oecd-mcp-server

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds context by explaining the dependency on a prior oecd_query_dataset spill and the duckdb provider requirement, which is useful behavioral context not present in annotations. It does not, however, describe what happens when no spill exists or when the provider condition is unmet, but the output schema covers return shape.

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 filler. The core purpose is front-loaded, the key usage ordering is stated immediately, and the environment constraint is given last. Every sentence carries distinct and necessary 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 has one fully documented parameter, an output schema, and annotations covering read-only and idempotent behavior, the description is complete for agent decision-making. It tells the agent what the tool does, when to call it, what it enables, and the precondition required.

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%: the canvas_id parameter is fully described in the schema, including its 10-character pattern and purpose. The description does not add extra detail about the parameter itself, so the baseline of 3 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 opens with a specific verb and resource: 'List tables and columns staged on a DataCanvas.' It clearly identifies this as a discovery tool for inspecting spill results, which is distinct from querying via oecd_dataframe_query or fetching dataset metadata. The name and title are supported rather than merely restated.

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 sequencing context: 'Call this before oecd_dataframe_query to discover exact table and column names for SQL.' It also states a concrete availability condition: 'Only available when CANVAS_PROVIDER_TYPE=duckdb is set.' It lacks an explicit when-not-to-use statement or mention of alternatives beyond the implied sibling ordering, but the context is clear and actionable.

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 and idempotentHint annotations, the description explicitly forbids DDL, DML, and file-reading functions, and discloses the runtime availability constraint based on CANVAS_PROVIDER_TYPE. This gives the agent concrete behavioral boundaries that are not fully captured by annotations alone.

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 with no filler. It front-loads the core purpose and read-only nature, then provides the prerequisite workflow, supported operations, and environment requirement. Every sentence contributes meaningful 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?

The description covers the prerequisite discovery step, the supported SQL operations, the rejection of non-SELECT statements, the dependency on oecd_query_dataset, and the duckdb environment requirement. Combined with the output schema, an agent has enough context to call the tool correctly without missing critical setup or constraints.

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 the relationship between the SQL parameter and table names returned by oecd_dataframe_describe, and by naming the supported query capabilities (aggregation, filtering, GROUP BY, JOIN, window functions) beyond the schema's generic SELECT statement description.

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 ('Run'), a resource ('OECD observation tables staged on a DataCanvas'), and the operation type ('read-only SQL SELECT'). It differentiates this tool from sibling tools by noting that oecd_dataframe_describe should be called first and that this tool is for querying staged tables, not for fetching or describing datasets.

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 clear usage context: call oecd_dataframe_describe first to discover table and column names, then use this tool for aggregation, filtering, GROUP BY, JOIN, and window functions. It also states the environment prerequisite (CANVAS_PROVIDER_TYPE=duckdb). It does not explicitly name alternatives or when-not-to-use scenarios, but the guidance is strong enough for an agent to select it correctly.

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, idempotentHint=true, and openWorldHint=true, so the safety profile is covered. The description adds value by disclosing that the tool returns key-construction information (position in the dot-delimited key) and that it is a prerequisite step, which is behavioral context beyond the annotations. It doesn't discuss rate limits or error cases, but for a read-only metadata fetch the annotations plus the description are sufficient.

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, each earning its place: the first states what the tool returns, the second details the return contents, and the third gives the usage context. The most important information (what it fetches and why it's needed) is front-loaded. No filler or repetition of schema details.

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 one parameter, a rich schema description, an output schema, and annotations covering safety and idempotency. The description explains the return value's purpose and the prerequisite relationship to oecd_query_dataset. Nothing an agent needs to decide whether to call this tool and how to use its output 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 flow_ref thoroughly, including the two accepted forms and an example. The description adds context by explaining that the output is needed to construct query keys, which helps the agent understand why flow_ref matters. Since the schema does the heavy lifting, a 4 is appropriate rather than a 5.

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 ('Fetch'), a specific resource ('a dataflow's dimensions'), and the concrete output (dimension names, codelist references, position in the dot-delimited key). It also explicitly distinguishes itself from oecd_query_dataset by saying it is required before calling that tool to understand key structure. This clearly differentiates it from siblings like oecd_get_dimension_values and oecd_query_dataset.

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 'Required before calling oecd_query_dataset to understand key structure,' which gives a clear when-to-use directive and names the alternative it supports. It also implies this is a prerequisite/metadata tool rather than a data-fetching tool, which helps an agent choose it over oecd_query_dataset or oecd_get_dimension_values. No exclusions are needed because the tool's role as a prerequisite is clearly stated.

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?

The description adds behavioral context beyond the readOnly/openWorld/idempotent annotations: responses are paginated at 'at most limit codes either way,' and codelists 'run to a thousand-plus entries,' explaining why querying and paging matter. 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?

Two sentences deliver purpose, usage context, a rationale for the query parameter, and pagination behavior. Everything earns its place and the key action 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?

Input schema fully documents parameters, an output schema exists, and the description supplies the workflow context and pagination behavior. It is complete for practical use, though explicit alternative tool guidance would make it stronger.

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 applies. The description adds workflow context but does not materially elaborate on individual parameter meanings beyond what the schema already documents, so it neither compensates nor needs to.

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 and resource: 'Fetch the valid codes and labels for one dimension of a dataflow.' It also frames the tool's role in the workflow, resolving human-readable names to SDMX codes before using oecd_query_dataset, which differentiates it from sibling 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?

Clear context is provided: use this tool to resolve names to codes before querying with oecd_query_dataset. It does not enumerate exclusions or alternative tools explicitly, but the intended position in the workflow is unambiguous.

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, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds value by naming the returned content (agencies, directorate, dataflow counts) and its intended role in a search workflow. It does not go further into behavior such as pagination, ordering, or how open-world results should be interpreted, but for a zero-parameter list tool this is sufficient.

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 with no filler. The first sentence states what the tool returns, and the second gives the concrete workflow purpose. Every word earns its place and the key action 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?

For a zero-parameter, read-only, idempotent listing tool with an output schema present, the description covers the essential information: what is returned and why an agent would invoke it. The annotations handle side-effect concerns, and the output schema handles return structure details.

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, so the input schema is complete by definition and there is nothing for the description to clarify. Baseline for no-parameter tools is 4, and the description appropriately avoids inventing parameter information that does not 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 a specific verb and resource ('List OECD SDMX agencies') and specifies the exact output fields ('directorate each belongs to, and the number of dataflows each publishes'). It differentiates itself from siblings by positioning itself as the precursor to filtering oecd_search_datasets, so an agent can distinguish it without opening the schema.

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 an explicit use case: discover agency IDs before filtering oecd_search_datasets by department. It names the sibling tool and the workflow context. It does not explicitly state when not to use it, but the positive guidance is specific enough to route an agent effectively.

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 cover readOnly/openWorld/idempotent, so the bar is lower. The description still adds meaningful behavior: decoded rows with labels, values scaled by the observation unit multiplier, and the large-result spill to DataCanvas with a preview slice when DataCanvas is absent. These details go beyond the annotations and help an agent understand side effects and result handling. 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?

Three sentences with no filler: purpose, return behavior and spill edge case, and a necessary prerequisite. Every sentence contributes new information and the most important facts come first. This is appropriately compact for the tool's complexity.

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 description covers the core call, return shape, spill behavior, and the critical dimension-order prerequisite. With an output schema present and full schema parameter documentation, the remaining gaps (e.g., exact period formats, error cases) are already covered structurally. It is complete enough for correct invocation, though it doesn't discuss pagination or error scenarios in free text.

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 description itself adds only cross-reference guidance (use oecd_get_dataset_info to learn dimension order), not extra semantics for the parameters. The schema's own descriptions already cover key syntax, flow_ref forms, canvas_id rules, and period formats. This does not meaningfully exceed the baseline.

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 first sentence clearly states a specific action—fetch observations from an OECD dataflow—and the filtering mechanism (dimension key, optional time range). It distinguishes itself from siblings like oecd_dataframe_query (which queries the spilled DataCanvas table) and oecd_get_dataset_info (which returns metadata). No ambiguity about what this tool does.

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 context: 'Call oecd_get_dataset_info first to learn the dimension order for constructing the key' and explains the spill behavior with a follow-up to oecd_dataframe_query. It does not explicitly enumerate when to avoid this tool in favor of other siblings, but the prerequisite and follow-up guidance are clear. This fits 'clear context, no 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 already declare readOnly, idempotent, and openWorld behavior, so the description only needs to add context. It adds the matching behavior (against names and descriptions) and the specific return payload, which is useful beyond the annotations. No contradiction with the annotation hints.

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 with no filler; the search action and matching scope are front-loaded, and the return-value note is relevant to downstream tool selection.

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 read-only search tool with a fully documented schema, rich annotations, and an output schema, the description covers the necessary selection and invocation context. It tells the agent what to expect and how the result connects to oecd_get_dataset_info.

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 input schema already documents query, limit, offset, and agency_id with examples and constraints. The tool description adds no parameter-level meaning, so the baseline 3 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 specifies a concrete action (search OECD dataflows by keyword or theme), the matching target (dataflow names and descriptions), and the returned identifiers (flow_ref, names, agency IDs). It also positions the tool as the discovery step preceding oecd_get_dataset_info, distinguishing it from sibling query/dataframe 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 makes the intended workflow clear: search by keyword or theme to obtain identifiers 'for use with oecd_get_dataset_info.' It does not explicitly list when not to use it or name alternatives, so it stops short of full exclusion guidance.

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