FactReason

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

With no annotations, the description carries the burden. It openly discloses the temporary offline status ('returns no data and bills nothing') and describes the read-only nature of the output. It doesn't mention auth or rate limits, but the key behavioral caveat is addressed.

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 longer than the ideal two-sentence example but remains well-structured: a critical warning first, followed by usage, output, and exclusions. Each sentence contributes information, though some redundancy exists (e.g., repeated 'third-party APIs').

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 there is no output schema, the description fully explains the return contents (removed endpoints, renamed parameters, type changes, migration notes). It also specifies use cases, exclusions, and an alternative tool, making it comprehensive for a tool with moderate 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?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal parameter-level enrichment; it mentions 'between two versions' and 'third-party API' context but doesn't provide additional syntax or edge-case guidance beyond the 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 clearly states what the tool does: 'Find what BREAKS between two versions of a third-party API' and lists specific outputs. It distinguishes itself from siblings by explicitly mentioning the alternative for packages and excluding internal APIs.

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 scenarios ('when code that calls an external API fails after a version bump', 'before upgrading a dependency that wraps an HTTP API'), an alternative tool, and a clear exclusion ('Do NOT use for general programming questions or for APIs you control').

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 the full transparency burden. It implies a read-only lookup via 'Look up' and mentions version-specific behavior, but it does not explicitly state safety (e.g., no side effects), rate limits, or whether it makes network calls. The description is adequate but not rich in behavioral context.

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 long, front-loaded with the primary action and immediately followed by usage context. Every sentence adds value with no redundancy or irrelevant detail.

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 2-parameter lookup tool with no output schema, the description covers the purpose, when to use, and what to expect ('exact request and response shape'). The version ambiguity and lack of explicit return format details prevent a perfect score, but the essential context is present.

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 fully documents both parameters (query and service) with descriptions, giving 100% coverage and a baseline of 3. However, the description's mention of 'at a specific version' is not mapped to any parameter, leaving the agent unclear on how to specify the version. Additionally, both parameters are optional (required: 0), which could confuse usage.

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: 'Look up the exact request and response shape of a third-party API endpoint at a specific version.' It also specifies the resource (external HTTP API) and differentiates from local codebase APIs, making it distinct from siblings like factreason_discover_api.

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 when-to-use guidance: 'Use this before writing or repairing a call to an external HTTP API' and an explicit exclusion: 'Do NOT use for APIs in the local codebase.' However, it does not name alternative tools from the sibling list, so it stops short of a 5.

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 discloses useful behavioral details: it reuses verified specifications to avoid token waste, reports unstated rate limits as 'unstated', and lists exactly what result components are returned. It stops short of stating whether it performs network calls or requires any special permissions, but the provided context is substantial.

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 three sentences, front-loaded with the main purpose, followed by efficiency rationale, an edge-case behavior, and a usage exclusion. Every sentence adds value 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 only one parameter and no output schema, the description thoroughly explains what the tool returns (auth schemes, scopes, rate-limit headers, error table), how it handles unspecified rate limits, and when not to use it. It does not specify the exact return format or behavior for unknown service names, but for the tool's simplicity this is sufficiently complete.

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%, and the single 'service' parameter is well defined with examples. The description adds no extra information about the parameter beyond saying the tool is for third-party APIs, which is already in the schema. Baseline 3 applies.

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 has a specific verb ('Get') and resource ('authentication schemes, required scopes, declared rate-limit headers, and complete error code table for a third-party API'). It clearly differs from siblings like factreason_api_schema or factreason_deprecation_scan by targeting auth/error/rate-limit details.

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 explicitly says to use for third-party APIs and includes a clear when-not ('Do NOT use for APIs in your own codebase'). It also implies the benefit (avoiding token burn) compared to rediscovering semantics, but does not name a specific alternative tool, so it misses the top score.

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 the full burden. It adds valuable behavioral context: 'Accuracy is best-effort — always confirm against the manufacturer datasheet before manufacturing.' This discloses that results are not guaranteed and require manual verification. However, it does not describe return format, error behavior, or rate limits, which would be useful given the lack of 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, front-loaded with the core purpose, followed by usage guidance and an important caveat. Every sentence earns its place and there is zero fluff.

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 two-parameter lookup tool with no output schema, the description covers the necessary context: what it does, when to use it, and a data-quality caveat. It could be more complete by briefly mentioning what is returned if no part is found, but the given information is sufficient for most use cases.

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 the baseline is 3. The description does not add much beyond what the schema already provides for the parameters; both parameters are already clearly described in the schema ('Alternative: free-text search' and 'Exact part number'). The tool description only summarizes the kind of facts returned, not parameter semantics.

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 'Look up' and clearly identifies the resource: verified electronics component facts. It enumerates the types of facts (pin assignments, voltage range, package type, alternative part numbers) which makes the tool's purpose unambiguous and clearly distinguishes it from the API-focused 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?

The description explicitly states when to use the tool: 'Use when wiring or reviewing a hardware design, selecting a substitute part, or checking a pin assignment before committing to a PCB layout.' It provides clear context, but does not mention when not to use it or mention any alternatives, so it falls short of a perfect 5.

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 relevant behaviors: the credit is tied to the calling API key, appears within seconds of payment, card details never reach the service, and a human is required to complete payment. This goes beyond a simple action statement and prepares the agent for expected outcomes and limitations.

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 three concise sentences, front-loaded with the main action, then providing when-to-use and behavioral caveats. Every sentence contributes essential information 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?

For a simple one-param tool with no output schema, the description covers the purpose, trigger conditions, limitations, and expected result (the link to return). It gives the agent everything needed to invoke the tool correctly and handle the response appropriately.

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 already fully describes amountUsd (range 1-1000 and common blocks). The description adds context about prepaid credit but does not elaborate on the parameter itself, so the baseline of 3 is appropriate given 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 creates a secure Stripe checkout link to add prepaid credit to the calling API key. It uses a specific verb and resource, and is easily distinguished from all sibling tools, which are documentation/analysis tools rather than action-oriented payment links.

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 specifies when to use: after HTTP 402 or low balance. It also provides crucial guidance that the agent cannot complete the payment itself and must return the link to the operator, which prevents misuse and ensures proper human involvement.

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 the full burden. It explicitly states what the tool returns (spec-declared sunset dates and replacements) and how it handles unstated fields (marked as unstated), which is valuable behavioral disclosure. It also clearly scopes the tool to third-party APIs, adding context beyond the 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?

The description is three sentences, with the core purpose front-loaded in the first sentence. Each subsequent sentence adds meaningful context about usage, return behavior, and exclusions, with no wasted words.

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's simplicity (2 optional params, no output schema), the description provides strong contextual completeness: purpose, usage timing, exclusions, and return behavior are all covered. The lack of an output schema is mitigated by the explicit statement about return values and handling of unstated fields.

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 clearly described in the schema. The description does not add parameter-specific details beyond the schema, but given full coverage, the baseline 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 'Find' and clearly identifies the resource (endpoints/parameters of a third-party API) and the information returned (deprecation status, sunset dates, replacements). It also distinguishes itself from siblings by explicitly scoping to third-party APIs and excluding own-codebase use.

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 states when to use (before upgrading an API integration or auditing code) and when not to use (APIs in your own codebase), providing strong usage context. However, it does not explicitly reference alternative sibling tools by name, so while the guidance is clear, it lacks the explicit alternative naming seen in the highest-caliber examples.

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 tool scans 'over a thousand services', returns matches 'ranked by how many of their endpoints relate to the capability', and includes examples. It also sets expectations that the output is not the callable shape, directing follow-up. Missing only minor details like no mention of read-only nature or output structure, but sufficient for the tool's scope.

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, each earning its place: purpose, when-to-use, output behavior, and next-step guidance. Information is front-loaded in the first sentence, and no extraneous content exists.

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-parameter discovery tool with no output schema, the description is complete. It explains what it does, how to use it, the ranking logic, example endpoints, and the recommended follow-up tool. No gaps in the context an agent needs.

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 already covers the sole parameter 'capability' with 100% description. The tool description adds value by providing concrete example values ('send an SMS', 'verify a phone number', 'create an invoice', 'manage DNS records'), which enriches the semantic meaning beyond the schema 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 purpose: 'Find WHICH third-party API can do something' with a specific verb and resource. It distinguishes itself from siblings by contrasting with factreason_integration_brief, which is for the callable shape once you know the API.

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 when you know the capability you need but not which provider offers it' and when-not-to: 'Do NOT use when you already know which API you are calling — go straight to factreason_integration_brief.' This names the alternative tool directly.

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 bears full responsibility for behavioral disclosure. It discloses that every field is read from the publisher's specification, that unstated rate limits/auth schemes are reported as unstated, and that it never guesses — a strong behavioral guarantee. However, it does not explicitly state that the tool is a read-only operation or whether it makes external network calls, which would be valuable context. Still, the emphasis on 'read' and 'get' strongly implies non-mutating behavior, so the gap is minor.

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 three sentences long, each earning its place: first states what it does, second gives when to use it, third explains the behavioral guarantee and exclusion. It is front-loaded with the core purpose, uses a clear em-dash structure, and contains zero fluff. 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?

For a simple two-parameter tool with no output schema, the description covers all necessary context: what it returns (callable shape, response codes), when to use it, and its behavioral policy (never guesses, unstated fields reported). It also provides an alternative (factreason_api_schema) and an exclusion, making the tool fully self-contained for an agent to decide whether to use it. Complete.

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 per the rubric the baseline is 3. The description mentions 'service' and 'endpoint' implicitly through the tool's purpose but does not add meaningful parameter-specific semantics beyond the schema. The schema already provides requiredness, types, and examples, so the description adds no extra value here. 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 opens with a specific verb+resource combination: 'Get everything needed to write ONE working call to a third-party API', then enumerates the exact contents (base URL, auth scheme, params, request body, response codes). It directly distinguishes itself from sibling factreason_api_schema by stating it returns the callable shape rather than a list of endpoints, and explicitly says to prefer it when actually making a call. This leaves no ambiguity about 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?

Usage is stated explicitly: 'Use this BEFORE writing or repairing a call to an external HTTP API' and 'Prefer this over factreason_api_schema when you intend to actually make the call'. It also gives a clear exclusion: 'Do NOT use for APIs in your own codebase.' This is a textbook example of when-to-use and when-not-to-use 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?

No annotations are provided, so the description carries the full transparency burden. It discloses the data source (registry metadata), specific outputs, output format (metadata fields with before/after values), and explicitly states that it does not read changelogs or source code. This is exemplary.

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: purpose first, then a detailed list of detected changes, then output format, then limitations. Every sentence earns its place, and there is no fluff 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?

For a metadata-only advisory tool with no output schema, the description explains what it returns, the output format, and its scope and limitations. It provides adequate context for an agent to decide when to invoke it, though it could briefly mention error cases or version validation.

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 covers all four parameters with clear descriptions (100% coverage). The tool description reiterates the from/to versions but adds no new parameter-level semantics beyond the schema, so the baseline of 3 applies.

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 clear, specific verb+resource: 'Before upgrading an npm or PyPI dependency, find out what breaks.' It enumerates the exact types of breaking changes detected, making its scope unambiguous and distinguishing it from API-focused 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?

The opening sentence gives explicit usage context ('Before upgrading'), and the closing limitation clarifies when it should not be relied upon (behavioral changes). It does not explicitly name alternative tools, but the context is sufficient for a typical 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?

With no annotations, the description carries the full burden. It discloses that webhook callbacks occur when the spec is published or auto-ingested, but it does not explain subscription persistence, authentication, or how to manage/remove the subscription.

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 with the core purpose, and an important exclamation about usage. Every word 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?

Adequate for a simple 2-param tool, but the phrase 'demand auto-ingest updates' is ambiguous and the description does not mention what response or confirmation to expect, given there is no 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 description coverage is 100%, so both parameters are already documented. The description adds a little by explaining webhookUrl's purpose and the third-party context for serviceName, but does not substantially go beyond the 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 clearly states the tool's purpose: to subscribe to specification changes or demand auto-ingest updates for a third-party API. It uses a specific verb (subscribe) and resource (specification changes) and distinguishes itself from siblings by explicitly excluding own-codebase APIs.

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 clear context for when to use: for third-party API spec notifications. It includes an explicit when-not instruction (do not use for own codebase) but does not name alternative tools for that case, so it falls short of full alternative awareness.

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