Sourcey

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context by stating that 'unknown eligibility and unsupported stacking remain explicit,' informing the agent about how the tool handles incomplete data. 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?

Two sentences, no filler. The first sentence conveys the core action and scope, and the second highlights an important behavioral guarantee. 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?

With an output schema present and annotations covering safety, the description provides sufficient context for correct invocation. It explains the comparison dimensions and the handling of unknown information. Minor omission: it doesn't describe the output format, but that is covered by the 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?

Schema coverage is 100% with both parameters (offers and facts) fully described. The description adds value by connecting these parameters to the comparison dimensions (value, access requirements, eligibility, etc.), making it clear how the inputs drive the output beyond raw schema definitions.

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 purpose: comparing startup credit or deal offers, with a clear scope (two to ten) and the attributes compared (value, access requirements, eligibility, freshness, evidence). It clearly distinguishes this from sibling tools like search_offers by focusing on side-by-side comparison rather than 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 gives clear context for when the tool is appropriate: when you need to compare multiple offers side by side. It does not explicitly mention when not to use it or provide alternative tools, but the scope is unmistakable and aligns with the tool name.

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, non-destructive behavior. The description adds valuable context about the change feed semantics: signed cursor for pagination and the types of changes tracked (added, changed, withdrawn, removed). This goes beyond the annotations and helps the agent understand the tool's 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?

The description is a single, well-structured sentence that front-loads the core purpose and lists the entity types and action types. Every word contributes meaning, with no redundancy or filler.

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 an output schema, so return format is covered there. The description explains the change feed purpose, the types of changes, and the optional cursor for pagination. It sufficiently distinguishes from sibling tools, though it could optionally mention ordering or limitations, but these are not essential given the 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?

Schema coverage is 100% and the single optional cursor parameter is already fully described in the schema as an 'Opaque signed cursor returned by the previous change-feed page'. The description only mentions 'after an optional cursor', which adds minimal extra meaning beyond the schema's own description.

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 specific verbs and resources: 'Track added, changed, withdrawn, and removed startup credits, discounts, free tiers, perks, and deals'. It clearly distinguishes this tool from siblings by specifying the change feed nature, which contrasts with search, compare, and inspect 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 description clearly implies use for tracking changes over time via the signed change feed, with an optional cursor for pagination. It does not explicitly state when not to use it or mention alternative tools, but the context is clear enough for an agent to decide when to invoke it.

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 readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety well. The description adds 'freshness and dispute status' as behavioral context, which is meaningful. However, it doesn't disclose what happens with invalid digests or disconnected revisions.

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 purpose, concise. The 'one to ten' and 'exact revisions' details are useful and add specificity without waste. No redundancy with annotations.

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?

Tool has an output schema (which handles return-value documentation), full annotation coverage, and 100% schema description coverage. Given this strong structured foundation, the description reasonably covers purpose and key behaviors. 'Freshness and dispute status' hints at output richness. Adequate but not exceptional.

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 fully documents revision_digests. The description adds 'exact revisions' and 'returned by Sourcey search or comparison' context, which clarifies the subtype of digests (offer revisions vs arbitrary hashes). Baseline 3 with modest added value.

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 'Inspect the provenance behind startup credits and deals' which has a clear verb+resource. However, it doesn't distinguish this from siblings like compare_offers or get_changes. 'Resolve one to ten exact revisions to supporting events and observations' clarifies scope but the differentiation from get_changes is ambiguous.

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 resolving 'exact revisions' returned by 'Sourcey search or comparison,' partially implying input prerequisite. However, there's no explicit when-to-use vs. alternatives guidance for compare_offers or get_changes, nor exclusions for when NOT to use this 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?

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context: it never assigns a grade, does not treat startup credits/deals as service readiness, and returns precise missing fields when the service graph is incomplete. These behavioral traits go beyond the annotations and help an agent set expectations.

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 dense paragraph with an overlong list of assessment aspects ('vendor product, API, SaaS service, authentication path, payment path, provisioning flow, operating interface, recovery flow, and exact agent standard'). While it communicates purpose, it could be tightened and front-loaded with the key behavior (YAML generation) before the exhaustive enumeration.

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 the extreme complexity of the input schema (16 parameters, 11 required, nested objects, enums), the description offers no guidance on how to construct a valid request, what the required fields represent, or any example. The absence of parameter semantics makes it nearly impossible for an agent to call this tool correctly without external knowledge.

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%, and the description provides zero explanation of the 16 parameters or their meaning. Required fields such as command_contract, scope_source_uris, assessment_targets, and others are completely unexplained. The description focuses solely on the tool's output and constraints, leaving the agent to guess how to populate complex nested structures.

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: to prepare canonical GitHub-ready YAML for an Agent Readiness assessment. It enumerates the specific scope (vendor product, API, SaaS service, authentication path, etc.) and explicitly differentiates from grading or credit handling, making its purpose 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 does not mention when to use this tool versus alternatives, nor does it name any sibling tools or conditions for selection. It implies a niche use (readiness declarations) but offers no explicit guidance on when to prefer it over compare_offers, inspect_evidence, etc.

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 readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds valuable behavioral context: 'unknowns remain explicit' indicates how the tool handles missing or ambiguous facts, and 'evaluate eligibility from typed company facts' clarifies the reliance on typed facts. This goes beyond the annotations by disclosing the tool's approach to uncertainty, which helps the agent set expectations.

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 no fluff. It front-loads the primary purpose, lists the offer types, then states filtering and eligibility evaluation, closing with the behavior on unknowns. Every phrase earns its place, and the structure is efficient for quick parsing.

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 5 parameters, one required, nested objects, and an output schema, the description covers the core function and key behavioral traits (explicit unknowns) adequately. It does not mention the limit or entities/categories parameters directly, but these are fully documented in the schema. The output schema covers return value details, so the description does not need to explain them. It is complete enough for an agent to call correctly, though it could be slightly richer on usage guidance.

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%, so all parameters have descriptions. The description does not add significant new meaning beyond the schema; it summarizes filters (query, vendor, category) but 'vendor' is not an exact parameter name (likely entities), and the schema already details each parameter. The baseline of 3 applies because the schema carries the parametric load; the description adds minimal extra clarity.

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 searches startup credits and deals, enumerating the types (cloud credits, SaaS discounts, free tiers, grants, perks, partner offers) and explicitly mentions filtering and eligibility evaluation. It distinguishes itself from sibling tools like compare_offers (comparison) and prepare_declaration (declaration) by focusing on search. The verb-resource combination is 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 context that this is a search tool for startup credits/deals with filtering and eligibility evaluation, but it does not explicitly state when to use this tool versus alternatives or when not to use it. It implies the search role but lacks direct guidance on routing to siblings such as compare_offers for post-search comparison. The usage context is clear enough for basic selection but incomplete for optimal differentiation.

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