venue-watch agent tools

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the call is free and that the data reflects current venues, but it omits other behavioral details such as authentication requirements, rate limits, or whether the result is a live snapshot or cached.

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, tightly written sentence fragment that front-loads the most important qualifier ('Free'). It wastes no words, though the fragment style is slightly telegraphic.

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, parameterless summary tool with no output schema, the description gives a reasonable indication of what data comes back. However, it does not describe the return structure (e.g., a list of venues with counts) or any ordering, which leaves the agent with an incomplete picture of the response.

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 takes zero parameters, so there are no parameter semantics to explain. Baseline 4 applies per the rubric for parameterless tools.

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 identifies the resource (venues that pay AI agents) and the key data point (open task counts), so an agent can understand it is a lookup/summary tool. It lacks an explicit verb like 'list' or 'get', but the intended output is still unambiguous from the phrasing.

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?

There is no guidance on when to use this tool versus alternatives, nor any prerequisite or context information. The word 'Free:' hints at cost but does not tell the agent when this tool is the right choice.

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, and it does disclose two meaningful traits: the call is paid ($0.002 USDC on Base via x402) and the snapshot contents. It does not cover failure modes (invalid address, insufficient balance), rate limits, or what a paid call returns on failure — gaps that matter for a paid, no-annotation 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?

Two tight sentences with the resource and payload front-loaded; the price/cost detail is placed last where it belongs. No filler, though the parenthetical is slightly telegraphic rather than fully readable.

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 one-parameter tool with no output schema and no annotations, the description does cover the returned fields and the cost, which is the important part. It still omits address format expectations and how the x402 payment is actually settled per call.

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% for the single required 'address' param, so the description must compensate. 'Base mainnet address' weakly implies an EVM-format address rather than an ENS name or another chain's address, but no format, casing, or example is given.

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?

States a specific verb+resource ('Base mainnet address snapshot') and enumerates the payload (ETH + major token balances, nonce, contract status). Sibling tools are all unrelated domains (PDF, India lookups, venues), so no differentiation is required. Minor ambiguity remains about whether 'Base' is the chain or a generic qualifier, but the field list resolves it.

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?

No guidance on when to reach for this tool versus alternatives, no prerequisites beyond the implied address argument, and no exclusions. The only orientation is the price line, which tells an agent it costs money but not when the call is warranted.

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 and does disclose two important traits: pages must be public and robots.txt is honoured, and calling costs $0.005 USDC on Base via x402 (a paid, protocol-mediated call). It still omits failure modes, rate limits, and whether JS-rendered content works.

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 dense sentences with zero waste; the core extraction semantics and returned fields are front-loaded before the payment clause. Every phrase 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?

For a 3-param, no-output-schema, no-annotation tool, the description covers cost, crawling constraints, and output fields, but leaves the limit parameter and pagination/edge-case behavior undefined. Adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It enriches two of three params (selector is CSS-selector syntax; url must be a public web page) but the limit parameter is never mentioned, leaving its meaning and maximum of 200 unexplained anywhere.

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?

States a specific verb (extract) plus resource (elements matching a CSS selector from a public web page) and enumerates the returned data (tag, text, attributes). It is distinct from conversion-type siblings like web_to_markdown, though it never names or contrasts them explicitly.

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?

Gives an implicit constraint (public web pages only, robots.txt honoured) but no when-to-use guidance, no prerequisite (e.g. payment) ordering, and no routing against alternatives such as pdf_to_text or web_to_markdown.

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 does disclose a genuinely important behavioral trait — the tool is paid ($0.001 USDC on Base via x402) — but says nothing about payment failure behavior, rate limits, or error handling for invalid codes.

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 short, front-loaded sentences with zero filler; the resource and returned fields come first, with the payment model as a compact trailing clause.

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 lookup with no annotations and no output schema, the description names the returned fields but omits the input format and any error/empty-result behavior. Adequate but with clear gaps an agent would benefit from.

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?

One parameter with 0% schema description coverage. The description implies 'code' is an IFSC code, but never states the expected format (e.g. 11-character alphanumeric) or whether it is case-sensitive, so it only partially compensates for the gap.

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?

States a specific verb (lookup) and resource (Indian bank IFSC), plus the data returned (bank, branch, MICR, UPI/NEFT/RTGS/IMPS). It is clearly distinct from the sibling india_pincode, but never explicitly contrasts with it.

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?

No statement of when to use this tool versus india_pincode or any alternative, and no prerequisites or exclusions. Usage must be inferred from the resource name alone.

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 helpfully discloses the paid-access model ($0.001 USDC on Base via x402), which the schema cannot express, but says nothing about invalid-PIN behavior, error handling, or response shape.

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 tight sentences, zero waste, with the core capability front-loaded and the pricing detail following. Nothing extraneous.

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 trivial one-parameter lookup with no output schema, stating the returned fields (state, district, post offices) and cost is reasonably complete. However, without annotations or an output schema, the missing PIN format and error behavior leave it only minimally sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single 'pin' parameter has 0% schema description coverage, so the description must compensate. Naming 'Indian PIN code lookup' partially clarifies the parameter's meaning, but gives no format guidance (digit count, leading zeros, string form), leaving a real gap.

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 gives a specific verb+resource (PIN code lookup) and enumerates the returned fields (state, district, post offices), so the agent knows exactly what this does. It does not explicitly differentiate itself from the similar sibling india_ifsc, but the domain distinction (PIN vs IFSC) is self-evident.

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?

There is no guidance on when to use this tool versus alternatives such as india_ifsc or base_address, and no prerequisites or input conditions are stated. Usage is only implied by the resource 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?

No annotations exist, so the description carries the full burden. It usefully discloses that calls cost $0.01 USDC on Base via x402, which is real behavioral context an agent must know before invoking. However it says nothing about pagination, rate limits, latency, or failure behavior, leaving significant gaps for a paid query 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?

Two compact sentences with no filler; the purpose and return fields are front-loaded before the pricing note. Slightly dense, but every clause carries 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?

For a 3-parameter tool with no annotations and no output schema, the description covers what is returned and the payment model, which is the most important missing structured context. It still omits parameter semantics and result-count/pagination behavior, so it is only partially 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 0%, so the description must compensate and largely does not. It hints at source selection ('OpenAlex and/or arXiv') but never explains q (the required 2-200 char query), limit (1-25), or the 'all' enum value, leaving all three parameters semantically undocumented.

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?

States a specific verb and resource ('Scholarly paper search') plus the underlying data sources (OpenAlex/arXiv) and the fields returned. An agent immediately knows this is a literature-lookup tool, and no sibling overlaps with that 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?

The description gives no when-to-use context, no exclusions, and does not name an alternative tool or route. The only implied usage comes from the tool name itself, so an agent must infer applicability.

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 and delivers meaningful traits: a per-page output shape, a hard input size ceiling, an explicit capability limit (no OCR), and the payment requirement ($0.005 USDC on Base via x402). It stops short of describing failure behavior, truncation, or encryption handling.

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?

A single sentence with clause-level density: capability first, then constraints, then price. Nothing is padded and the most decision-relevant information 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?

No output schema and no annotations means the description should describe the return shape and edge behavior more fully. 'Per-page text' gestures at structure but leaves format (JSON pages, separators) and the max_chars truncation contract unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It partially covers 'url' by requiring a public PDF under 10 MB, but 'max_chars' is never mentioned, so its truncation semantics are documented nowhere.

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?

States a specific verb (extract), resource (per-page text from a PDF URL), and scoping constraints (public URL, <=10 MB, no OCR). The PDF resource clearly separates it from the web-oriented sibling web_to_markdown without needing to name it.

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 constraints 'public PDF URL', '<=10 MB', and 'no OCR' implicitly define when the tool works and when it does not (scanned/private/oversized PDFs are out of scope). No explicit alternative sibling is named, but the usage envelope is clear.

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 behavioral burden, and it does disclose two non-obvious traits: robots.txt compliance and the $0.02 USDC x402 payment requirement. However, it says nothing about what happens when individual URLs fail, are blocked, or time out in a batch, which is the most important behavioral unknown for a multi-URL read.

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 compact sentences, cost and scope front-loaded, no filler. Every clause carries information an agent needs: volume cap, output format, legal constraint, and price.

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?

There is no output schema or annotation set, so the description should cover more ground: batch partial-failure semantics are absent and max_chars is undocumented. The payment and robots.txt details are valuable, but a paid multi-URL tool needs at least a note on failure behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for both parameters. It effectively documents the 'urls' limit ('up to 10') but never mentions max_chars at all, leaving one of two parameters with no explanation anywhere.

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?

States a concrete verb and resource ('Read up to 10 public web pages') plus the output format ('as clean Markdown'), which is more specific than most siblings. It never names or contrasts with the closely related web_to_markdown or extract tools, so an agent must infer the batch-vs-single distinction on its own.

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 phrase 'in one call' and the explicit 'up to 10' cap imply the batch use case is cheaper than repeated single-page reads, but the description never says when to prefer this over web_to_markdown, extract, or pdf_to_text. Usage is implied rather than stated.

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 this description carries the full behavioral burden, and it does disclose genuinely useful context the schema cannot: the extraction is LLM-free (deterministic), and invocation costs $0.003 USDC on Base via x402, a payment requirement an agent must know before calling. It omits failure modes, redirect/JS-handling behavior, rate limits, and what a truncated result looks like.

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 clauses, zero waste: purpose first, cost second. Nothing could be removed without losing 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?

For a tool with no annotations, no output schema, and two parameters at 0% schema description coverage, this is too thin. It should at least explain max_chars and give some indication of what the Markdown response contains or how failures are reported.

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 does not mention either parameter. 'url' is self-evident from its name and format, but 'max_chars' is entirely unexplained — no unit semantics, no statement of whether the page is truncated or an error is raised when exceeded.

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?

States a specific verb+resource+output format: fetch a public web page, return clean Markdown. The 'public web page' framing implicitly separates it from pdf_to_text, but it never names or contrasts with a sibling explicitly. An agent can identify the action unambiguously.

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?

No when-to-use or when-not-to-use guidance is given. The word 'public' hints that authenticated or private pages are out of scope, but this is inference rather than stated policy, and no alternative tool is offered for those cases.

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 behavioral burden well: it discloses that it is an unpaid probe, what it checks (USDC validity, prices, metadata), the verdict outputs, and the cost ($0.003 USDC on Base). It does not cover auth needs, rate limits, or error 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?

A single dense sentence that front-loads the core purpose and packs useful details without obvious filler. The jargon ('non-canonical/lookalike USDC') is unexplained but still 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 tool with no annotations, no output schema, and 0% schema description coverage, the description does well on purpose and output verdict but omits parameter semantics, especially the method enum. It is adequate but leaves a clear gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description does not mention either parameter. 'Endpoint' loosely implies the url, but the method parameter (GET/POST enum) is entirely undocumented in the 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?

States a specific action ('Pre-payment check') on a specific resource ('x402 endpoint') and lists what it decodes and flags. Clear, but does not distinguish from any particular sibling because none are relevant.

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 phrase 'Pre-payment check' gives explicit context for when to use it: before paying an x402 endpoint. It does not state when not to use it or name any alternative tools.

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