Vector Panda Documentation

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context that the tool provides current prices and latency meanings, which is useful beyond annotations. It does not contradict any annotation.

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 front-loads the key topics (hot, warm, paused, prices, latency, selection). It contains no fluff and is appropriately sized for an informational tool.

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 no output schema and no parameters. The description explains what information is returned: per-GB prices, latency implications, and when to pick each tier. This is sufficient for an agent to understand what the tool does and what it will get back.

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 in the schema, so the baseline score of 4 applies. The description does not need to explain parameter semantics as there are none.

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 compares hot, warm, and paused storage tiers, covering per-GB prices, query latency implications, and selection criteria. This is specific and distinguishes it from generic pricing tools like get_pricing.

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 does not explicitly mention when to use this tool versus alternatives such as get_pricing. It implies the tool is for storage-tier comparison, but an agent might not know whether to call this or get_pricing for general pricing information. No exclusions or alternative tool references 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?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description reinforces this by stating it 'lists' or 'passes one' for details, with no side effects mentioned. It also adds context that data comes from the OpenAPI spec, enhancing transparency 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?

The description is concise, using two sentences to convey the full functionality. It is well-structured, front-loading the primary action (list or detail) and then adding the source. No unnecessary words 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?

For a simple tool, the description provides all necessary context: the action (list/detail), the parameter format, the type of information returned, and the data source. It is self-contained and sufficient for an agent to decide when and how to use it.

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 'operation' has a clear description explaining the expected format (e.g., 'POST /api/v1/query') and the behavior when omitted (list all). This fully covers the parameter's meaning and usage, with 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: listing all public REST API operations or providing detailed information (parameters, request body, responses, auth) for a specific operation. It also mentions the source (OpenAPI spec), leaving no ambiguity about what the 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 an example of how to specify an operation but does not explicitly differentiate when to use this tool versus the sibling tools (e.g., compare_storage_tiers, get_pricing). It implies usage for API operation lookups, but a direct comparison or condition for selection is missing.

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 non-destructive behavior. The description adds context about content (dataset, recall, cost) but no additional behavioral traits, which is acceptable given the annotation coverage.

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 and efficiently conveys the core purpose. The phrasing is slightly awkward ('Measured benchmark results' as a noun phrase) but remains concise and scannable.

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 mentions key included elements (dataset, recall, cost) but does not specify output format or any further context. Given the lack of an output schema, a bit more detail on what 'methodology' entails could strengthen completeness, though it is adequate for a simple getter.

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 the schema coverage is 100% (empty). The description correctly implies no parameters are needed, aligning with the baseline for parameterless tools.

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 indicates the tool provides benchmark results and reproduction methodology, distinguishing it from siblings like pricing or storage comparison. However, it lacks an explicit verb (e.g., 'Gets'), though the title compensates.

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 versus alternatives like compare_storage_tiers or get_pricing. The mention of 'through the public API' is not a usage condition.

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 annotations already declare readOnlyHint true, idempotent true, and destructiveHint false, which sufficiently convey safety. The description adds value by specifying the exact nature of the retrieved data, reinforcing that this is a read-only lookup.

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 concise, using only two sentences to convey the purpose and key details. It is front-loaded with the main purpose and contains no unnecessary words or repetition.

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 no parameters and no output schema, the description provides enough context to understand what data will be returned. It mentions storage-tier pricing, monthly credits, billing cycle, and exclusions, covering all necessary information for a user to decide to invoke it.

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 is empty with no parameters, so no parameter explanations are needed. The description confirms the tool requires no input, making the parameter semantics fully clear.

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 current pricing, specifying storage-tier pricing, monthly usage credits, and billing cycle. It also clarifies what is not included (per-query or egress fees), leaving no ambiguity about the tool's 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 implicitly indicates when to use the tool (to obtain current pricing details) and provides useful context about the scope of the returned data. It does not explicitly contrast with sibling tools, but the purpose is obvious enough that a user would not confuse it with the other listed tools.

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 annotations declare readOnly, non-destructive, and idempotent behavior, which is consistent with a documentation/guide tool. The description does not contradict these annotations, and the nature of a quickstart guide aligns perfectly.

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 conveys the tool's purpose and content. No fluff or redundancy. It is front-loaded with the key term 'quickstart' and specifies the exact steps covered.

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 quickstart guide with no parameters and no output schema, the description fully captures what the user will get: installation, file upload, and similarity query steps. No additional context is needed, and the sibling tool set does not overlap with this specific guide.

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 no parameters, so there is no risk of misunderstanding. The schema coverage is 100% (empty), and the description does not imply any hidden inputs. This is appropriate for a static guide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a quickstart guide for installing the SDK, uploading a file, and running similarity queries. The verb 'quickstart' and specific steps make the purpose unambiguous, and it is distinct from sibling tools like pricing or API operations.

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 the tool is for getting started with Vector Panda, but it does not explicitly state when to use it versus alternatives like read_doc or search_docs. However, the name and content strongly suggest it is the entry point for new users.

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 annotations already declare read-only, idempotent, and non-destructive behavior. The description adds a small detail about the data source (canonical features page) and is fully consistent with the annotations, so the bar is met.

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 with no unnecessary words. It conveys the essential information efficiently.

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 list tool with no output schema, the description is complete. It states what is listed (file formats), the context (vector ingestion), and the source. No additional context is needed for an agent to invoke it 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?

There are no parameters, so there is nothing to document. The schema is complete with zero parameters, and the description does not need to explain any inputs.

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's purpose: listing file formats accepted for vector ingestion, with a specific source reference ('canonical features page'). The verb 'list' and scope 'accepted for vector ingestion' are precise, and it is easy to distinguish 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?

Usage is implied rather than explicit: the tool is obviously for retrieving supported formats, but the description does not name alternative tools or specify conditions for when to use this tool versus others. This is acceptable for a simple list tool but lacks direct 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?

The readOnlyHint and idempotentHint annotations already cover side-effect behavior, and the description does not contradict them. The description adds useful context by indicating that the operation retrieves full Markdown content rather than a summary, which supports safe expectations about the response.

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, focused sentence with no redundant content. It front-loads the core action and resource, and immediately follows with the required parameter and an illustrative example.

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 read operation, the description adequately conveys that the tool returns the full Markdown of the specified document. While error behavior for a nonexistent path is not mentioned, the core usage and expected output are clear enough for an agent to invoke the tool 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 only parameter, 'path', is fully described with its purpose and a realistic example. Schema coverage is 100% and the description aligns exactly with the parameter's role.

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 'Fetch', the resource type 'document', and the scope 'one document by its site path'. It also contrasts with search_docs by specifying full Markdown content, making the tool's distinct purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'as returned by search_docs' provides useful guidance that this tool is meant to be used with paths originating from search results. Including a concrete path example further clarifies the expected input format, though it does not explicitly enumerate all scenarios where this tool should be preferred over siblings.

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 annotations already indicate readOnlyHint=true, openWorldHint=false, idempotentHint=true, and destructiveHint=false, making the safe, non-mutating nature explicit. The description adds behavioral details about the return format (ranked matches with canonical URLs and freshness), which goes beyond the annotations. However, it does not mention pagination or other response details, but that is not a significant omission given 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 compact and information-dense, consisting of two sentences that convey the purpose, scope, and output without any redundancy. It is well-structured and front-loads the essential action and resource. No unnecessary details or filler are present.

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 there is no output schema, the description adequately specifies what the tool returns (ranked matches with canonical URLs and freshness). The parameter descriptions in the schema cover the inputs. The context of being a search tool is complete enough for an agent to decide when to use it, though it could benefit from a brief note on how results are ordered or whether any filters are applied, but this is 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?

The schema descriptions for both parameters ('Search terms' and 'Maximum results (default 5, cap 20)') are clear and fully cover their meaning, giving 100% schema_description_coverage. The tool description does not add any additional semantic nuance about the parameters beyond what is already in the schema. Therefore, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a full-text search across specific content types (concepts, tutorials, projects, engineering build log) and specifies what it returns (ranked matches with URLs and freshness). This distinguishes it from sibling tools like read_doc, which likely fetch a specific document, and list_supported_formats. The verb 'search' is explicit and the resource scope is defined.

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 clear context on when to use this tool—when needing to search across the documentation corpus. It does not explicitly mention alternatives or exclusions, but the sibling tool list and the nature of a search tool make it apparent that this is the appropriate choice for discovery rather than direct retrieval. The absence of explicit 'when not to use' statements is a minor gap, but the context is sufficiently clear.

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