LastLook Data

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

Annotations already declare readOnlyHint true, idempotentHint true, and destructiveHint false. The description adds value by specifying the data scope ('single most recent value') and cost difference, which are not covered by 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 wasted words. The key information (purpose, cost, usage guidance) is front-loaded and easy to parse.

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 simple with one parameter and has an output schema. The description covers purpose, usage, cost, and alternatives comprehensively. No gaps.

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% with enums and descriptions for series_id. The description mentions examples (CPI, unemployment rate, mortgage rate) that align with the enum, but adds little semantic meaning 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 tool 'returns only the single most recent value for any supported FRED series.' It explicitly distinguishes the purpose from the sibling tool get_series by contrasting 'current reading' vs. 'historical observations.'

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 provides explicit guidance on when to use this tool ('when you need just the latest reading') and when to use the alternative get_series ('when you need historical observations'). It also highlights a cost advantage ($0.01 vs $0.05), aiding decision-making.

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 and idempotentHint. The description adds that it retrieves release dates and mentions the data source (FRED). No side effects or behavioral quirks needed, so the description complements annotations well.

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 redundant information. The purpose is front-loaded and the structure is efficient.

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 simple param set with an output schema, the description fully covers what the tool does, when to use it, and gives concrete examples. No gaps.

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 the single parameter 'days' with enum and description. The description does not elaborate on the parameter beyond what the schema provides, meeting the baseline expectation for 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 explicitly states it returns upcoming FRED economic data release dates and lists examples (CPI, jobs report, GDP). It clearly distinguishes from sibling tools like get_current_value which return actual values.

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 instructs to use this tool to find when the next major economic data will be published. While it doesn't explicitly mention when not to use it, the sibling context makes it clear that value-retrieval tools are for actual numbers.

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 indicate read-only, idempotent, open-world. The description adds data source (ECB) and date format, providing useful context 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?

Three sentences, no waste. Purpose, source, and format are front-loaded and efficient.

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?

Output schema covers return details. Description covers purpose, source, and format. Could mention error handling for missing dates but openWorldHint covers that.

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%. Description reinforces date format and enumeration context. Adds marginal value over schema by specifying G10 identity.

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 it returns exchange rates for G10 pairs on a specific date, with data source and format. It distinguishes from siblings like get_fx_rate_current and get_fx_rate_series.

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 single-date lookup but does not explicitly guide when to use over alternatives like get_fx_rate_current or get_fx_rate_series.

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 provide safety and idempotency cues. The description adds the data source (European Central Bank) and explicitly lists all supported currency pairs, which is useful 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, clear and front-loaded with the core purpose; no extraneous words. Efficient and to the point.

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 simple with one parameter and an output schema. The description covers source, supported pairs, and purpose, making it fully complete for an 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 single parameter 'pair' is fully documented with an enum and description in the schema, achieving 100% coverage. The description merely restates 'G10 currency pair', adding no new information 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', the resource 'current exchange rate', and the scope 'G10 currency pair', with a specific list of supported pairs. It distinguishes from sibling tools like get_fx_rate_by_date or get_fx_rate_series.

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 for current rates only, and the list of pairs sets expectations. However, it does not explicitly state when not to use or suggest alternatives like get_fx_rate_by_date for historical rates.

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 provide read-only, idempotent, open-world hints. Description adds value by specifying the data source (ECB) and that rates are daily. No contradictions 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 a single sentence that efficiently states the core functionality. It is front-loaded with the verb and resource. Could potentially add a bit more context without becoming verbose.

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 presence of an output schema, the description need not detail return values. It sufficiently covers source, data type, and scope. Annotations and schema together provide a complete picture for a tool of this complexity.

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%, with enums and descriptions for both parameters. The description adds minimal extra meaning beyond 'G10 currency pair' which aligns with the enum list. 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 name, title, and description clearly indicate the tool retrieves historical daily exchange rates for G10 currency pairs from the ECB. It competently distinguishes itself from sibling tools like 'get_current_value' and 'get_fx_rate_by_date' 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 implies usage for obtaining historical series, but lacks explicit guidance on when-not-to-use or direct comparisons with siblings. However, the context of sibling names helps differentiate use cases.

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 indicate readOnly, idempotent, and not destructive. The description adds value by explaining the return includes an interpretation and the normal band, and identifies the data source (FRED). This goes 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 front-load the core function, with no wasted words. Every sentence provides essential information: what it returns, the interpretation, and the source.

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 there are no parameters and an output schema exists (not shown but present), the description sufficiently covers the tool's purpose, return content, and source. No additional context is needed.

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?

There are no parameters, so schema coverage is 100%. The baseline for no parameters is 4, and the description does not need to add parameter information. It implicitly suggests a zero-parameter invocation.

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 the spread between EFFR and IORB with interpretation of Fed policy stance, specifying the normal band and source. This is a specific verb+resource that distinguishes it from sibling tools that return single values or series.

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 for getting this specific spread, but does not provide explicit guidance on when to use versus alternatives like get_current_value or get_value_by_date. No exclusions or alternative tools are mentioned.

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, openWorldHint, and non-destructive. The description adds context about the indicator's meaning (recession threshold, measurement methodology) and data source. No contradictions. It enhances understanding 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?

The description is three sentences, front-loaded with the main purpose, and each sentence adds value: what it returns, the key threshold, and the source. No redundant or extra words.

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 parameterless tool with an output schema, the description adequately covers what the tool does and the significance of the value. It could optionally mention freshness (e.g., 'real-time' implies current but not frequency) but overall is complete.

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 no parameters (0), so schema coverage is 100%. The description does not need to explain parameters. The baseline of 4 applies as it provides sufficient context for a parameterless tool.

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 it returns the real-time Sahm Rule recession indicator, explaining what it measures, the threshold for recession (>=0.50), and the data source (FRED SAHMREALTIME). This distinguishes it from sibling tools like get_current_value or get_series by naming the specific indicator and its purpose.

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 this tool should be used when a recession indicator is needed, but it does not explicitly guide when to use this over alternatives such as get_series for FRED data or other get_ tools. It lacks when-not-to-use or comparison with siblings, which are many.

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 indicate read-only, idempotent, and non-destructive. The description adds value by clarifying that the most recent observation is the current value and by disclosing the cost tiers for each days option. No contradictions 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 appropriately sized and front-loaded with purpose. The bulleted examples are clear and useful, though listing 6 could be trimmed to 3 representative ones without losing clarity.

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 (2 params, output schema present, annotations covering safety), the description provides all necessary information: what it returns, examples, parameter details, cost hints, and how to interpret the last observation. No gaps.

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 100% coverage with descriptions. The description adds practical context for each parameter: examples of common series_id values and the meaning of days options (e.g., '30 for current/recent values'). This enhances understanding 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 it returns recent observations for FRED data series and provides multiple concrete examples (mortgage rates, yields, CPI, etc.). However, it does not explicitly distinguish itself from sibling tools like get_current_value or get_value_by_date, which could cause confusion.

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 offers common use cases and examples, implying when to use the tool (e.g., to get current or recent values). But it lacks explicit guidance on when not to use it or how it differs from alternatives like get_current_value or get_value_by_date.

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 clearly mark the tool as read-only, idempotent, and non-destructive. The description adds the important behavioral trait that it only returns data for business days. This is sufficient given the annotations and simple nature of the 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 only two sentences, front-loading the core purpose and immediately following with essential usage constraints. Every word adds 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 (single parameter, good annotations, output schema exists), the description covers the key behavioral constraint (business days) and format requirement. Missing detail about behavior on non-business days is minor but prevents a perfect score.

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 100% coverage with a clear pattern and description for the date parameter. The description adds the valuable context 'Business days only', which is not in the schema and clarifies when valid inputs are expected.

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 it returns the 30-year US Treasury yield for a specific date. The title and name reinforce this. The distinction from siblings like get_treasury_yield_current and get_yield_curve is implicit but clear due to the specific time scope.

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 usage constraints (business days only, YYYY-MM-DD format) but does not explicitly state when to use this tool versus alternatives. It implies use for a specific historical date, not for current values or curves, but lacks explicit 'when to use' and 'when not to use' guidance.

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 cover readOnly, idempotent, etc. Description adds context: free (no payment), data source (FRED). No contradictions.

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 waste. First sentence defines tool, second adds cost and alternative references. 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 parameterless tool with output schema, description covers source, series, type of data. Complete.

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?

No parameters in schema; baseline 4 as per guidelines. Description has no need to add parameter info.

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?

Clearly states it returns the most recent 30-year US Treasury constant maturity yield (DGS30). Specific verb+resource and names siblings.

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 says when to use: for current 30-year yield. Provides alternatives for other series: get_current_value or get_series.

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 indicate idempotent, read-only, and non-destructive behavior. The description adds the constraint 'Business days only,' which is valuable behavioral information beyond the 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 the key action in the first sentence and necessary constraints in the second. No extraneous 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 that an output schema exists (as per context signals), the description covers purpose, date format, and business day restriction. It does not explain behavior on non-business days, but for a read-only query tool, this is adequate.

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 both parameters have descriptions and enum. The description adds no new semantic information beyond what the schema provides, so baseline score 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 clearly states it returns the value of any supported FRED series for a specific date, with a specific verb+resource. It distinguishes from siblings like get_current_value (which returns current values) and get_treasury_yield_by_date (which is limited to treasury yields).

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 provides explicit guidance: 'Business days only' and 'Use YYYY-MM-DD format.' While it doesn't explicitly state when not to use it, the context of siblings makes the use case clear.

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 indicate read-only, idempotent, non-destructive behavior. The description adds value by specifying the exact metrics and their historical significance, which aids output interpretation.

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 concise, front-loaded sentences with no wasted words. Each sentence adds essential information: returns and context.

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?

Fully complete for a zero-parameter tool with annotations and output schema. Description clarifies the specific spreads and source, leaving no ambiguity.

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?

No parameters exist; per guidelines baseline is 4. Description adds meaning about what the tool returns beyond schema, though schema coverage is 100%.

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 the exact spreads returned (2s10s, 3m10y) and the inversion signal, clearly distinguishing it from siblings like get_treasury_yield_* that return individual yields.

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 a use case (recession analysis) by stating that inverted curves precede recessions, but does not explicitly exclude alternatives or provide when-not-to-use guidance.

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