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 declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds that it is 'Free discovery' and enumerates the returned content types, which exceeds the bare annotation. However, it does not disclose any pagination, limit, or error behavior, though those are less critical for a metadata lookup. The description adds value without contradicting 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 sentences, no filler. The first sentence opens with a benefit ('Free discovery') and immediately states the output, followed by the usage trigger and the concrete outcomes. Every clause earns its place, and the critical guidance 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?

Given a single parameter, full schema coverage, and readOnly safety annotation, the description covers the essential context: what it returns, when to call it, and what to expect. It omits a formal response schema or error cases, but for a lightweight discovery tool these are not significant gaps. The mention of 'preferred canonical tool guidance' effectively routes the agent to correct subsequent actions.

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 provides a 100% descriptive parameter definition: 'Pack identifier from get_catalog. Newly catalog-admitted packs require no MCP schema change.' The description adds no further semantic detail about pack_id beyond what is already in the schema. Per the rubric, a baseline of 3 applies when schema coverage is high, and the description does not raise it further.

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 purpose: it returns detailed metadata, coverage, freshness, tool guidance, and first-query examples for one pack. It defines the resource as 'one pack' and the action as 'returns', which effectively distinguishes it from get_catalog (which lists packs) and query_dataset (which executes queries). The reuse of 'preferred canonical tool guidance' reinforces its role as a decision aid.

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 instructs 'Call this before querying a new pack', providing a clear when-to-use condition. It also explains the benefits (time shape, coverage limits, paste-ready query), which signals how it fits into the workflow. It does not explicitly state when to use alternatives like get_catalog or query_dataset, but the purpose differentiation is strong enough to imply the alternatives.

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, so the read-only nature is covered. The description adds the free vs paid pack distinction and the pricing reference (x402 Base USDC), which is valuable behavioral context beyond annotations, such as potential access restrictions.

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, front-loaded with purpose, and the pack list is compact. No fluff; every part contributes to understanding the tool's scope and the available data sources.

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 complex with optional nested parameters and no output schema, but the description doesn't explain response format or provide example queries beyond the API contract reference. It's adequate but not complete for a generic query tool, leaving the agent to rely on the referenced contract.

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 descriptions cover all 8 parameters (100% coverage), so the baseline is 3. The description adds concrete pack ids and refers to the API contract, enriching understanding of pack_id and the overall query structure beyond what the schema provides.

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 ('query'), a resource (dataset via source_id or pack_id), and references the exact API contract (POST /api/v1/query/dataset). It also lists all available packs, clearly distinguishing this data-access tool from sibling catalog/pack 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?

Usage context is implied by the tool name and the pack list, but the description never explicitly says when to prefer this over get_catalog or get_pack. It also doesn't state prerequisites like needing a catalog first, so an agent must infer the selection logic.

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