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?

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?

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 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.