Real Alpha

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the description does not need to re-litigate safety. It adds genuine behavioral context beyond that: results are ordered by group size, grouping is by stance on the question, and each entry carries speaker/show/date provenance. It does not say whether all answers are returned or a sample, which caps it below 5.

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 opening sentence and result-shape clause are well front-loaded and earn their place. However, the bulk of the description is a near-verbatim repetition of all 34 enum values, which is largely redundant with the schema and makes the definition long and unwieldy.

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, so describing the return structure is necessary and the description does it well (grouping, ordering, per-item fields). Minor gaps remain: no statement about coverage/completeness of answers or whether an unknown/unmatched question yields an empty result.

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% and the enum already constrains the parameter, so the baseline is 3. The description earns more by glossing each slug in natural language (e.g. 'is-openai-profitable (How does OpenAI make money, and is it profitable?)'), which genuinely helps the agent map a user's phrasing onto the right id.

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 ('how the people building AI answer one of the big questions') and goes further, describing the exact shape of the result: answers grouped by stance, largest group first, each with quote, speaker, show and date. An agent can tell this is a curated answer-roundup tool, distinct from raw moment retrieval 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?

Usage is only implied: the agent infers it must pick one of the listed question ids. There is no explicit 'use this when...' and no mention of the alternatives in the sibling set (what_did_they_say, search_moments, transcript_excerpt) or how to choose among them.

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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so safety is covered. The description adds valuable substance about what the call returns — speaker, show/date, quote, summary, takeaways, topics, big questions, and other best minutes — which is important because there is no output 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?

A single front-loaded sentence that identifies the resource and input first, then enumerates returned content. The long content list is dense but each item is informative for an agent that has no output schema.

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 read tool with annotations and no output schema, this is complete: input source, scope, and the payload contents are all described, leaving no material ambiguity about how to call it or what it yields.

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% and the schema already explains the slug format and the page-link alternative. The description's mention of 'from search results' adds only marginal guidance beyond that, 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?

States the resource precisely (one moment, in full) and enumerates the content it returns, which clearly separates it from list/search siblings like search_moments and latest_moments. The verb is implied rather than stated ('One moment in full'), keeping it just short of a crisp verb+resource formulation.

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?

'by its slug (from search results) or its realalpha.app/s/ link' tells the agent where the required input comes from, implying it is a follow-up call after search. However, it never explicitly contrasts with siblings such as search_moments or answers_to, so when-to-use is only implied.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's job is to add semantics — and it does: results are ordered newest-episode-first and represent the best minutes of each episode rather than an exhaustive transcript dump. It stops short of describing pagination or result shape, but the key behavioral traits are disclosed.

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 front-loaded sentence with no filler, and the ordering constraint appears before the optional filter. The phrasing "moments Real Alpha cut" is slightly colloquial but still parses 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 2-optional-parameter read-only listing tool with full schema coverage, this is close to sufficient, but with no output schema the description never says what a "moment" returns (title, timestamp, show, clip reference), which an agent needs to decide whether this tool fits.

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% (n default 10, max 20; show max length 100), so the schema already carries the parameter detail. The description adds only the loose mapping of "every show or only one" to the show filter and the ordering implication for n — marginal value beyond the schema, which matches the baseline 3.

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 newest moments Real Alpha cut, newest episode first" names the resource (moments), the ordering (newest episode first) and the unit (each episode's best minutes), which is far more specific than the bare title. It does not explicitly contrast itself with search_moments or get_moment, so sibling differentiation is left to inference.

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?

"from every show or only one" implies when the optional show filter is appropriate, but there is no explicit statement of when to call this versus search_moments (query-driven) or get_moment (single lookup). Usage is inferable 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?

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds real behavior beyond that: what fields the query is matched against, that results are relevance-ordered, that 'company' matches both speakers from it and discussion of it, and exactly which fields come back per result.

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 dense sentences that front-load the core action and matching rules, then filters, then return shape. Nearly every clause carries information, though the phrase 'the moments Real Alpha cut from long interviews' is slightly indirect framing before the actionable part.

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?

With no output schema, the description compensates by enumerating result fields (speaker, role, show, date, quote, citation page). Combined with matching semantics, filter dimensions, and ranking, an agent has everything needed to call it and interpret 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?

Schema description coverage is 100%, so every parameter including defaults and limits is already documented; that establishes a baseline of 3. The description restates the filter dimensions ('who is talking', 'a company', 'a show', 'how recent') and clarifies the dual semantics of company matching, but adds no syntax, format, or default details beyond the schema.

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 ('Search the moments Real Alpha cut from long interviews') and describes the matching target and ranking ('best match first'). It is clearly a broad keyword search over moments, but it never names or distinguishes its closest siblings ('what_did_they_say', 'latest_moments', 'answers_to'), so an agent must infer the boundary.

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?

Usage is implied by the description of filters and relevance ranking (use this when you want to find moments by topic, speaker, company, show, or recency). There is no explicit when-to-use/when-not-to-use guidance and no routing to alternatives such as 'what_did_they_say' or 'latest_moments'.

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 annotations already declare readOnly, idempotent, non-destructive, and closed-world behavior. The description adds useful behavioral detail beyond that: the return contains timed transcript lines, optional speaker information, and a link that plays the episode from that point, with a roughly one-minute scope.

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 front-loaded with what the tool returns and is only two sentences. The first sentence is dense with several clauses, but every clause contributes useful domain detail rather than repeating schema or annotations.

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 a simple two-parameter read-only tool with full schema coverage and no output schema, the description supplies the missing pieces: expected return content, temporal scope, intended use, and how to derive the parameter values from a moment URL.

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 meaningful context by tying both parameters to a moment's episode_url, clarifying where the video id and second values come from in practice.

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 a specific verb and resource: it returns about a minute of an episode transcript starting at a given second, including timed lines, speaker labels when available, and a playback link. It does not explicitly differentiate itself from nearby siblings such as what_did_they_say or get_moment, so it falls short of a 5.

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 gives a clear intended use case: checking the exact words around a moment's quote, and explains that a moment's episode_url contains the video id and second needed to call it. It does not state when not to use it or name alternative tools, so it is strong but not exhaustive.

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 critical context beyond the annotations and schema by defining the scope of the data ('Real Alpha cut'), the sorting behavior ('newest first'), and what supplementary metadata will be provided ('who they are and links their page').

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 highly information dense, packing scope, ordering, filtering logic, and output context into a single sentence without becoming confusing or overly wordy.

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 high coverage of the input schema and strong read-only annotations, the description fully conveys all necessary operational constraints (scope, ordering, retrieval method) for the agent to use the tool correctly and confidently.

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?

While the schema is comprehensive, the description adds semantic weight by specifying that the `topic` parameter can be 'a topic or a few words' and reinforces the default behavior for the `limit` parameter (10 if not given). This adds necessary interpretive value.

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 defines the tool's function as retrieving specific statements or 'moments' from interviews within the 'Real Alpha cut.' It is highly specific and immediately identifies the data source.

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 clear usage guidance by explaining the result ordering ('newest first') and the matching criteria ('every moment where they are talking, or only those on a topic, best match first'). This helps the agent contextualize when and how the data will be retrieved.

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