Nass

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

Describes that Pipeworx picks the right tool and fills arguments, disclosing its orchestration behavior. With no annotations, this provides essential behavioral context about dynamic routing. Could add more about potential latency or error handling.

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 sentences: purpose, mechanism, and three clear examples. No wasted words, front-loaded with the core value proposition.

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 (1 param, no output schema), the description is complete enough. Examples illustrate usage well. Could mention that it may use multiple data sources or that results are from the best available source at the time.

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% for the single parameter 'question', so baseline is 3. Description adds minimal extra meaning beyond the schema, just says 'in natural language' which is already implied by the description and examples.

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?

Clearly states the tool answers plain English questions using best available data source, with specific verb ('Ask a question') and resource ('best available data source'). Examples distinguish from sibling tools that are domain-specific (nass_*, discover_tools, etc.).

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 says to describe needs without browsing tools or learning schemas, implying use when you want a natural language query. Does not explicitly say when not to use or mention alternatives, but context signals and sibling names (like nass_* tools) imply alternatives for specific data domains.

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 explains that the tool returns 'the most relevant tools with names and descriptions' and implies it is a search/retrieval tool. It does not explicitly state it is read-only or non-destructive, but the nature of searching a catalog is inherently non-destructive. A minor gap is not mentioning if it has any side effects or rate limits, but for a search tool this is acceptable.

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 three sentences long, each adding value: first sentence states the action, second sentence describes the return, third sentence gives usage guidance. No fluff or repetition.

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, no nested objects), the description is complete. It explains the purpose, return value, and when to use it. However, it does not mention what happens if no tools match or the behavior of the limit parameter beyond schema defaults. Still, for a search/discovery tool, this is sufficient.

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 both parameters ('query' and 'limit') are already described in the input schema. The description does not add additional meaning beyond what the schema provides, such as format requirements or default behavior. The baseline of 3 is appropriate since the schema already does the heavy lifting.

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 ('Search') and resource ('Pipeworx tool catalog'), and clearly distinguishes the tool's purpose: it's for discovering relevant tools when many are available, as indicated by 'Call this FIRST when you have 500+ tools available'. This differentiates it from sibling tools which are domain-specific query 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 explicitly states when to use this tool: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' It implies this is a preliminary step before using specific tools like nass_* or ask_pipeworx, though it does not list explicit alternatives. The 'FIRST' emphasis is clear guidance.

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 deletion but does not disclose whether deletion is permanent, reversible, or requires specific permissions. However, the tool is simple (one required param) and the operation is obvious.

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 is front-loaded and contains no unnecessary words.

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 simplicity of the tool (one required parameter, no output schema), the description is sufficiently complete. It clearly states what the tool does and what parameter is needed.

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% and the parameter 'key' is described in the schema. The description adds no additional meaning beyond the schema, but baseline is 3.

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 'Delete' and the resource 'a stored memory by key'. This distinguishes it from siblings like 'remember' (store) and 'recall' (retrieve).

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 use when you need to delete a memory. It does not explicitly mention when not to use it or alternative tools, but the purpose is clear and distinct from 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?

No annotations are provided, so the description carries the full burden. It states pre-filtering to source=SURVEY and sector=CROPS, which is useful behavioral context. However, it does not disclose potential rate limits, data freshness, or whether it requires specific authentication beyond the API key. The description is adequate but not comprehensive.

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 extremely concise, with only two sentences that are front-loaded with the main purpose. Every sentence provides useful information without waste.

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 no output schema and moderate complexity (5 params, 2 required), the description covers the core purpose and pre-filtering. It lacks details on output format or potential edge cases, but for a data retrieval tool, it is reasonably 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 baseline is 3. The description adds value by clarifying the pre-filtering context and listing example crop names, which helps with parameter selection. It does not repeat the schema but provides additional semantic context.

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 clear verb ('Get') and specific resource ('US crop production data'). It distinguishes from siblings by noting it's a 'shortcut for querying NASS survey data on crop yields, production totals, and planted/harvested acreage' and explicitly states pre-filtering to source=SURVEY, sector=CROPS, which differentiates it from nass_livestock and nass_prices.

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 implicitly indicates when to use this tool (for crop production data from NASS survey) and mentions pre-filtering, but does not explicitly state when not to use it or provide alternatives like nass_query for more general queries. However, given the sibling tools, it is clear this is for crop-specific data.

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 are empty, so the description must disclose behavior. It mentions pre-filtering (source=SURVEY, freq=WEEKLY) and the API key requirement, which is useful. However, it does not describe what happens on invalid input, rate limits, or response format (e.g., data structure, pagination). The tool likely returns data, but no behavioral traits beyond the pre-filters are disclosed.

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: the first clearly states the tool's purpose and data scope, the second adds pre-filtering context. No redundant or unnecessary text. Front-loaded with actionable information.

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 4 parameters, no output schema, and no annotations, the description is reasonably complete. It explains the pre-filtering, required parameters, and the type of data returned. It could mention that output is a table-like structure or that errors occur without a valid API key, but overall it provides sufficient context for a simple data retrieval 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?

Schema coverage is 100%, so parameters are fully documented. The description adds context that the data is pre-filtered (source=SURVEY, freq=WEEKLY), but does not elaborate on parameter values (e.g., valid state names) beyond what the schema says. This is baseline value given high 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 explicitly states the tool retrieves 'weekly crop progress and condition reports' and lists specific data points (planting progress, emergence, blooming, harvest completion, crop condition ratings). It clearly identifies the resource (crop progress reports) and the verb (get). The pre-filtering info distinguishes it from other NASS tools like nass_crop_production or nass_prices.

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 retrieving weekly progress data but does not explicitly state when to use this vs. alternatives like nass_crop_production (production) or nass_query (custom queries). No exclusion criteria or alternative recommendations are provided.

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 partially discloses behavior: it mentions pre-filtering to 'ANIMALS & PRODUCTS' sector. However, it does not explain whether the tool is read-only or any side effects (likely none). The 'Get' verb suggests read, but without annotations, more detail would help.

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 front-loads the purpose and includes a key detail about pre-filtering. No redundant information.

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 no output schema and no annotations, the description could provide more context about return structure or additional behavior. However, it adequately describes the tool's purpose and parameters for a data retrieval 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?

Schema coverage is 100%, so the schema already describes all parameters. The description adds no additional semantics beyond 'pre-filtered to sector=ANIMALS & PRODUCTS', which relates to the default sector but is not a parameter. Baseline 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 gets US livestock data and lists specific data types (inventory counts, slaughter numbers, production) and pre-filtering (sector=ANIMALS & PRODUCTS). This distinguishes it from other NASS tools like nass_crop_production and nass_prices.

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 use for livestock data but does not explicitly state when to use this tool versus siblings like nass_crop_production or nass_query. There is no guidance on when not to use it or alternative 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 carries the full burden. It discloses the pre-filters (source and stat_category), which is helpful. However, it does not mention response format, pagination, rate limits, or whether the tool is read-only (likely but unstated). No contradiction with annotations since annotations are empty.

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 concise, consisting of two sentences with no wasted words. It front-loads the core purpose and adds specific pre-filter details efficiently.

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 4 parameters (100% schema coverage), no output schema, and no annotations, the description is adequate but not complete. It explains the pre-filters and optionality of state/year, but lacks details on return values, error conditions, or data ranges. Adequate for a straightforward data retrieval 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?

Schema description coverage is 100%, so all parameters have descriptions. The description adds pre-filter context but does not provide additional semantics beyond what the schema already says. The commodity parameter lists valid values in the description, but these are also in the schema. Baseline 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 retrieves prices received by US farmers for crops and livestock, and distinguishes it from siblings by specifying pre-filters (source=SURVEY, stat_category=PRICE RECEIVED). It does not explicitly differentiate from sibling tools like nass_crop_production, but the pre-filters provide some context.

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 mentions pre-filters and that state is optional (defaults to national), which helps with usage. However, it does not provide explicit guidance on when to use this tool versus alternatives like nass_query or nass_crop_production, nor does it mention any limitations or prerequisites beyond the API key.

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 full burden. It mentions the source (USDA NASS Quick Stats) and that results include various agricultural data. However, it does not disclose important behavioral traits like API key requirement (already in schema), rate limits, pagination, or data freshness. With 0 annotations, more behavioral detail would be expected for a higher 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 a single, well-structured paragraph of two sentences. The first sentence immediately states the tool's purpose and source, and the second expands on capabilities and outputs. No superfluous information; 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 10 parameters, 100% schema coverage, no output schema, and no annotations, the description provides a high-level overview that complements the schema. It explains what the tool returns (production, yield, etc.), which is not in the schema. However, it does not mention response structure, error handling, or usage limits, which would make it complete for a tool of this complexity.

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 baseline is 3. The description adds value by summarizing the overall purpose and return types, but does not provide additional semantics for individual parameters beyond what the schema already says (e.g., 'commodity' is exemplified, but not further explained). It neither adds nor detracts meaningfully from 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?

The description clearly states the tool queries USDA NASS Quick Stats, a comprehensive source of US agricultural statistics. It explicitly lists what the tool supports (filtering by commodity, statistic category, geography, year) and what it returns (production, yield, acreage, prices, livestock, etc.), distinguishing it from sibling tools like nass_crop_production or nass_prices which are narrower.

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 needing comprehensive agricultural statistics from USDA NASS. It contrasts with sibling tools by being 'the most comprehensive source', suggesting alternatives for specific domains. However, it does not explicitly state when not to use it (e.g., for very specific crop progress data, nass_crop_progress might be better), which would elevate the score.

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?

Since no annotations are provided, the description bears full responsibility. It explains the core behavior: retrieving by key vs. listing all when key is omitted. However, it does not mention what happens if the key does not exist, or any error conditions.

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-loads the main action, and avoids unnecessary details. 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?

Given the tool's low complexity (one optional parameter, no output schema), the description is nearly complete. It could mention what format the returned memories take, but the omission is minor.

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 value by explaining that omitting the key lists all memories, which is not explicit in the schema. This extra context justifies a 4.

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 specific verbs ('retrieve', 'list') and clearly identifies the resource ('stored memory'). It distinguishes the two modes of operation (by key vs. all), which helps the agent understand the tool's scope.

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 explicitly states when to use this tool ('to retrieve context you saved earlier'), but does not contrast with alternatives like 'forget' or 'remember'. It omits explicit 'when not to use' guidance, though the context 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?

No annotations provided, so description carries full burden. It discloses persistence differences: 'Authenticated users get persistent memory; anonymous sessions last 24 hours.' However, it doesn't mention side effects (e.g., overwriting existing keys) or idempotency, which would add value. With no annotations, this is adequate but not exhaustive.

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, no fluff. First sentence states core purpose, second gives use cases and behavioral notes. Each sentence adds unique value.

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 string params, no output schema), the description fully covers purpose, usage, and key behavioral aspects. No gaps remain.

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 baseline is 3. The description adds usage examples ('subject_property', 'target_ticker') that are not in schema, providing extra context. However, the schema already describes key and value adequately, so the description adds moderate value.

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 'Store a key-value pair in your session memory' with specific verb 'store' and resource 'session memory'. It distinguishes from sibling tools like 'recall' (retrieval) and 'forget' (deletion) by focusing on writing to memory.

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 explains when to use this tool: 'save intermediate findings, user preferences, or context across tool calls'. It implicitly contrasts with 'recall' (retrieval) and 'forget' (deletion) as siblings. No explicit exclusions, but the examples provide clear guidance.

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