Orion Celestia Intel

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

With no annotations, the description carries full burden. It discloses the tool is a read operation returning specific data types and optional narrative. However, it does not discuss any behavioral traits like rate limits, authentication needs, or response format details beyond the high-level components.

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-loading the key purpose and content. Every sentence adds value: first defines the brief, second gives usage advice, third explains the parameter. No redundant or filler text.

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 no output schema, the description adequately explains the returned data: headline, anomaly signals with severity, key network metrics, and provenance links. It also covers the optional narrative. It could include more about the structure of metrics or signals, but for a daily brief tool, this is reasonably 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?

Schema coverage is 100% and the schema already describes include_narrative as 'Also return Orion's written prose narrative (markdown, with a citation check)'. The description adds a slightly rephrased summary ('Set include_narrative for Orion's written prose summary'), but does not provide additional meaning beyond what the schema offers, so a 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 it provides Orion's latest daily analysis of the Celestia mainnet, listing specific components (headline, anomaly signals, network metrics, provenance links). It implicitly distinguishes from siblings by positioning itself as the starting point for 'what is happening on Celestia today?', which contrasts with more specialized tools like get_metric_history or get_signals.

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 advises 'Start here for "what is happening on Celestia today?"', giving clear usage context. It also mentions setting include_narrative for the prose summary. However, it lacks explicit exclusions or alternatives, though siblings provide enough context for differentiation.

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 does not disclose behavioral traits such as read-only nature, rate limits, or side effects. It only describes the output, leaving the agent uninformed about safety or constraints.

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 presents the core purpose, second lists the metrics. No extra words, 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?

For a simple list-type tool with no output schema, the description covers the essential aspects: what it returns, source, order, and available metrics. Minor missing details like default time range (though 'days' parameter has default 30 in schema) could be mentioned but not critical.

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 schema already documents both parameters. The description adds context about the source (daily reports) and order (oldest to newest) but does not significantly expand on parameter meaning beyond the enum list.

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 provides a time series of one snapshot metric across daily reports, listing the available metrics. This is specific and distinguishes it from sibling tools like get_daily_brief or get_trends.

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?

No guidance is given on when to use this tool vs alternatives, nor are there any exclusions or prerequisites mentioned. The description lacks usage context.

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?

No annotations are provided, so the description bears full responsibility. It discloses it is a read operation (detail retrieval) but doesn't mention side effects, authentication, or rate limits. Adequate but not comprehensive.

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, well-structured sentence that efficiently conveys the tool's purpose, input, and output. 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 the tool has one parameter and no output schema, the description adequately explains input and output. It lacks error handling or response format details, but this is acceptable for a simple 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?

Schema description coverage is 100% with a basic description. The tool description adds value by specifying the source (get_top_namespaces) for obtaining the hex id, enhancing parameter understanding.

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 retrieves details for a single Celestia namespace by namespace_id, listing specific output fields (size, blob count, last activity). It distinguishes from sibling by referencing get_top_namespaces as the source for the id.

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 implicitly guides usage by stating the input is a hex namespace_id from get_top_namespaces. It doesn't explicitly state when not to use or alternatives, but the purpose is straightforward.

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 full burden and accurately states it provides current metrics with provenance links. It doesn't disclose potential costs or update frequency, but for a simple read operation, the information 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?

The description is a single, front-loaded sentence that efficiently lists key metrics without wasted words. It earns its place by providing specific, useful 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 no output schema and zero parameters, the description fully describes what the tool returns (a list of current metrics) and mentions provenance links, leaving no obvious 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?

There are no parameters, and schema coverage is 100% trivially. Per guidelines, a baseline of 4 is appropriate since the description need not add parameter meaning; it is already complete.

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 identifies the tool as providing current Celestia mainnet metrics from Orion's latest snapshot, listing specific metrics (height, DA volume, fees, etc.), which distinguishes it well from siblings like get_daily_brief or get_metric_history.

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?

While no explicit when-to-use guidance is given, the description implies that this tool is for current snapshot data, differentiating it from siblings that offer daily briefs or historical metrics. The context is clear but lacks explicit exclusions or 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?

With no annotations provided, the description carries full burden. It discloses that the tool returns a comprehensive report with specific components, but does not mention rate limits, authentication, side effects, or other behavioral traits. Adequate but limited.

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 concise sentence (16 words) that front-loads the purpose and key contents, with no redundant 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?

Although no output schema exists, the description lists report components (metrics, signals, summary, provenance), providing a good high-level understanding. Could elaborate further on output structure, but adequate given tool simplicity.

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 for the single parameter 'date', including format and cross-reference. The description adds no additional meaning beyond what the schema already provides, meeting 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 description clearly specifies the verb 'get' and the resource 'Full Orion report' for a specific date, listing contents (snapshot metrics, signals, summary, provenance). This distinguishes it from sibling tools like get_daily_brief or get_signals.

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 retrieving a full daily report for a specific date, but does not explicitly state when to use this tool versus alternatives like get_signals or get_trends. No 'when not to' guidance 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?

No annotations provided, so description carries the burden. It covers retrieval behavior, default date, and filtering, but does not explicitly state read-only nature, authorization needs, or side effects. Adequate but not exhaustive.

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 defines purpose and context, second lists signal components and filter. No redundant information, front-loaded with actionable 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?

Given no output schema, description provides enough information about signal structure. Parameter documentation is complete. Somewhat lacks details on output format (e.g., list vs array), but sufficient for an agent to infer return type.

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%, baseline 3. Description adds context beyond schema by enumerating signal components (type, severity, baseline, data source) and clarifying default behavior for 'date'. This helps the agent understand return content, compensating for lack of output 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?

Description clearly states it retrieves anomaly/health signals from Orion for a day, with specific content details (type, severity, baseline, data source). However, it does not explicitly differentiate from siblings like get_trends or get_daily_brief, leaving some ambiguity.

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?

No guidance on when to use this tool versus alternatives (e.g., get_metric_history for metrics, get_report for reports). The description implies usage for daily anomaly inspection but lacks when-not or exclusion criteria.

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 must convey behavioral traits. It mentions 'live from Celenium' indicating real-time data, and states the sorting criterion (total blob size). However, it does not disclose rate limits, pagination behavior, or confirm idempotency, leaving some gaps.

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 sentences with no redundant information. Every sentence adds value: first defines what the tool retrieves, second clarifies the purpose. Ideal for quick scanning.

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 tool with one optional parameter and no output schema, the description covers the main functional aspects: what data is returned, sorting, and freshness. Minor gap: does not hint at the structure of the response (e.g., namespace name, blob size).

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 single 'limit' parameter has a clear description). The tool description does not add additional parameter details 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 largest namespaces on Celestia by total blob size, using the verb 'shows' and specifying the resource (namespaces) and metric (DA volume). It distinguishes from siblings like 'get_namespace' (single namespace) and 'get_top_validators' (validators).

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 viewing top namespaces by data volume, but lacks explicit guidance on when to use this tool over alternatives like 'get_namespace' or 'get_trends'. No when-not-to-use or exclusion criteria 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?

No annotations exist, so the description carries the full burden. It states data is 'live from Celenium' indicating real-time freshness, but omits behavioral details like rate limits, pagination, or authentication requirements. Adequate but not thorough.

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, front-loaded sentence that conveys all essential information without any wasted words. Every word earns its place.

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 one optional parameter and no output schema, the description adequately covers return fields (moniker, stake TIA, commission, jailed status). The tool is simple, and the description is sufficiently complete for an agent to understand its behavior.

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 the 'limit' parameter fully described in the input schema. The description adds no additional semantic value beyond the schema, resulting in a baseline score of 3.

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 retrieves the largest Celestia validators by stake, specifying the exact data fields (moniker, stake TIA, commission, jailed status). It distinguishes from sibling tools like get_top_namespaces which target a different resource.

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 fetching top validators but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or when-not-to-use conditions are provided, though the simplicity of the tool mitigates the need.

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?

No annotations provided, so description carries the transparency burden. It implies a read-only operation ('computed from daily snapshots') but does not explicitly state side effects, auth needs, or performance. The description is adequate but not detailed.

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?

Single, clear sentence that efficiently conveys purpose and usage. No wasted 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?

Given no parameters, no output schema, and no annotations, the description is complete enough for the tool's simplicity. It covers purpose and usage adequately.

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, so with 100% schema coverage the description does not need to add parameter info. Baseline score of 4 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?

Description clearly states the tool computes day-over-day and week-over-week changes for key Celestia metrics from daily snapshots. It answers a specific question ('what changed since yesterday/last week?') and differentiates from siblings like get_daily_brief and get_metric_history.

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?

Explicit context is given: use when needing to know changes over daily/weekly periods. While it doesn't formally exclude alternatives, the description implies the specific use case.

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 ordering (newest first) and content (date, signal counts) but does not mention pagination behavior, default limit, or error handling. The limit parameter's behavior is partially covered by the schema.

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, efficient sentence that conveys the purpose, content, and ordering without any unnecessary words. It is front-loaded with 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?

For a simple list tool with one optional parameter, the description is fairly complete. It specifies the type of reports, the fields returned, and the sort order. It lacks explicit mention of default behavior when limit is omitted, but that is minor.

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 baseline is 3. The description adds meaningful context about the returned entries (date, signal counts) and ordering, which goes beyond the schema's parameter description of limit.

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 an index of daily Celestia reports with date and signal counts, sorted newest first. It distinguishes from sibling tools like get_report (single report) and search_reports (search).

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 browsing report indices but does not explicitly state when to use this tool versus alternatives like search_reports or get_report. No exclusions or conditions are 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?

Discloses behavior well: plain keyword scan, no LLM, results verbatim, grouped by date. With no annotations, description carries full burden and does so adequately.

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, no fluff. Every sentence adds 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?

Covers input and output sufficiently for a search tool with few parameters. No output schema, but description explains return format (grouped by date). Good enough.

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%, baseline 3. Description adds value by specifying searched fields (titles, details, types, metrics) and output grouping, enhancing understanding beyond 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?

Clearly states keyword search across recent daily reports, distinguishing from siblings like list_reports and get_report. Specific verb (search) and resource (reports) with scope (signals, grouped by date).

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?

Description mentions plain keyword scan and no LLM, implying when to use exact match. However, no explicit exclusion or alternative guidance among siblings.

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