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

With no annotations provided, the description carries the full burden. It does disclose that endpoints are undocumented and volatile, which is useful behavioral context for reliability expectations. However, it fails to mention potential side effects (e.g., network calls, rate limits, or whether it is strictly read-only). For a health-probe tool this is a notable gap, but the description at least sets expectations about endpoint instability.

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 with no filler. The primary action is front-loaded ('Probe each backend and report which are answering'), followed by a single usage directive. Every word earns its place, making it extremely efficient for an agent to parse quickly.

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 (zero parameters, no output schema), the description covers purpose and usage well. The only missing piece is an explicit description of the output format (e.g., a list of backend names with statuses), though 'report which are answering' implies this. It could also mention whether the tool returns success/failure codes, but overall it is nearly complete for the tool's 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?

The tool has zero parameters, so the description naturally has no parameter details to add. The schema is empty and the description correctly focuses on behavior rather than inputs. A baseline of 4 is appropriate because no compensation is needed for undocumented 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 states a specific action ('Probe each backend') and a clear outcome ('report which are answering'), which is unique among siblings that focus on search, quotes, history, and indicators. It clearly identifies the resource (backends) and the diagnostic intent, leaving no ambiguity about what the tool does.

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 specifies when to use the tool ('Use this when a tool starts failing') and adds crucial context ('these TradingView endpoints are undocumented and change without notice'). This gives an agent a clear trigger condition and expectation, effectively distinguishing it from routine data-access tools. No alternative is named, but the condition alone is sufficient 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 adds useful behavioral context: data source with fallback, oldest-first ordering, and both epoch seconds and ISO timestamps in UTC. However, with no annotations at all, it does not disclose error behavior, rate limits, or implications of the two-provider fallback; the full burden is on the description and it covers only part of that burden.

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?

Three compact sentences, each carrying distinct information: what is returned, the data source fallback, and the response ordering/timestamp format. No filler or repetition of the title or schema field descriptions.

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 and no output schema, the description should go further. It states ordering, timestamps, and source, but omits the response format, failure behavior, fallback trigger conditions, and any limits on historical depth—important details for an agent that must call 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?

Schema description coverage is 100%, so the schema already documents all four parameters. The description adds no new parameter-level meaning beyond context like ordering and timestamps, keeping it at the baseline expected when structured schema is complete.

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 and resource: historical open/high/low/close/volume candles for one instrument. It is clearly distinguishable from its siblings—tv_quote for current pricing, tv_indicators for computed metrics, and tv_search_symbol for discovery.

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 explicit when-to-use or when-not-to-use guidance. The use case is implied by 'Historical... candles for one instrument,' but the description never names alternatives or tells the agent to prefer tv_quote for live prices or tv_indicators for derived values.

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 provided, the description carries the full burden. It discloses that values are precomputed, TradingView-only, and include a rating scale, which is useful. However, it does not mention error behavior, symbol validation limits, authentication needs, or response format details, leaving notable transparency gaps.

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 core purpose is front-loaded in the first sentence, followed by the key caveat and then the indicator list. The long list is necessary because the schema lacks an enum for indicators, but it does make the description verbose. Overall, each sentence 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 four-parameter tool with full schema coverage and no output schema, the description covers the purpose, data source, rating scale, and valid indicator values. It lacks response-structure details and explicit alternative routing, but what an agent needs to invoke it correctly is largely 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?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful value beyond the schema by enumerating the exact valid indicator names (RSI, MACD.macd, SMA20, etc.) and clarifying the rating scale associated with Recommend.* indicators. This compensates for the schema's generic 'Indicator names' 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: it retrieves TradingView's precomputed indicator values and a Strong Buy..Strong Sell technical rating at a chosen timeframe. It also distinguishes itself from the 'licensed fallback' by noting that fallback does not compute indicators, which differentiates it from data-provider siblings.

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 clear context: use this tool when you need actual indicator values, because 'the licensed fallback does not compute indicators.' It does not name sibling tools explicitly or provide when-not-to-use exclusions, but the TradingView-only caveat is a practical usage signal.

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. It discloses the TradingView-first fallback to a configured licensed provider and warns about the `freshness` field, which is the key operational caveat for a quote tool. It does not cover rate limits or error behavior, but the most important behavioral trait is present.

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 carrying distinct information: return fields, accepted formats, provider routing, and the freshness caveat. Information is front-loaded with the core purpose and there is no filler.

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 read-only quote tool with no output schema, the description adequately names the return fields (price, change, volume, freshness) and the accepted input formats. It does not describe units or error cases, but the schema covers market slugs and the core call contract is clear.

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 the baseline is 3. The description adds genuine value by explaining accepted ticker formats (bare vs. qualified with exchange prefixes) and the one-or-more-instruments constraint, which enriches the generic schema text for `symbols`.

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 and resource: 'Current price, change, and volume for one or more instruments.' It clearly conveys the tool's snapshot purpose and gives ticker-format examples, though it never explicitly names or differentiates from sibling tools like tv_history or tv_indicators.

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 this is for current quote data and includes a useful caution to check the `freshness` field before treating a price as live. However, it does not explicitly state when to prefer this over tv_history, tv_indicators, or tv_search_symbol, so alternative routing is left to inference.

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 provided, the description carries the full burden of behavioral disclosure. It explains the output format (fully-qualified tickers) but does not mention potential side effects, rate limits, or what happens when no matches are found. However, as a read-only search, the main behavior is straightforward and the description does not mislead; it just adds limited extra 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 two sentences with no wasted words. It front-loads the core purpose and immediately follows with a clear usage directive. It is perfectly sized for an agent to grasp quickly.

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 three parameters and full schema coverage, the description covers the essential context: purpose, output format, and when to use it. It lacks an explicit description of the return structure (beyond the symbol format), but given the simplicity and lack of an output schema, this is a minor gap. Overall, it is complete enough for an agent to call 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?

Schema description coverage is 100%, and the parameter descriptions in the schema are already informative (e.g., query examples, exchange restrictions). The tool description itself adds no additional parameter semantics beyond what the schema provides, so a 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 clearly states the tool's function: resolving a ticker, company name, or ISIN into fully-qualified TradingView tickers (EXCHANGE:SYMBOL). It uses a specific verb (resolve), specifies the resource (symbols), and defines the output format, making it distinct from sibling tools like tv_quote or tv_history.

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 instructs when to use this tool: 'Use this first when the exchange is ambiguous, or when a symbol lookup failed elsewhere.' This gives concrete conditions and implies it is a first-resort for symbol resolution, clearly separating it from other tools that consume resolved symbols.

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