Strale - 290+ API capabilities for AI agents

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

With no annotations provided, the description carries full behavioral disclosure burden and excels: it specifies return format ('EUR cents (integer) and formatted EUR string'), authentication requirements ('Requires an API key'), and error behavior ('returns an auth instruction if none is configured'). This comprehensively covers what would typically be in annotations or output schemas.

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?

Four tightly constructed sentences with zero redundancy: (1) purpose definition, (2) pre-usage context, (3) post-usage context, (4) return format and auth requirements. Every sentence adds distinct value beyond the structured fields, with critical information 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?

Despite lacking an output schema and annotations, the description fully compensates by explaining return values (balance formats), authentication behavior, and usage timing. For a simple zero-parameter balance check, this coverage is complete and sufficient for correct agent 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?

The input schema defines zero parameters (empty properties object). Per evaluation rules, 0 parameters warrants a baseline score of 4. The description appropriately does not mention parameters since none exist, and the schema coverage is 100% (vacuously true for empty 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 opens with a specific verb ('Returns') and clear resource ('Strale wallet balance'), precisely defining the tool's function. It effectively distinguishes itself from siblings like strale_execute and strale_transaction by focusing on balance retrieval rather than action execution or transaction handling.

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?

Provides explicit temporal guidance: 'Call this before executing paid capabilities to verify sufficient funds, or after a series of calls to reconcile spend.' This clearly positions the tool as a pre/post check relative to paid operations, distinguishing it from strale_execute which likely performs the actual spending.

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 and excels by disclosing: the return object structure (quality score, latency, price, provenance), the freemium model (5 free capabilities, 10/day limit), payment mechanism (debits from wallet), and failure mode (execution fails if over max_price_cents).

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?

Six information-dense sentences with zero waste. Front-loaded with purpose ('Executes...'), followed by usage context, prerequisites, return value details, and cost model. Every sentence earns its place 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 lack of output schema, the description compensates by detailing the result object contents. It covers the 271-capability registry context, billing implications, rate limits, and integrates with 2 sibling tools (strale_search, strale_balance) to complete the usage picture. Sufficient for a paid execution 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 coverage is 100%, establishing a baseline of 3. The description adds value by contextualizing parameters within the workflow—explaining that slug and inputs come from strale_search results and that max_price_cents relates to high-value calls requiring balance checks—adding semantic meaning beyond isolated field 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?

The description clearly states the tool 'Executes a Strale capability by slug and returns the result,' providing a specific verb (executes) and resource (Strale capability). It distinguishes itself from siblings by explicitly referencing strale_search and strale_balance as prerequisites in the workflow.

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?

Provides explicit when-to-use guidance ('when you need to perform any verification, validation, lookup, or data extraction') and clear prerequisites ('Call strale_search first to find the right slug' and 'check strale_balance first for high-value calls'). Names specific sibling tools as alternatives/prerequisites.

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 the full burden: it discloses the exact return values (5 specific capability slugs with descriptions/examples), auth requirements ('No API key required'), and the boundary between free and paid tiers (271 paid capabilities).

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 efficiently cover purpose, usage timing, and output structure. Every sentence adds distinct value without redundancy, and critical information (no API key needed) is 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 zero-parameter discovery tool with no output schema, the description comprehensively compensates by detailing the exact return structure (specific slugs, descriptions, example inputs) and the pathway to paid features.

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 zero parameters, triggering the baseline score of 4. The description adds relevant context that no API key is required, though there are no parameters to semantically enrich.

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 ('Lists') with clear resource ('free capabilities') and scope ('without an API key'), immediately distinguishing it from sibling tools like strale_execute or strale_balance which likely require authentication.

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 when to use ('Call this on first connection to see what you can do immediately') and implies the alternative workflow (271 paid capabilities vs these 5 free ones), though it does not explicitly name sibling tools as 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 the full burden. It successfully discloses the return format (markdown document), specific content sections covered (dual-profile model, 5x5 matrix, audit trails, etc.), and authentication requirements ('No API key required'). Minor gap: doesn't mention caching or idempotency, though implied by the nature of the endpoint.

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?

Well-structured with four distinct information units: purpose, usage trigger, content enumeration, and auth note. The list of eight content topics (dual-profile scoring model through current limitations) is slightly verbose but justified as it helps agents determine if this tool contains needed 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 simplicity (no input params) and lack of output schema, the description adequately compensates by detailing the structure and scope of the returned markdown document. It provides sufficient context for an agent to understand what knowledge it will gain without needing an explicit output schema.

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 zero parameters. Per evaluation rules, zero parameters defaults to a baseline score of 4. The description appropriately acknowledges this implicitly by not mentioning parameters.

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 ('Returns') and resource ('Strale Quality Score methodology as a full reference document'), clearly distinguishing it from operational siblings like strale_execute, strale_search, and strale_transaction which likely perform actions or queries rather than returning documentation.

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 when to call the tool: 'when you need to understand how capability quality scores are computed, or when a user asks how trust is evaluated.' This provides clear differentiation from strale_trust_profile (which likely returns a specific profile instance) and operational 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?

With no annotations provided, the description carries full behavioral disclosure burden and succeeds admirably. It explicitly documents return values ('server status, version, tool count...'), authentication requirements ('No API key required'), and operational scope (reachability check), compensating completely for the lack of structured annotations or output 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?

Four sentences total, each earning its place: (1) purpose definition, (2) usage timing, (3) return value disclosure, (4) authentication note. No redundancy or filler. Information is front-loaded with the core action in the first sentence.

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 zero-parameter health check tool with no output schema, the description is exhaustive. It compensates for missing output schema by detailing return fields, covers authentication, and provides usage context. Nothing essential is missing given the tool's low 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?

Input schema contains zero parameters, establishing a baseline of 4 per evaluation rules. The description correctly omits parameter discussion since none exist, neither adding nor subtracting value in this dimension.

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 opens with specific verbs ('Checks') and clear resources ('Strale API', 'MCP server'), explicitly defining this as a connectivity verification tool. It effectively distinguishes from operational siblings like strale_execute and strale_search by focusing on infrastructure health rather than business logic.

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?

Provides explicit temporal guidance: 'Call this before a series of capability executions' and situational guidance: 'when troubleshooting connection issues.' This clearly defines both when to use (pre-flight checks) and implies when not to use (not for actual data retrieval or mutations).

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 disclosure burden. It effectively compensates by stating 'No API key required,' describing the return structure (ranked results with specific fields including price and SQS score), and noting the registry scope (271 capabilities). Lacks rate limits or pagination bounds.

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?

Five sentences with zero waste: purpose, usage guidance, return format, registry scope, and auth requirements. Information is front-loaded with the core action, and every sentence earns its place without 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 absence of an output schema, the description admirably details the return structure and field types. With 100% schema parameter coverage and clear behavioral context, it is nearly complete; minor gaps include pagination result limits and error state descriptions.

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%, establishing a baseline of 3. The description adds value by clarifying that the query parameter accepts 'natural language' (not just keywords as the schema suggests) and cross-referencing the category values ('compliance, finance, web intelligence, developer tools'), enhancing semantic understanding beyond the schema's technical 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?

The description opens with a specific verb ('Searches') and resource ('Strale capability registry'), clearly distinguishing it from sibling tools like strale_execute or strale_balance. It details the search mechanisms (keyword, category, natural language) and return format, leaving no ambiguity about the tool's function.

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?

Provides explicit positive guidance: 'Use this when you need to find the right capability for a task but don't know the exact slug.' This implicitly contrasts with strale_execute (which requires the slug), though it doesn't explicitly name the alternative tool.

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 full burden. Discloses rich return value details (inputs, outputs, latency, price, data provenance, status, failure categorization) compensating for lack of output schema. Notes critical auth behavior: 'Free-tier transactions are accessible by ID without an API key'. Minor gap: doesn't explicitly confirm read-only/safety nature (implied by 'Retrieve' but not stated).

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?

Four sentences, zero waste: purpose (sentence 1), return values (sentence 2), usage guidelines (sentence 3), authentication note (sentence 4). Front-loaded with core action and perfectly sized.

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?

Despite no output schema, description comprehensively lists return fields. Covers authentication edge case (free-tier access). Single parameter well-contextualized. Minor deduction for not explicitly stating safety profile (read-only) given lack of annotations.

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% (transaction_id fully documented in schema as 'Transaction ID returned from a strale_execute call'). Description mentions 'by transaction ID' but adds no semantic detail beyond what the schema already provides, meeting the baseline for high-coverage schemas.

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?

Opens with specific verb ('Retrieve') + resource ('past execution record') and explicitly scopes to 'transaction ID'. Explicitly distinguishes from sibling strale_execute by stating it inspects 'what a previous strale_execute call returned', making the relationship between tools crystal clear.

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?

Provides explicit when-to-use guidance: 'Use this to inspect what a previous strale_execute call returned, debug failures, or provide an audit trail'. Names the sibling alternative directly, implying the correct workflow (execute first, then retrieve).

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 the full burden. It successfully discloses the complete return value structure (SQS scores, grades, execution guidance, test history, cost envelope) and notes 'No API key required.' It implies a read-only operation through the verb 'Returns,' though it could explicitly state this is non-destructive.

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 structured logically: purpose (sentence 1), usage context (sentence 2), and output/behavioral details (sentence 3). Every sentence earns its place with zero redundancy. Information is front-loaded with the core function before detailing return values.

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 absence of an output schema, the description appropriately compensates by enumerating all return fields and their formats (score ranges, grade scales, guidance options). For a 2-parameter tool with simple types and full schema coverage, the description is complete, though explicit error handling notes could further improve 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?

Schema description coverage is 100%, with the slug parameter including helpful examples ('swedish-company-data') and the type parameter explaining the enum values. The description references 'capability or solution' aligning with these parameters but does not add syntax details or usage nuances beyond what the schema already provides, warranting the baseline score.

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 the tool 'Returns the trust profile for a capability or solution,' using a specific verb and resource. It clearly distinguishes itself from sibling tools like strale_execute (which likely runs checks) and strale_search (which likely finds capabilities) by focusing on metadata/quality retrieval.

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?

Provides explicit guidance: 'Call this before relying on a capability for high-stakes decisions, or when a user asks how reliable a specific check is.' This establishes clear trigger conditions. However, it could be improved by explicitly contrasting with strale_execute to clarify this is not for running the actual check.

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?

Without annotations, the description carries the full burden. It discloses behavioral traits such as mode-specific budgets (8s for outbound, sub-second for reverse-call), evaluator sets, and support for pre-trade simulation. However, it lacks details on error handling, rate limits, or data freshness, which prevents a perfect score.

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 well-structured with major points upfront (verdict, modes). It is concise for the breadth of information but could be slightly tightened (e.g., repeating 'EVM wallet/contract/token' versus schema). Overall, it is effective and not 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 no output schema, the description compensates by listing return fields (verdict, reason_codes, evidence map, audit_url). It covers evidence types (sanctions, mixer-graded, etc.) but lacks details on the structure of reason_codes or critical_flags. With 8 parameters and complex functionality, it provides solid but not exhaustive completeness.

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% description coverage, so baseline is 3. The description adds value by explaining the 'mode' parameter's default and use case, and elaborating on 'target' by listing examples (EVM addresses, Solana addresses, protocol slugs). This extra context justifies a score above 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 states the tool's function: returning a decision-ready answer about on-chain counterparties, including verdict, reason_codes, and evidence. It lists specific use cases (sending, swapping, bridging) and distinguishes itself from sibling tools like strale_balance or strale_execute by focusing on assurance and risk assessment.

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 the tool: 'Use before any agent transacts on-chain' and details two modes ('outbound' for pre-payment vetting, 'reverse-call' for x402 gating) with clear context for each. It effectively differentiates from siblings by emphasizing pre-transaction risk assessment.

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