DaedalMap Population Estimates

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

Annotations already declare readOnlyHint=true, so the description's claim of returning data is consistent. It adds context by specifying the returned items are 'live agent-ready' and from 'DaedalMap', but does not describe pagination, limits, or output structure beyond a list.

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 purpose ('Free discovery') and immediately specifying the return value. Every word contributes.

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 is simple (no parameters, no output schema, one sibling tool type), the description adequately covers the purpose. However, it could be improved by hinting at the relationship with get_pack (e.g., 'Use this to browse available packs, then get_pack to retrieve a specific pack').

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 0 parameters, so by baseline the description does not need to add parameter semantics. The description mentions 'Free discovery' which aligns with no inputs, but no additional value is needed beyond 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 verb 'Returns' and the resource 'list of live agent-ready data packs available on DaedalMap', distinguishing it from siblings like 'get_pack' (which likely retrieves a single pack) and 'query_dataset' (which queries dataset content).

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 a browse/search use case, but there is no explicit guidance on when to use this tool versus alternatives (get_pack, query_dataset). No 'when not to use' is 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?

Annotations already mark readOnlyHint=true, so safety is covered. The description adds behavioral insight such as 'free discovery', coverage limits, and including a paste-ready first query, which enhances transparency beyond 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: first states what is returned, second provides usage guidance. No wasted words, front-loaded with purpose and key 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?

Despite no output schema, the description covers all necessary aspects: return content, usage context, and hints about output. With full schema coverage and clear annotations, the description is complete for agent 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 coverage is 100% and already lists valid pack_id values. The description does not add extra parameter meaning beyond 'for one pack', so 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 that the tool returns detailed metadata, coverage, freshness, canonical tool guidance, and first-query examples for one pack. It distinguishes from sibling tools by emphasizing its preparatory role before querying data.

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 advises to call this before querying a new pack to learn about time shape, coverage limits, and the first query. This provides clear when-to-use context and implies alternatives like query_dataset.

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 safety profile is known. The description adds valuable behavioral context about the tool's output structure (what it returns) and its prerequisite (needs a tool name from tools/list). It doesn't contradict 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 compact and front-loaded, with the first sentence summarizing the tool's function and the second providing usage guidance. Every sentence carries information; no filler or 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?

Despite the absence of an output schema, the description enumerates the key return categories comprehensively, enabling the agent to know what to expect. It also covers prerequisites and usage context, making it complete for a simple one-parameter 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 input schema has full coverage: tool_name is described as 'Exact tool name from tools/list.' The description reinforces this by mentioning tools/list, but provides no additional semantic detail beyond the schema. With 100% schema coverage, 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 provides guidance for another tool on the MCP facade, enumerating the specific categories of information it returns (usage, refusals, examples, limits, outputs, provenance, next calls, interaction contract). This distinguishes it from sibling tools like get_catalog, get_pack, and query_dataset, which serve different purposes.

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 gives explicit usage guidance: use tools/list to find tool names, then call this before an unfamiliar tool. It provides clear context for when to use the tool, but does not explicitly state when not to use it or name alternatives (though the guidance implies it's for unfamiliar tools only).

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 provide readOnlyHint=true, covering safety. The description adds pack availability and pricing information (free vs paid, x402 Base USDC), which is useful context beyond the annotations. However, it does not disclose behavior like result formatting, pagination, 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, with the core purpose in the first and pack details in the second. No filler or redundant content. Well-structured and 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?

The tool has 8 parameters, nested objects, and no output schema. The description references the API contract which helps but does not explicitly explain return values, pagination, or error behavior. Pack lists are helpful, but for a complex query tool, more info on expected outputs would improve completeness.

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?

All 8 parameters have descriptions in the schema, so the schema already covers parameter semantics fully. The description adds context about supported packs but does not add new parameter meaning beyond what's in the schema. Baseline 3 applies due to high schema 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 clearly states the tool performs structured queries for source_id or pack_id access, with an explicit API contract reference. It differentiates from siblings (get_catalog, get_pack) by focusing on data queries rather than metadata retrieval.

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 indicates usage for direct access to specific source_id or pack_id, listing available free and paid packs. It provides clear context for when to use the tool but does not explicitly exclude alternatives like get_catalog or get_pack. No when-not guidance is given, but the purpose is evident.

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