Tapeline

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

No annotations are present, so the description must carry explanatory weight. The description does disclose that this returns the same public rows an anonymous visitor sees, which implies a read-only, publicly visible publication rather. It also says each row contains score, signal, and one-line reasoning. It does not disclose publication refresh behavior, timezone interpretation of 'today', or error behavior, so the disclosure is only partial.

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, dense sentence that fronts-loaded the most important information: what the tool does, where it comes from, and what each returned row contains. There is no filler or repetition.

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, public read-only list endpoint, the description captures the main context well: source, positioning, row contents, and public visibility. It would be stronger if it clarified what 'today' means in practice or what 'signal' represents, but with no output schema present, the return-value description is still substantial.

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 is fully covered at 100%, and the single replacement parameter is well-described by the schema itself ('How many rows, 1-10, default 10'). The description adds no parameter meaning beyond the schema, 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 identifies the verb (get), resource (today's published top picks on tapeline.io), and scope (highest-scoring tickers, same rows an anonymous visitor sees). This strongly distinguishes it from the sibling tools, which target individual tickers, scores, track records, or search results.

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 intended use case is implied clearly: call this when a user wants today's published pocket picks or top-scoring tickers from tapeline.io. However, the description does not explicitly say when to use this tool instead of direct siblings like get_ticker_score or search_tickers, so the agent must infer the boundary.

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 burden and does a solid job: it discloses that it returns every publication occurrence, the comparison against SPY for the next session, and explicitly notes that losses are included. It does not detail output structure, but the core behavior is 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?

A single compact sentence delivers the query scope, the output content, and an important caveat ('losses included') with no filler. It is front-loaded and every element adds value.

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 tool with no output schema, the description supplies enough content for an agent to know what to expect: per-pick positive and negative outcomes versus SPY. It could mention return formats or time ranges, but the description keeps the needed context clear and 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 one required parameter `symbol` has 100% schema description coverage, so the schema already explains it. The description only refers to 'this ticker' and does not add additional parameter context beyond the schema's 'US ticker symbol, e.g. NVDA'.

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 resource: a specific ticker's history of Tapeline daily top 10 appearances plus next-session resolution against SPY. It distinguishes this from siblings: it is for one ticker's historical picks and outcomes, not a daily list or aggregated score.

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 when to use it: when you need a single ticker's pick history with SPY-relative results. It gives context but does not explicitly explain when to prefer this over siblings like get_daily_picks or get_track_record, nor does it state any exclusions.

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 must carry the behavioral burden. It does describe the output data model, current score with a 0-100 range, signal label, confidence, and reason, which is helpful. However, it does not disclose how errors are handled for invalid or delisted tickers, data freshness, or whether the score can change meaningfully over time.

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 first sentence front-loads the core deliverable and output components, and the second sentence gives practical usage guidance.

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, the description provides enough information to invoke it and understand its output: score range, signal, confidence, reasoning, ticker scope, and the trigger context. It does not specify failure modes or edge-case behavior, but those are not critical for this simple lookup tool.

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 already documents the only parameter, `symbol`, with a clear US ticker example. The description adds no additional parameter-specific detail beyond 'single US ticker', so the schema carries the full weight. A baseline of 3 is appropriate because schema coverage is 100%.

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 pair ('Tapeline's current six-factor score') and lists the exact output components: score, signal label, confidence, and one-line reason. It also scopes the tool to a single US ticker, which distinguishes it from sibling tools that likely deal with lists or records.

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 says 'Use when asked what Tapeline says about a specific stock,' providing clear contextual guidance for when this tool is appropriate. It does not explicitly name alternative tools or state when not to use it, but the single-ticker scoping is enough for the typical call.

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 burden of behavioral context. It discloses that the record is public, never edited, aggregated, and includes a sample-size qualifier. It does not explicitly state read-only behavior, but that is strongly implied by the description and zero parameters.

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. It front-loads the resource and its key attributes, then gives a clear usage directive. Every clause adds value.

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 zero-parameter tool with no output schema, the description effectively explains what the agent will receive and when to use it. It covers the key performance metrics and the important sample-size caveat, so an agent can confidently call and interpret the tool.

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 schema provides complete parameter coverage. The description does not need to explain parameters; the baseline of 4 is appropriate since there is nothing to elaborate on.

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 names a specific resource ('Tapeline's public, never-edited record') and precisely enumerates the key data points it returns. It clearly differentiates this tool as the aggregate performance record rather than per-pick or per-ticker data.

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 call the tool: 'Call this before stating any Tapeline performance figure.' This is a clear, actionable guideline, though it does not explicitly exclude sibling tools, the instruction is sufficient for correct use.

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 behavioral burden. It discloses that the tool only covers 'covered tickers' and that the query can be a symbol or company name, but it does not describe what a response looks like, whether results are partial matches, or what happens with ambiguous input. It is minimally transparent but not deficient.

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 concise sentences with no filler. The core action is front-loaded and the intended use case is given immediately. It earns its place by moving the agent quickly to the correct action.

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, single-parameter search tool, the description is largely complete. Since there is no output schema, one might want more detail on the return structure, but the stated purpose of resolving a company name to a symbol makes the expected result obvious enough for most agent use.

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 documents the single 'query' parameter as 'Symbol or company name' with 100% coverage. The description adds a small amount of context by implying the query is what gets resolved to a ticker, but it does not meaningfully expand on the schema. 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 identifies the tool as a lookup operation ('Look up covered tickers by symbol or company name') and states its role in resolving a company name to a symbol. This is specific enough to distinguish it from the sibling read tools like get_ticker_record and get_ticker_score.

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 tells the agent when to use this tool: it should be used to resolve a company name to a symbol before calling the other tools. It does not explicitly state when not to use it, but the intended usage context is clear and actionable.

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