TikTok data for agents

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

With no annotations provided, the description carries the burden. It discloses that the call is 'Free,' which is a key behavioral trait (no cost), and it clarifies that it reads the balance using the Authorization header. This conveys the read-only nature and the relevant auth context, which is adequate for a simple balance-check 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 extremely concise: two short sentences, front-loaded with 'Free' and then the purpose. Every word earns its place, 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 the tool's simplicity (no params, no output schema), the description covers all essential context: what it does, which authorization header is used, that it's free, and when to call it. It is complete for an agent to select and invoke 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 tool has zero parameters, so the baseline is 4. The description adds no parameter details, but none are needed since the schema is empty and all relevant context (which API key) is 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?

The description clearly states the tool's purpose: 'Show the remaining credit balance for the API key sent in this connection's Authorization header.' This is a specific verb+resource combination that is distinct from all sibling tools (describe_endpoint, fetch_data, etc.).

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 provides explicit when-to-use guidance: 'Call this before a long run of paid requests.' This gives clear context, though it does not explicitly mention when not to use it or name alternative tools. The guidance is sufficient for this simple tool.

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

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

With no annotations available, the description carries the full burden for behavioral disclosure. It discloses the output content (parameters, allowed values, price, response shape) and the fact that it is free. It does not mention potential rate limits or authentication requirements, but for a metadata read tool this is reasonably transparent.

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 sentence that front-loads the cost ('Free') and purpose. Every phrase adds value—there is no fluff or redundancy—making it highly concise and well structured.

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 metadata tool, the description covers the input (endpoint path), output (full call signature including response shape), and cost (free). It does not explicitly state prerequisites like needing to know the endpoint path first, but that is inherent. The absence of an output schema is mitigated by the description explaining what will be returned.

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% for the single 'path' parameter, including an example. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score 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 clearly states the tool shows the full call signature for one endpoint, including required/optional parameters, allowed values, price, and response shape. The verb 'Show' and specific resource 'call signature' distinguish it from sibling tools like fetch_data or search_endpoints.

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 'Free' signals when this tool is appropriate to use without cost. The description implies it is for inspecting a specific endpoint before calling it, but it does not explicitly compare with alternatives like search_endpoints or fetch_data. Context is clear, though exclusions are not mentioned.

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 paid nature, pricing per call in USDC on multiple chains, payment via x402 or bearer token, and prepaid credits—key behavioral context. However, it does not mention response format, rate limits, or error handling, which are relevant for a generic fetcher.

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 dense but every sentence adds essential information: cost, purpose, parameter passing, pricing details, and a prerequisite hint. The initial 'Paid.' is abrupt but effective. Slightly wordy but appropriately sized for the tool's complexity.

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 generic endpoint fetcher with no output schema, the description covers the critical context: how to invoke (path + params), payment/auth requirements, and a pointer for parameter uncertainty. It lacks mention of rate limits or return shape, but those are endpoint-dependent and acceptable to omit.

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 by clarifying that params bundles both path and query parameters and provides an example ({ query: 'x402', sort: 'Latest' }), which goes beyond the schema's generic description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: 'Fetch live data from any TikTok endpoint.' This clearly distinguishes it from siblings like tiktok_post_search (specific endpoint) and describe_endpoint (metadata tool), establishing fetch_data as the generic executor.

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 tells the agent to call describe_endpoint first if unsure about parameters, and implies when to use: for any TikTok endpoint. This provides clear guidance on using the tool versus alternatives.

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 the operation is free and returns paths, descriptions, and prices, which covers main outcomes. It omits rate limits, authentication, and side effects, but for a search tool these are likely minimal.

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 short sentences: cost, purpose, returns, plus a concise directive. Every sentence adds distinct information with no redundancy, making it 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 search tool with one parameter and no output schema, the description covers purpose, return content, cost, and usage context. It lacks details on result format or pagination, but given the tool's simplicity and the presence of describe_endpoint, it is adequate.

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 provides 100% coverage for the single 'query' parameter, including a description and example. The tool description adds a redundant 'by keyword' but no new semantic information, so 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 ('Search') with a clear resource ('fetcher.sh's TikTok endpoints') and states return values. It distinguishes from siblings by focusing on endpoint discovery rather than data retrieval or detailed documentation.

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 'Start here' provides clear guidance that this tool is the recommended entry point, implying it should be used before other operations. However, it does not explicitly name alternatives or state when not to use it, stopping short of full exclusion 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?

The description discloses meaningful behavioral details: the tool is paid ($0.004), returns live data as JSON, and requires payment via x402 or an Authorization Bearer key. This provides cost and auth context beyond annotations (which are absent). However, it does not explain pagination behavior, result structure, or cursor usage, leaving gaps for a paid 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?

The description is extremely concise, using four short fragments to convey cost, purpose, data format, and payment method. Each fragment carries distinct information with no redundancy or wasted words. It is well-structured and front-loads the most critical detail (cost).

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?

While the description mentions live data as JSON, it does not describe what fields are returned, how pagination works, or what regions are valid. With no output schema and no annotation support, the description leaves the agent without crucial execution context. The paid nature increases the need for clarity about expected results.

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 covers 100% of parameters but with minimal descriptions like 'Optional.' and 'Required.' that add no semantic meaning. The tool description does not mention any parameter, so it fails to explain what cursor, region, sortType, or dateRange control. The enum values are listed in the schema, but their practical meaning is not conveyed.

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 tool as a TikTok post search with the phrase 'Post search — TikTok.' This specifies the verb (search) and resource (TikTok posts), and it distinguishes itself from sibling tools like fetch_data by being platform-specific and search-focused.

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 explicit guidance is provided about when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or compare with siblings like search_endpoints. The only hint is the paid cost, which implies a usage condition but not a decision framework.

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 discloses key behavioral traits: minimum amount, the two distinct workflows (recharge vs. mint new key), the one-time retrieval of the new key, and the irreversible loss if not stored. Minor gaps include failure modes and what happens upon successful recharge, but the core behaviors are well covered.

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, information-dense, and front-loaded with the purpose ('Paid... Buy prepaid credits'). Every sentence contributes meaningful details about payment, workflows, and security caveats, 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 no output schema and no annotations, the description covers the essential operational context: what the tool does, payment method, the two modes, and the critical security caveat about key recovery. It falls short of fully describing the return value for the recharge case and payment execution details, but overall it is quite 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 the baseline is 3. The description adds context about payment and the two modes but does not significantly enhance the understanding of the individual parameters beyond the schema's own descriptions. It does not introduce new parameter-specific 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 clearly states the tool's function: 'Buy prepaid credits with x402' and distinguishes it from sibling data-retrieval tools. It explains the two operational modes (recharging an existing key vs. creating a new one), making the purpose specific and unambiguous.

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

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

The description provides clear contextual guidance for when to use the tool ('so later calls skip the per-call payment round trip') and explains the conditional behavior based on presence/absence of the Authorization header. It lacks explicit 'when not to use' or named alternatives, but the context is sufficient.

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