basecite

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

Annotations already cover read-only, idempotent, and non-destructive behaviors. The description adds value by specifying the returned record is 'bounded', includes provenance and not_evaluated fields, and explicitly excludes raw file download, truth verification, company verification, and ranking claims – providing transparency about the tool's limitations 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 two sentences with the main purpose front-loaded. The second sentence's long exclusion list is purposeful for disambiguation, though slightly dense; 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?

Given the presence of an output schema and safety-covering annotations, the description sufficiently covers the tool's purpose, scope, and behavioral boundaries. Minor gaps include no mention of not-found behavior or how the two identifiers relate, but these are secondary for a simple single-record 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?

Schema description coverage is 0%, so the description must compensate. It indicates the parameters are used as lookup keys ('by org_id and upload_id') but does not explain their meaning, relationship, or constraints beyond what the schema already encodes via names and length limits.

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 uses a specific verb ('Return') and names the exact resource ('one bounded customer/solution-company submitted AI context record') identified by org_id and upload_id. It also lists what the tool does not return (no raw file download, no list-all, no unbounded export, no verification), clearly distinguishing it from broader retrieval 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 intended use is implied: call this tool when you have both org_id and upload_id and need a single AI context record. The negative constraints establish boundaries (e.g., not for list-all or export), but no explicit when-to-use-or-avoid wording or named sibling alternatives 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 mark this as read-only, idempotent, and non-destructive, so the description need not repeat those. It adds meaningful behavioral context by stating that the tool 'never returns customer data' and 'requires no credential,' which are useful safety and access insights 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?

Two concise sentences provide the core purpose and key safety context with no filler. The main capability is front-loaded, and every sentence 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?

This is a simple, zero-required-parameter, read-only introspection tool with an output schema and complete annotation coverage. The description covers the essential context: machine-readable output, no customer data, and no credential requirement, making it fully adequate for correct invocation.

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 optional parameter include_boundaries is fully documented in the schema. The description itself adds no additional parameter meaning, so a 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 uses a specific verb and resource: 'Return the machine-readable capabilities and integration boundaries of the BaseCite service.' This clearly distinguishes the tool from siblings like basecite_get_ai_context and basecite_get_public_discovery by focusing on service capabilities and boundaries rather than AI context or public discovery data.

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

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

The description implies this tool is for introspecting what BaseCite can do and where its boundaries are, and notes that no credential is needed. However, it does not explicitly state when to prefer this over the sibling tools or when not to use it, leaving some routing inference to the agent.

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

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

The annotations already declare read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond these annotations by disclosing that the tool never returns customer data and requires no credential, which is useful for an agent deciding whether this tool is safe and appropriate.

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 short sentences with no redundant filler. The core function is front-loaded, and the behavioral caveat about customer data and credentials is placed directly after, making it efficient 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?

For a simple tool with no required parameters, full schema coverage, and an output schema, the description covers the essential facts: what it returns, that it is public-only, and that no credentials are needed. It lacks explicit sibling differentiation, but the tool's low complexity means nothing critical 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?

The single parameter include_registry is fully described in the schema, so schema coverage is 100%. The description does not add much parameter-level detail beyond the schema, but it does slightly reinforce that registry links are part of the public discovery output. 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 states a specific verb and resource: returning BaseCite public developer, API, MCP, registry, and safety-boundary links. It clearly separates this from the sibling tools by emphasizing 'public discovery' and 'never returns customer data', making the tool's scope immediately understandable.

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 context by noting it requires no credential and only returns public links, which suggests when to use it. However, it does not explicitly compare against basecite_get_ai_context or basecite_get_capabilities, nor does it state when not to use this tool, leaving the routing mostly to inference.

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