qeaas-mcp

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

Without annotations, the description carries full weight for transparency. It discloses the rejection-sampling technique, absence of modulo bias, anonymity, rate limiting, and transparency via echoed DRBG bytes. This is highly informative for an agent.

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 concise sentences, front-loaded with the core purpose. Every clause adds value without redundancy. Highly 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?

Given no output schema, the description should clarify the return value. It mentions 'echoing DRBG bytes' but does not specify the primary output (e.g., a list of dice roll results). This gap reduces completeness for an agent.

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 add meaning to the parameters. It does not explicitly describe 'count' or 'sides', though the context of dice rolls makes them intuitive. The description fails to provide essential parameter documentation.

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 that the tool performs 'dice rolls' using rejection sampling to avoid modulo bias, distinguishing it from sibling tools like 'random' and 'random_bytes' which serve general random generation. The purpose is explicit and specific.

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 mentions 'anonymous and rate-limited' which informs about usage context, but does not explicitly provide when-to-use or when-not-to-use guidance relative to sibling tools. However, the purpose is so specific that appropriate usage is implied.

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

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

The description mentions the API key requirement and the optional shared secret output (for the demo). However, with no annotations, it fails to disclose error conditions, side effects (e.g., whether it modifies state), or the exact output format. The behavioral transparency is partial but not absent.

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 extremely concise: two sentences, no redundant words. It front-loads the core action and then adds the requirement. Every sentence serves a purpose.

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 lack of output schema and annotations, the description is somewhat incomplete. It does not specify the return format (e.g., base64), error handling, or what happens when 'include_shared_secret' is false. The mention of 'for the demo' introduces ambiguity about production 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?

With 0% schema description coverage, the description compensates by clarifying that 'public_key' is an ML-KEM-768 key and that 'include_shared_secret' controls shared secret inclusion. This adds significant meaning beyond the bare schema, though it does not specify formats or constraints.

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 the specific verb 'Encapsulate' and clearly identifies the resource ('supplied ML-KEM-768 public key') and output ('ciphertext' and optionally 'shared secret'). It also implicitly distinguishes from sibling tools like 'kem_keypair' by focusing on encapsulation.

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 state when to use this tool versus alternatives (e.g., kem_keypair for key generation). The only guidance is implicit: it requires an X-API-Key and is for encapsulation, but no when-to-use or when-not-to-use information 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?

With no annotations, the description carries full burden. It discloses the return of public key, conditional secret key, and seeding approach, but lacks details on side effects, idempotency, or storage behavior.

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 wasted words. The purpose is front-loaded, and the demo-only constraint is clearly stated.

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 single optional parameter tool with no output schema, the description sufficiently covers the key behavior, seeding, auth requirement, and the conditional return. It also differentiates from the sibling tool kem_encapsulate.

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%, but the description adds meaning by explaining that include_secret_key controls whether the secret key is returned and that it's only for demo flow. This compensates for the missing schema descriptions.

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 generates an ML-KEM-768 keypair, specifying the algorithm and seeding method. It distinguishes from sibling tools like kem_encapsulate, which performs encapsulation instead.

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 mentions that the secret key is only for demo flow and requires X-API-Key, but does not explicitly state when to use this tool over alternatives or provide context for typical usage scenarios.

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 compensates by disclosing key behaviors: DRBG-derived (deterministic but seeded), anonymous, rate-limited, and capped at 64 bytes. It does not mention idempotency or side effects, but covers the most critical aspects.

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, information-dense sentence with no wasted words, covering origin, format, anonymity, rate limit, cap, and use case.

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 parameter and no output schema, the description covers the key constraints and usage context. It misses details on error handling or response format, but these are minor 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 0%, but the parameter name 'bytes' is self-explanatory and the description reinforces the cap. The description does not explicitly map the parameter to the output length, but the context is sufficient.

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 random bytes in base64 format, with a specific cap of 64 bytes, and distinguishes itself from sibling tools like random_bytes by mentioning DRBG derivation and its role powering the dice player.

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 implies usage for small, anonymous random byte generation, but does not explicitly compare to alternatives like random_bytes or provide when-not-to-use scenarios.

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 fully discloses behavior: it generates cryptographic bytes (DRBG-derived), outputs hex or base64, includes a signed provenance receipt, and imposes key-based rate limits and quotas. No side effects mentioned, but as a read-like operation this is acceptable.

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 sentence packed with relevant information: endpoint type, cryptographic source, output formats, authentication, size constraints, and rate limits. No unnecessary words; every part 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?

Given the tool has only 2 parameters and no output schema, the description covers the core: what it does, auth, constraints, and output format. It does not elaborate on the provenance receipt format, but the description is sufficient for an agent to invoke 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?

Schema coverage is 0%, so description must compensate. It mentions size range (32..4096 bytes) and possible formats (hex or base64), but does not explain default value for format or explicitly state that size is required. Adds meaning but lacks full precision.

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 verb 'generates' implicitly through 'Canonical developer endpoint' and explicitly states the resource: 'DRBG-derived bytes in hex or base64 with a signed provenance receipt.' It clearly distinguishes from siblings like 'random' and 'dice' by mentioning DRBG and provenance.

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 states requirements: 'Requires X-API-Key; 32..4096 bytes; per-key rate limit + daily quota.' Provides clear context for when to use (cryptographic randomness) but stops short of naming alternatives or stating when not to use.

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 carries full burden. It discloses that the tool is anonymous and rate-limited, and clarifies it only checks provenance not value. However, it omits details on error handling, idempotency, or whether inputs are validated, leaving gaps in behavioral transparency.

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, consisting of two well-structured sentences. The first defines the core purpose, and the second adds important caveats (provenance only, anonymous, rate-limited). Every word serves a purpose 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?

For a simple provenance verification tool with no output schema, the description is moderately complete. It covers the inputs and basic behavior but lacks any indication of the return format (e.g., boolean, details) or whether both parameters can be omitted. Sibling tools are unrelated, so cross-tool context is sufficient.

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 0% description coverage, meaning the schema provides no explanations. The description only states 'from its request_id and/or receipt', which adds minimal semantic value beyond the parameter names. It does not clarify formats, constraints, or whether at least one is required.

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 that the tool verifies provenance of an issued value using request_id and/or receipt, and explicitly distinguishes it from being a value-confirmation oracle. The verb 'verify' and resource 'provenance' are specific and unambiguous.

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 for when to use the tool (provenance verification) and when not to (value confirmation). It also mentions anonymity and rate limits. However, it does not explicitly address prerequisites or alternatives among sibling tools, though siblings are unrelated.

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