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

With no annotations provided, the description carries the full burden. It discloses the fallback behavior for rejected TIME_PERIOD clauses, the truncated flag and that rows are 'NOT a representative sample' when truncated, and the ToolError format with a [kind] discriminator and retry hint. It also notes that TIME_PERIOD and SERIES_KEY are added automatically. This is thorough behavioral disclosure.

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 moderately long but every sentence earns its place. It is front-loaded with the purpose, then proceeds logically to prerequisites, fallback behavior, return info, and error handling. It is structured and does not ramble, though it could be slightly trimmed without losing 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 the tool's complexity (6 params, output schema exists, no annotations), the description covers the critical gotchas: prerequisite inspection, fallback behavior, truncation semantics, and error format. It does not need to explain return values because an output schema is present. Nothing essential for correct invocation is missing.

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 contextualizing the ref and filters parameters—telling the agent to obtain component/code IDs from inspect_dataflow and explaining the consequence of omitting filters. This goes beyond the schema's per-parameter descriptions, hence 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 opens with 'Retrieve observations as records. This returns the actual numbers.' which is a specific verb+resource that clearly distinguishes this from the sibling tools (list_services, search_dataflows, inspect_dataflow). It leaves no ambiguity about what the tool does and its output nature.

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?

It explicitly instructs the agent to 'Call inspect_dataflow first to confirm component and code IDs and to check the size of what you are about to pull.' This is a clear prerequisite and directly routes the agent to the right sibling. It also warns that omitting the filters 'pulls the whole dataflow and will almost certainly truncate,' giving concrete guidance on when to use filters.

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 and does a solid job: it discloses the return content, warns about truncation signals, and lists the ToolError condition. It could more explicitly state that the operation is read-only and has no side effects, but 'Inspect' plus the returns/raises structure conveys the essential behavioral profile.

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, front-loaded with the core purpose, and uses a clear structure with Returns and Raises sections. Each sentence earns its place; the 'three questions' framing is especially efficient because it instantly communicates the tool's decision-making 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?

For a 4-parameter inspection tool with an output schema and a richly documented input schema, the description is complete: it states what the tool does, when to call it relative to get_data, what it returns, and when it errors. The sibling relationships are sufficiently covered by the explicit get_data comparison.

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 the baseline is 3. The tool description does not add parameter-level meaning beyond the schema, but the schema itself already explains ref format, filter syntax, the IN() alternative, and find_code semantics. The description's reference to 'filters' aligns with the schema but adds no new parameter information.

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 and resource: 'Inspect a dataflow's components, available codes and size.' It further clarifies the tool's role by naming the three questions it answers, distinguishing it from get_data by positioning itself as the pre-retrieval inspection step.

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 'Call this before get_data' and gives a concrete stopping condition: 'If you only need to know whether data exist for some subset, pass filters and stop here rather than going on to get_data.' This gives clear when-to-use guidance and differentiates it from the main sibling alternative.

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 tool's behavior (lists services) and explicitly describes the return format ('known services, with capability notes'). It does not mention side effects or permissions, but listing is inherently non-destructive and the return description adds meaningful context, warranting a 4.

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—two sentences plus a 'Returns:' line—with the core purpose front-loaded. Every sentence adds value: it explains the tool's purpose, notes the service URL flexibility for other tools, and outlines the return. No waste 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?

Given the tool's simplicity (0 params, list operation) and the presence of an output schema, the description covers the essentials: what the tool does and what it returns. It could be more explicit about the possible content of 'capability notes' or how services are discovered, but it is adequate for the agent to use 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 tool has zero parameters and schema coverage is 100% (empty schema). The baseline for 0 params is 4. The description adds context about passing service arguments to other tools, which is helpful for the broader workflow even though it does not directly describe this tool's parameters (none exist).

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 'List the SDMX services this server can query', clearly stating the verb, resource, and scope. It distinguishes itself from siblings like search_dataflows, inspect_dataflow, and get_data, which all focus on data retrieval rather than service enumeration.

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 by noting that any SDMX-REST v2 base URL can be passed as a service argument to other tools, which directs the user to this listing tool to discover available services before querying data. However, it does not explicitly state 'use this when you need to see available services' or provide a when-not-to-use rule, leaving some inference to the agent.

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 that exactly one request is issued, terms are matched locally, and errors raise ToolError with a [kind] discriminator and retry hint. This goes beyond typical descriptions, though it stops short of covering side effects or permissions—less critical for a read-only search.

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 well-structured, front-loading the purpose, then behavioral notes, then Returns and Raises sections. Every sentence adds value: the request-count note, the synonym advice, the ref routing hint, and the error format are all useful and not redundant with the schema.

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 relative simplicity, full schema coverage, and the existence of an output schema, the description covers purpose, usage, behavior, return value routing, and error handling. No critical information an agent needs to call it correctly is missing.

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 the schema already documents all three parameters thoroughly. The description adds no additional parameter-level detail, but the baseline of 3 applies since the schema 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 opens with 'Find dataflows (datasets) matching a topic', which names a specific verb and resource. It is clearly distinct from siblings: search_dataflows searches for dataflows, list_services lists services, inspect_dataflow inspects a single dataflow, and get_data fetches 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?

The description explains when to use the tool (topic search) and gives efficiency guidance: put synonyms in one query because matching is local. It does not explicitly name alternatives or say when not to use it, but the purpose is clear and the returned refs point to the next tool, providing contextual routing.

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