, for a basic inspection tool, the description covers the core action and the target resource adequately.

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 provides a description for the single parameter 'dataset' as 'Dataset slug or id', and the description repeats the same information ('by slug or id'). Since schema coverage is 100%, the description adds no additional semantic meaning beyond the schema. 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 the tool's purpose: inspecting a single data.gouv.fr dataset by slug or id via the official public API. It names the specific resource (data.gouv.fr dataset) and the intended input type, which is sufficient to distinguish it from the sibling tools that handle other resources like HDX, GDACS, or weather 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?

The description implies the tool is for inspection (read operation) via the official public API, which gives a hint for when to use it: when you need a single dataset's details from data.gouv.fr. However, it does not explicitly state when not to use it or provide alternatives (e.g., use search_data_gouv to find datasets, or get_sources to list sources).

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 bears full responsibility for disclosing behavior. It does not mention that the operation is read-only, any potential side effects, rate limits, or data freshness. It only states the action without transparency about constraints or side effects.

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, concise sentence that directly states the tool's function without any superfluous words. It is well-structured and easy to parse.

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 simplicity (two parameters, no output schema), the description adequately conveys the purpose and the main filtering behavior. While it could explicitly mention the return format (e.g., parsed entries vs. raw RSS), it is sufficiently complete for a straightforward fetch operation.

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 provides descriptions for both parameters (limit and query) with 100% coverage. The tool description does not add any additional semantic meaning beyond what is in the schema, so baseline score 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 the tool's purpose: fetching GDACS RSS and returning recent alert entries with a filter. It specifies the resource (GDACS RSS) and the action (fetch/return), and it is distinct from sibling tools which deal with other datasets.

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 implies usage for GDACS alerts but does not explicitly compare to alternatives or provide context on when to use it. The tool name and sibling context make it fairly obvious, but explicit guidance is lacking.

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 full burden. It indicates a non-mutating listing action and describes the curation criteria ('official and high-value'), but it does not disclose whether the list is static or fetched live, whether pagination applies, or what kinds of sources are included.

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, front-loaded sentence with no filler or repetition. Every word contributes meaning.

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 parameterless list tool, the description is adequate, but it leaves some ambiguity about what counts as a 'source' and what the returned list contains. Since there is no output schema, a bit more detail about the result format or source types would improve completeness.

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 and the schema is fully covered, so no parameter explanation is needed from the description. The description adds reasonable semantic context about the source selection, which is sufficient for a parameterless 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?

The description clearly states that the tool lists curated official and high-value sources for Madagascar, with a specific verb ('List') and resource ('sources for Madagascar'). It is unambiguous, though it does not explicitly distinguish itself from siblings like madagascar_list_reference_items.

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 guidance is provided about when to use this tool versus sibling tools such as madagascar_search_hdx_datasets or madagascar_search_data_gouv. The description implies a general listing purpose but offers no exclusions, prerequisites, or alternative recommendations.

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. The word 'Fetch' implies a read-only operation, and naming Open-Meteo as the source is useful context. However, it doesn't disclose response format, potential errors, data freshness, or other behavioral traits beyond the basic fetch action.

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 with no filler. It efficiently communicates the action, source, and location options without 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?

The description is sufficient for a simple weather fetch tool: it states what, from where, and the location options. Full parameter details live in the schema, and the lack of an output schema is acceptable given the straightforward nature of weather retrieval. A minor gap is that it doesn't hint at what weather fields will be returned.

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% with each parameter already described, including the conditional relationship between city and lat/lon. The description's reference to 'a Madagascar reference city or explicit coordinates' reinforces but does not meaningfully expand the schema's parameter semantics.

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 ('Fetch'), identifies the resource ('current weather from Open-Meteo'), and clearly scopes to Madagascar reference cities or explicit coordinates. This distinguishes it from the sibling data-search and alert 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 description implies usage for weather queries and clarifies the two ways to specify location (city or coordinates). However, it does not explicitly mention when to use this tool over its siblings, nor does it state any exclusions or 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?

No annotations are provided, so the description carries the burden. It states 'when available', hinting that it might sometimes be empty or unavailable, but does not elaborate on any side effects or return format. For a simple list tool, this is adequate but minimal.

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?

Single sentence, succinct, directly states purpose. No fluff.

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 listing tool, the description is sufficient. Could mention what type of reference items, but given the tool's simplicity and no output schema, it's adequately complete.

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?

There are 0 parameters, so the schema is empty. The description mentions nothing about parameters, but there are none. Baseline for 0 params is 4, which 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 verb 'List' and the resource 'built-in reference items for this MCP', which distinguishes it from sibling tools that search external datasets or sources. It's specific and 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 indicates that this tool lists built-in reference items, implying it's for accessing internal static data. It doesn't explicitly state when not to use it or mention alternatives, but the simplicity of the tool (no parameters) and its distinct purpose from siblings make usage clear.

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 mentions 'official public API,' suggesting a read-only operation, but does not explicitly state side effects, rate limits, or error behavior. It also omits any indication of whether the operation is safe or destructive, leaving room for assumptions.

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, concise sentence that directly states the tool's purpose. It avoids unnecessary words and is well-structured for quick comprehension.

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 the basic functionality but lacks context about return format, additional behaviors, or typical use cases. Given the presence of sibling tools, it would benefit from mentioning that this is specifically for French open data, but it remains minimally complete.

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 provides descriptions for both parameters (query and page_size), covering semantics adequately. The tool description does not add further detail about the parameters, but since schema coverage is 100%, the 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 the action (search), resource (public datasets), and target platform (data.gouv.fr) using the official API. However, it does not explicitly differentiate from sibling tools like madagascar_search_hdx_datasets, though the mention of data.gouv.fr implies a 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?

There is no guidance on when to use this tool versus alternatives, such as madagascar_search_hdx_datasets. It lacks context about which data source is preferred or any exclusions, making it unclear for an agent to decide between similar search 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?

No annotations are provided, so the description carries the full burden. It does not disclose side effects, read-only nature, rate limits, or error behavior. The mention of CKAN API is minimal and does not address behavioral details.

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 concise sentence with no unnecessary information. It is well-structured and to the point.

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 minimal and does not explain the return format or any output specifics. Given the simple schema and lack of output schema, it is somewhat complete, but could benefit from stating what the search returns (e.g., list of datasets).

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?

Both parameters (rows and query) are described in the schema with clear meanings. The tool description adds no additional context beyond the schema, so it meets the baseline for high 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 purpose: searching HDX for Madagascar datasets via CKAN API. It distinguishes from sibling tools like search_data_gouv and get_dataset.

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 implies usage for HDX Madagascar searches, but does not explicitly contrast with alternative search tools or state when not to use it. It is clear enough for the intended use case.

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