Pointer Minerals — Texas Oil & Gas Data

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

No annotations are provided, so the description carries the burden. It clearly states the tool returns a URL and DOI, implying a read-only operation, but does not disclose whether the URL requires authentication, rate limits, or what happens if the dataset name is invalid. The description is honest but lacks detail.

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 that is direct and free of unnecessary words. It front-loads the action and output clearly.

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 tool with one parameter and no output schema, the description sufficiently explains the primary return value (URL and DOI). However, it does not mention related tools like list_datasets, which would help complete the usage context. Minor gap but overall 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 schema has a single 'name' parameter with no description, and the description adds that it is the name of a dataset. This provides some meaning beyond the schema, but it does not clarify the source of valid names or format expectations. With 0% schema coverage, more explicit parameter guidance would be beneficial.

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 verb ('Get') and the exact resource ('bulk-download URL and DOI for a named dataset'). It distinguishes itself from sibling tools like list_datasets (which lists datasets) and get_county_production (which gets production data) by specifying the exact output.

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 provide any guidance on when to use this tool versus alternatives. It does not mention that the dataset name should come from list_datasets, nor any exclusions or prerequisites. The agent must infer usage from context.

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 burden of behavioral disclosure. It mentions the data grain (monthly), units (bbl, Mcf), and optional date window, but does not disclose behavior around the limit parameter, default state, return format, or edge cases such as invalid dates. This is minimal behavioral information beyond what the tool name implies.

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 short sentences, front-loaded with the core purpose and then the optional date window. Every word is purposeful, with no fluff or redundancy. It is appropriately concise for the tool's simplicity.

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 has 5 parameters, no output schema, and no annotations, the description is too sparse. It does not mention the limit parameter, the state default, the required county field (beyond the tool name), or the expected return structure. An agent would not have enough context to invoke the tool correctly without additional assumptions.

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 0%, so the description must compensate. It does explain that from/to are optional and expect YYYY-MM format, but it does not explain the required county parameter or the limit and state parameters. The description adds value for only two of the five parameters, leaving the others undocumented.

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 tool's purpose: retrieving monthly oil (bbl) and gas (Mcf) production for a Texas county. It specifies the resource (county production), the units, and the temporal granularity, which distinguishes it from sibling tools like get_operator or list_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 context: it is for getting county-level production data, with an optional from/to date window. However, it does not explicitly mention when to use this tool versus alternatives, nor does it provide exclusion criteria. The usage guidance is primarily about parameter filtering, not tool selection.

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 disclose behavior. It does mention the exact/fuzzy matching semantics and the required-parameter condition, which is useful. However, it omits critical behavioral details: what the response looks like, behavior when no match is found, handling of the state parameter, and the nature of the fuzzy matching. The agent is left without knowledge of return structure or error cases.

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 consists of two concise sentences that are front-loaded with the core action and required condition. Every word serves a purpose, with no fluff or repetition. It is appropriately sized for the tool's simplicity.

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 tool is a lookup with 3 parameters, no annotations, and no output schema. The description covers the lookup mechanism but fails to state the return type, error behavior, or the role of the state parameter. This is a significant gap because the agent cannot predict what the tool will return or how failures are signaled, making the description incomplete for reliable 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?

Schema description coverage is 0%, so the description must compensate. It clarifies that org_no is the RRC org number and that name uses fuzzy matching, and it specifies the OR requirement. However, the state parameter is entirely unmentioned, and its default value and purpose are not explained. The description adds value for two of three parameters but ignores the third.

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: 'Look up a Texas operator by RRC org number (exact) or name (fuzzy)'. It specifies a concrete verb ('look up'), resource ('Texas operator'), and the lookup methods (exact/fuzzy). This distinguishes it from siblings like search_operators by highlighting the exact-match capability and the specific identifier.

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 includes a usage constraint ('One of org_no or name is required') but offers no guidance on when to choose this tool over alternatives like search_operators. There is no mention of suitable scenarios, exclusions, or comparative context, leaving the agent to infer the tool's role from its name and 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?

With no annotations provided, the description carries the full burden of behavioral transparency. It discloses the output contents (counties with production tier and score) but does not mention read-only status, rate limits, auth requirements, or any side effects. This is adequate for a simple list tool but lacks depth.

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 that is front-loaded with the action and resource, using concise language with no wasted words. It is appropriately sized for a simple tool.

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 tool has a simple schema and no output schema, but the description still omits details like the state parameter, return format specifics, and any limitations. It is minimally adequate for understanding the core function but incomplete for an agent to know all invocation context.

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 one parameter 'state' with default 'TX' and no description, and the description does not mention any parameter. With a schema description coverage of 0%, the description fails to clarify whether other states are supported or how the parameter affects results, providing zero added meaning.

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 the specific verb 'List' and identifies the resource as 'Texas counties Pointer covers', which clearly states what the tool does. It also mentions the output includes production tier and score, but it does not explicitly contrast it with sibling tools like list_datasets or search_operators.

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 provides no guidance on when to use this tool versus alternatives such as get_county_production or list_datasets. There is no mention of context, exclusions, or prerequisites, leaving the agent without clear selection criteria.

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 must disclose behavioral traits. It states 'list' (a read operation) and the fields returned, but does not clarify pagination, return format, or any limitations. This is sufficient for a simple list, but lacks richer context.

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 that directly states the action and result, with no redundant or filler content.

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 (no parameters, no output schema, no annotations), the description provides adequate context: what it lists and what fields are included. It lacks explicit mention of the output structure or relationships to sibling tools, but this is not critical for a basic listing 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 tool has zero parameters, so the baseline is 4. The description needs no parameter explanations, and the schema confirms there are none.

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 lists Pointer Minerals datasets and enumerates the exact attributes (license, DOI, grain, coverage, bulk-download URLs), making its purpose unambiguous and distinct from sibling tools like list_counties.

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 use case is implied: use this tool when you need to list datasets. However, there is no explicit guidance on when to prefer this over alternatives like search_operators or list_counties, nor are any exclusions or prerequisites mentioned.

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 of behavioral disclosure. It adds useful context like 'FTS' and 'ranked matches', but omits other behavioral traits such as how the state parameter affects results, whether pagination exists, or what the output format looks like.

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 highly concise: two short sentences with no filler. It front-loads the core purpose and adds the most important behavioral detail (result cap) 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 adequate for a straightforward search tool but leaves gaps: it does not mention the configurable 'state' parameter despite the description saying 'Texas', nor does it describe return fields or how results relate to get_operator. Since there is no output schema, a bit more detail would be valuable.

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 0%, so the description must compensate. It clarifies that 'query' is an operator name and that the limit is up to 50, but it completely omits the 'state' parameter, which is notable given Texas is hard-coded in the description while the schema allows a configurable state.

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 searches Texas operators by name using full-text search, which directly distinguishes it from sibling tools like get_operator (retrieves a specific operator) and list_counties (lists counties). The result limit and ranking are also mentioned, making the 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 usage context is implied: use this tool when you need to find operators by name. However, it does not explicitly explain when not to use it or how it compares to get_operator (e.g., for retrieving full operator details by ID).

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