DaedalMap Distributed Manufacturing Locations

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

Annotations already declare readOnlyHint=true, so the safe read nature is covered. The description adds useful context by specifying the content (live agent-ready data packs on DaedalMap), but does not disclose rate limits, pagination, or response structure, leaving room for more transparency given the low complexity.

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 with no filler. The first sentence sets context ('Free discovery') and the second clearly states the function. Every word earns its place, and it is maximally concise.

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 list tool, the description is largely complete: it names the output (list of data packs) and the source (DaedalMap). It could be more thorough by explaining what 'agent-ready' means, but overall it provides enough context for an agent to select and invoke 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 has zero parameters, and schema description coverage is 100% (vacuously). Since there are no parameters to explain, the description need not add parameter semantics, and the baseline of 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 clearly states the tool returns a list of live agent-ready data packs on DaedalMap, using a specific verb ('returns') and a distinct resource. It inherently differentiates from siblings like get_pack (specific pack retrieval) and query_dataset (data querying).

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 phrase 'Free discovery' implies this tool is for initial exploration, and siblings suggest alternatives, but there is no explicit when-to-use or exclusions. The guidance is implied rather than stated, so it falls short of a clear directive.

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 readOnlyHint=true, but the description adds value by noting 'Free discovery,' describing the specific outputs (coverage, freshness, canonical tool guidance), and mentioning the 'paste-ready first query.' This goes beyond what annotations provide, though it does not address potential errors 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?

The description is two sentences long, front-loaded with 'Free discovery,' then efficiently lists what is returned and when to use it. Every phrase contributes value, with 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?

Given the simplicity of the tool (one parameter, readOnly annotation, no output schema), the description covers the return contents and usage context thoroughly. It does not describe error behavior, but the schema's example values mitigate that gap.

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 includes a list of example pack IDs, so the schema fully documents the parameter. The description adds no extra semantic detail beyond referring to 'one pack,' so a 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 a specific verb and resource: 'Returns detailed metadata, coverage, freshness, preferred canonical tool guidance, and first-query examples for one pack.' It distinguishes itself from siblings by positioning as a pre-query discovery step, contrasting with query_dataset and get_catalog.

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 advises 'Call this before querying a new pack,' providing clear context for when to use it. It does not explicitly mention when not to use it or name alternatives, but the guidance is unambiguous enough for correct 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?

Beyond the readOnlyHint annotation, the description discloses substantial behavioral context: it returns a comprehensive set of guidance items including refusals, access limits, provenance fields, and recommended next calls. It also frames itself as 'blind-caller guidance', clarifying its purpose for agents unfamiliar with the tool. 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?

The description is two sentences with no wasted words, but the first sentence is a dense enumeration of many return items which may reduce scannability. Still, every element earns its place and the structure is front-loaded with the core purpose.

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 output schema, the description compensates by explicitly listing the types of information returned, plus usage steps and the interaction contract. For a simple one-parameter tool with a readOnly annotation, this is complete guidance enabling correct invocation and understanding of outputs.

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 provides 100% coverage with a clear description for tool_name ('Exact tool name from tools/list'). The description adds useful context by instructing to use tools/list first and to call this before an unfamiliar tool, reinforcing how to discover the correct parameter 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 the tool provides guidance for one tool, with a specific verb 'Returns' and a defined resource. It distinguishes itself from sibling tools (get_catalog, get_pack, query_dataset) which are data retrieval tools, while this is meta-guidance for tool usage.

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 says to use tools/list to discover names and call this before an unfamiliar tool, providing clear context for when to use it. However, it does not mention when not to use it or alternatives beyond mentioning tools/list as a prerequisite, so it lacks explicit exclusions.

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 declare readOnlyHint=true, and the description does not contradict this. The description adds value by mentioning the API contract and the free/paid distinction with pricing (x402 Base USDC), which is useful behavioral context. However, it does not disclose pagination behavior, response format, or any limitations beyond the schema, so it meets but does not exceed the baseline for a read-only tool.

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 sentence states the core purpose and API contract. The second sentence enumerates free and paid packs, including pricing. Every clause earns its place, and the most important information is front-loaded.

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 8 parameters, nested objects, and no output schema, the description provides essential contextual information: the pack list (critical for pack_id values) and the API contract reference. It does not describe return structure or advanced usage patterns, but the schema handles parameter semantics. The pack enumeration makes the tool usable, so it is reasonably complete for 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 schema covers 100% of parameters with descriptions, so the baseline is 3. The description does not add parameter-level details beyond listing example pack_ids and noting the paid nature of some. It does not elaborate on filters, metrics, sort, or output controls, which are already described in the schema. Thus it neither enhances nor detracts 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 opens with a specific verb-resource pair: 'Generic structured query for direct source_id or pack_id access'. It clearly distinguishes itself from sibling tools like get_catalog (metadata) and get_pack (pack details) by focusing on querying data directly. The mention of the API contract adds precision.

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 conveys usage: it is for direct data access via source_id or pack_id. It lists free and paid packs, giving practical guidance on what is available. However, it does not explicitly contrast with siblings (e.g., 'use get_catalog to list packs') or state when not to use this tool, so it stops short of full alternative guidance.

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