Sell My Data To AI

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 mitigates this by disclosing the scope of returned content (fees, custody, vetting, legal entity, contact), which tells the agent this is a safe informational read, but it never states read-only behavior, freshness, or whether the facts are static or dynamic.

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, and the topic list is the useful part. It is a noun phrase rather than a sentence with a verb, which makes it read like a table of contents entry, but nothing is wasted.

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, zero-annotation, no-output-schema read tool, the description covers what an agent needs to decide to call it: the subject matter of the returned facts. It falls short only on routing against siblings, which is a usage concern rather than missing content.

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 for the description to disambiguate beyond what the empty schema already shows. Baseline of 4 applies since no parameter meaning is required.

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 enumerates the exact subject matter returned (fees, data custody, vetting standards, legal entity, contact), which is far more specific than the name 'get_facts' alone. It is clear this is a static informational lookup rather than a data query, but it never explicitly distinguishes itself from siblings like 'search' or 'get_page', leaving differentiation to inference.

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 when-to-use guidance and no alternatives are named, despite four plausible sibling tools (get_page, search, list_data_buyers, list_services). An agent must guess whether to call this versus searching or fetching a page for the same content. The only implicit signal is that this covers fixed policy/company facts rather than listings.

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 usefully reveals the return format ('full Markdown text'), which an agent could not infer otherwise, but says nothing about missing-path behavior, auth, or rate 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?

A single front-loaded sentence with the verb, return type, scope, key parameter, and an example — 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 one-parameter read tool with no output schema, the description covers what is returned and how to address the resource. It stops short of error behavior or pointing to 'search'/'list' tools for path discovery.

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 0% and the schema only declares a bare string, so the description does real work by defining the parameter as a site-relative path and giving a concrete example ('/guides/how-to-sell-data-to-ai-companies').

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 ('Get the full Markdown text of a page') and scopes it to one site, sellmydatatoai.net. It is clearly distinct from siblings like list_services or get_facts, though it does not explicitly reference them.

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: call it with a path to fetch a page's content. There is no guidance on when to prefer it over 'search' for discovering content, nor what to do if the path is unknown or invalid.

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 it partially meets it by disclosing the record contents an agent will receive — payouts, size requirements, exclusivity, and fees. It says nothing about ordering, total count, freshness, or cost/auth requirements, and it does not clarify that the result set is unfiltered and unpaginated (the tool takes zero parameters).

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 compact sentence with zero filler, and the most discriminating detail (AI training data buyers) is front-loaded. The only structural weakness is that it is a noun phrase fragment rather than a statement of what the tool does.

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 read-only listing tool with no annotations and no output schema, the description supplies enough about the entity type and returned fields for an agent to decide whether to call it. Missing only secondary context such as list size or ordering, which is minor at this complexity level.

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 no parameters, so there is no parameter semantics to explain; baseline 4 applies by rule. The description's field list is a bonus not required at this dimension.

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 resource ('Companies that buy, broker or market data for AI training') with clear qualifying criteria, which distinguishes it from siblings like list_services and get_facts. It falls short of a 5 because there is no verb at all — the agent must infer that this is a list/retrieval tool and whether it returns a collection or details of one company.

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 given, no alternatives are named, and no exclusions are stated. The agent has to infer usage purely from the name and the sibling list (search, get_facts, get_page, list_services).

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. The phrase 'with URLs' hints that each entry includes a link, and the 'list_' naming implies a read-only enumeration, but there is no explicit statement about safety, exhaustiveness, ordering, or pagination.

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?

It is a single short fragment with no wasted words, but the fragmentary grammar ('The brokerage services ... offers, with URLs') reads as an incomplete sentence rather than a front-loaded statement of what the tool does.

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, no-output-schema listing tool the description is nearly sufficient, telling the agent it returns brokerage services with URLs. It stops short of saying whether the list is complete or how it relates to the sibling buyer/service listings.

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 and the schema is an empty object, so there is nothing for the description to disambiguate. Baseline 4 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 names the resource ('brokerage services Sell My Data To AI offers') and notes it includes URLs, but it is a bare noun phrase with no verb, so the read/list action comes only from the tool name. It does not distinguish itself from the similarly-shaped sibling list_data_buyers.

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 statement of when to call this versus get_facts, get_page, search, or list_data_buyers. An agent must infer that this is the catalog lookup for service offerings.

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 and discloses almost nothing behavioral: no statement about result ordering/relevance ranking, result shape, whether the search is fuzzy or exact, or how the limit parameter affects output. The only hint is that it spans multiple content types.

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. The enumerated list is long but each item carries information about coverage, so it earns its place; it stops short of 5 only because the list could be grouped more crisply.

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 search tool with no annotations and no output schema, the description should explain at least the result behavior and the limit parameter. Neither is covered, leaving the agent unable to predict result count or shape before invoking.

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%: 'query' is documented in the schema, but 'limit' has no description anywhere. The description adds no meaning to either parameter — it does not mention the limit, default result count, or anything about query syntax beyond the examples already in 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 ('Search') and enumerates the resource types covered (guides, industry pages, training data types, buyer profiles, glossary terms), so an agent knows exactly what corpus is queried. It does not, however, distinguish itself from siblings like get_facts or list_data_buyers, so the boundary is left implicit.

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 explicit instruction on when to use this tool versus get_facts, get_page, or list_data_buyers. Usage is only inferable from the enumerated content list; no when-not or alternative-routing guidance is given.

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