Fact Verification MCP

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

With no annotations, the description fully covers behavioral aspects: return structure, caching (24h), pricing ($0.01/claim, min $0.05, free 10/day), payment flow on 402, and auth bypass via bearer token. No contradictions.

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 returns, caching, pricing, and error handling. Every sentence adds necessary detail without redundancy, making it informative yet 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 the complexity (paid tool with caching and retry logic) and presence of an output schema, the description thoroughly covers all aspects: return format, pricing, error recovery, free tier, and authentication. It leaves no ambiguity for the 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 coverage is 100%, baseline 3. The description adds context beyond the schema: 'claims' is clarified as array of strings (1-50), 'agent_id' scopes the free counter, and 'payment_tx' is used for re-call after 402. This additional information justifies a score of 4.

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 starts with 'Verify many factual claims in one batch fact-check call,' clearly stating the tool's purpose as a batch operation for fact-checking, distinguishing it from the sibling 'verify_claim' which likely handles single claims.

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 mentions when to use this tool (batch of 1-50 claims), caching behavior ('repeats are cheap'), and payment details including the 402 retry flow with payment_tx, providing comprehensive guidance for correct invocation.

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 bears the burden of behavioral disclosure. It reveals that the tool is paid ($0.50), returns structured JSON, defaults to today UTC, and has a payment recovery flow involving a 402 error and payment_tx. It also mentions authentication via Authorization header. It could also mention rate limits or data freshness, but current coverage is strong.

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 four sentences, each conveying essential information: purpose, value proposition, price and default, and payment flow. There is no redundancy. The structure is well-organized and front-loaded with the tool's core function.

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 complexity (payment, free-tier, error handling) and the presence of an output schema, the description covers all critical aspects: what it does, output format, pricing, date handling, payment recovery workflow, authentication, and relation to sibling tools. An agent has sufficient information to invoke it 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?

The input schema already covers all three parameters with descriptions. The description adds value by explaining the payment transaction parameter (payment_tx) in the context of error recovery, and the agent_id's role in scoping the free-tier counter. This enriches the semantic understanding 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 verb 'Get' and the resource 'top 5 signals from today's brief as structured JSON'. It distinguishes itself from the sibling 'daily_brief' by being a cheap sample, and specifies the output format and purpose (deciding whether to buy the full brief).

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 (to get a sample, decide on full brief), default date (today UTC), payment ($0.50), and how to handle a 402 response (pay challenge and recall with payment_tx). It also mentions the free-tier counter scoped by agent_id. However, it does not explicitly state when not to use it (e.g., if the full brief is needed 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?

Given no annotations, the description fully carries behavioral transparency. It explicitly states the cost ($5 per brief), default date (today UTC), expiration at midnight UTC, payment flow (402, payment_tx, stripe_token), and authentication bypass via Authorization header. No contradictions.

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, starting with core purpose and content, then payment details. It is slightly long but every sentence adds value—no redundancy. Front-loading is good, as the main action is stated first.

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 complexity (payment, expiration, provenance) and the presence of an output schema, the description covers key aspects. It might benefit from mentioning error handling or rate limits, but overall it is sufficiently complete for an agent to use the tool 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 100%, so baseline is 3. The description adds value beyond schema by explaining the payment flow context for payment_tx and stripe_token, and the expiration behavior for date. This enriches the agent's understanding of how parameters interact.

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: 'Get the curated daily fact-check brief' and enumerates its contents (top disputed claims, most-verified claims, trending topics, counts by verdict + domain). This distinguishes it from siblings like brief_summary and verify_claim, which serve different functions.

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 explains what the tool does and its paid nature but does not explicitly compare to sibling tools or state when to use it versus alternatives. Usage context is implied (daily fact-check briefs), but no when-not guidance 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 must cover behavioral aspects. It mentions the tool is free and specifies the return content, including a list of data servers. However, it does not mention any potential side effects, authentication needs, or rate limits. Overall transparent for a read-only info tool.

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 purpose. Every sentence adds value: the first states the core function and cost, the second details the outputs. 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?

For a simple tool with no parameters and an existing output schema, the description covers all essential aspects: what it does, what it returns, and lists relevant data servers. Contextually 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?

The tool has zero parameters, and the description does not need to add parameter semantics. The baseline score of 4 is appropriate as the description adds value by detailing what the tool returns without needing to explain 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 clearly states the tool's purpose: 'Get FoundryNet Data Network info + provenance attestation details.' It specifies the exact outputs and distinguishes itself from sibling tools that focus on verification and summaries.

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

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

The description implies usage for obtaining provenance attestation info and network details, but does not explicitly state when not to use it or provide direct comparisons to sibling tools. The context makes it clear, but lacks explicit guidelines.

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 effectively discloses behavioral traits: payment model, daily free allowance, 24h caching, provenance attestation, and payment flow (402 handling). It lacks explicit read-only assurance but implies it through the check purpose.

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 with two focused paragraphs: the first on purpose and capabilities, the second on pricing and payment flow. It front-loads essential information without extraneous content.

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 complexity (payment, caching, provenance) and the presence of an output schema, the description provides sufficient context: purpose, usage, payment rules, caching, and authorization. It covers major behavioral aspects without needing to explain return values.

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 tool description adds no new parameter information beyond what the schema already provides. The description and schema descriptions for url, agent_id, and payment_tx are nearly identical, so 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 clearly states the tool checks a source URL's credibility and trustworthiness, listing specific aspects like domain age, trust signals, bias indicators, and publication history. It distinguishes from sibling tools by focusing on single-source verification for citations.

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 explains when to use the tool (to weigh whether a citation is trustworthy) and mentions caching and payment. However, it does not explicitly state when not to use it or provide direct comparisons to sibling tools like batch_verify or verify_claim.

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 payment model, caching (24h), provenance attestation, domain classification, and verdict types. Beyond what annotations would provide, it adds behavioral context such as payment handling on 402. Missing minor details like idempotency.

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 relatively concise but includes necessary details on payment and caching. The structure is front-loaded with purpose, then behavioral and payment details. Every sentence adds value, though the payment section could be slightly trimmed.

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 complexity (4 params, output schema present, no nested objects), the description is complete. It covers input semantics, output details (verdict, confidence, sources, attestation), payment, caching, and domain classification. The existence of an output schema reduces the need to further explain return values.

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 description adds meaning beyond the schema: explains the 'claim' parameter with an example, clarifies 'payment_tx' is only for re-calls after a 402, and provides context for 'agent_id'. This significantly aids correct invocation.

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: 'Verify a factual claim and return a verdict, confidence score, and cited sources.' It specifies the verb 'verify' and the resource 'factual claim', and the details differentiate it from siblings like 'batch_verify' and 'source_check'.

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 on when to use (verifying factual claims), mentions payment and free allowance, and describes handling of 402 errors. However, it does not explicitly state when not to use or provide direct alternatives, though sibling tools are listed separately.

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