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

Annotations already establish that the operation is read-only, non-destructive, and idempotent. The description adds the non-obvious behavioral nuance that the result exposes the underlying dataset's schema, which is central to the tool's purpose. This is useful context without contradicting 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 short sentences carry the full purpose, key identifiers, and return concept with no filler. The framing question is the only extra flourish and it helps an agent understand the tool's intent rather than adding noise.

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 one-parameter lookup with rich annotations and an output schema, the description covers the lookup key, scope, and core return component. It could include an explicit alternative-routing hint, but the low complexity makes the definition complete enough for correct invocation.

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 already documents the sole parameter at 100% coverage, so the description adds little new semantic weight. It restates that the value can be a mint address or a coin id, which matches the schema and provides no additional format or disambiguation details.

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 ('look up'), a single resource ('one coin'), and the accepted keys ('mint address or coin id'), then states the distinctive return value (the backing dataset and its schema). The framing question distinguishes it from sibling dataset tools by clarifying what question it answers.

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 gives clear context for use: when you have a coin identifier and want the coin along with the dataset behind it. It does not explicitly contrast with get_dataset or search_datasets, but the scope ('one coin' by its own id) makes the intended scenario unambiguous.

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 this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond those annotations: it explicitly states that the file is not public, that only schema and sample are returned, and that coin information is included when present. This is useful for setting agent expectations without contradicting 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 two concise sentences with no wasted words. It front-loads the primary action and resource, then immediately clarifies critical constraints about the file and what is returned.

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 an output schema present, the description does not need to detail return values, and it already covers the key non-obvious behavior: the file itself is not public, only schema and sample are returned, and coin information is included when available. For a simple one-parameter read-only tool with rich annotations, this is 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?

Schema description coverage is 100%, so the single parameter dataset_id is already fully documented in the schema, including its source from search_datasets. The description does not add parameter-specific detail, but that is acceptable given the baseline of 3 when schema coverage is complete.

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 ('Read') with a clear resource ('one dataset') and explicitly defines the scope: every column with type and description, one sample row, and any coin launched on it. It also distinguishes itself by clarifying that it returns only the schema and sample, not the file, which differentiates it from sibling tools like get_coin and search_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 this is the tool to use when you need the full schema/sample for one specific dataset, and the input schema says the ID comes from search_datasets. However, it does not explicitly state when to use this tool versus alternatives or name exclusion conditions, so the usage guidance is implied rather than 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?

Adds crucial behavior beyond annotations: no transaction is built or signed, the link expires, and the tool 'cannot spend anything on its own'. This complements readOnlyHint=false/openWorldHint=true by clarifying the side-effect profile (staging only, no spending) and the temporary nature of the URL.

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: first states purpose and outcome, second clarifies safety constraints and directs the agent to give the confirmUrl to the user. No filler or 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?

With an output schema and full parameter descriptions, the description covers the remaining context: the dataset prerequisite, the non-spending behavior, link expiration, and the next user action. Nothing essential for correct invocation 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?

Schema coverage is 100% and every parameter is described in the schema. The description adds no parameter-level detail beyond what is already available, though it reinforces that dataset_id must belong to this account. 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?

States a specific verb ('Stage'), resource ('pump.fun launch for a dataset this account uploaded'), and outcome ('return a link'). Distinguishes from siblings like get_coin or list_launchpad by focusing on the pre-transaction staging step.

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 clear context: only for datasets this account uploaded, and that it returns a user-facing confirm link. Does not name alternatives or explicit exclusions, but the sibling tools are functionally distinct, so the usage context is sufficiently 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?

Annotations already establish this as safe, read-only, and idempotent. The description adds behavioral value by specifying the 'newest first' ordering and the exact return fields, which go beyond what the annotations declare. No contradiction with 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?

A single sentence with no filler. The main action, ordering, and key output fields are all front-loaded, making it easy to scan and act on.

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 rich annotations, full parameter schema coverage, and presence of an output schema, this description is complete. It clarifies the scope ('launched on Realia datasets'), ordering, and result contents, so an agent has the necessary context to call the tool correctly.

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 already documents both parameters (limit and query) with 100% coverage, so the baseline is 3. The description adds no additional parameter-specific guidance, but it does not need to since the schema handles this fully.

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 ('List') and names the exact resource ('coins launched on Realia datasets'), with ordering and output fields. This clearly distinguishes it from siblings like get_coin (single coin) and search_datasets (dataset 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?

The description implies a clear use case: listing all Realia launchpad coins in reverse chronological order. It does not explicitly mention alternatives or exclusions, but the context is clear enough that an agent can select this tool over the listed 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is established. The description adds behavior beyond annotations: search across title/description/column name, empty query for newest entries, and the lightweight return shape (id, size, field names).

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 earn their place: the first scopes the operation, the second states the return envelope, and the third covers empty-query behavior and routes to get_dataset. No filler or repetition of schema fields.

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 read-only search tool with full schema descriptions and an output schema, the description covers what the tool returns, how to get more detail, and a special case (empty query). Nothing required for a correct call 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 already describes query and limit at 100% coverage, so the baseline is 3. The description adds meaning by clarifying that query matches title, description, or column name and that leaving it empty returns the newest datasets, which is not expressed in the schema descriptions.

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 ('Search'), a clear resource ('real-world datasets published on Realia'), and the fields searched (title, description, column name). It also distinguishes itself from get_dataset by stating what it returns, so an agent can tell it apart from 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?

It implicitly establishes when to use the tool: for lightweight discovery/searching by text, and explicitly routes follow-up needs to get_dataset for 'full schema and a sample row'. It does not discuss sibling tools like upload_dataset or list_launchpad, but those are not relevant to this search operation.

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 establish that this is a mutating, non-idempotent operation. The description adds meaningful behavioral context beyond that: the dataset becomes publicly searchable, the schema and sample row are inferred, content must be passed directly, and there is an 8 MB limit. No contradiction with annotations exists.

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?

Four compact sentences deliver the purpose, invocation detail, behavioral side effects, and constraint with no filler. The most important information is front-loaded, and 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?

Given the five parameters, annotations, and the presence of an output schema, the description covers the essential operational context: token ownership, raw content encoding, public visibility, schema inference, and the size limit. Return values are already handled by the output schema, so nothing critical 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?

Schema coverage is 100%, so the baseline is 3. The description adds genuine value by clarifying that `content` must be raw file text rather than a path, by narrowing supported formats, and by noting the size constraint. This supplements the schema without merely repeating it.

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 ('Publish'), a concrete resource ('dataset to Realia'), and the supported formats (CSV, TSV, JSON). It also scopes the operation to the token's account, making it unambiguous versus the read-oriented sibling tools like search_datasets, get_dataset, and list_launchpad.

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 this tool: when an agent needs to make dataset contents publicly available under the current token. It does not explicitly name exclusions or alternatives, but the sibling tools are functionally distinct enough that the intended usage is 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so safety is covered. The description adds token scope and the account/wallet result focus, but does not disclose additional behavioral details beyond what annotations and output schema already convey.

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 the behavior stated first and the usage trigger second. Every word earns its place and there is no 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?

With no parameters, strong annotations, an output schema, and a clear usage context, the description provides everything an agent needs to decide when to call this tool and what to expect.

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 the description carries no parameter documentation burden. Baseline 4 is appropriate because there is nothing for it to explain.

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?

Description uses a specific verb ('Show') and names the exact resource: the Realia account associated with the server's API token, plus its Solana wallets. This clearly distinguishes it from sibling tools like upload_dataset and launch_coin.

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 instructs 'Use this first when an upload or launch fails with an authentication error,' giving a concrete trigger and ordering relative to the sibling upload/launch operations. This is direct and actionable.

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