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

With no annotations provided, the description carries the full burden of explaining behavior. It states that a document is fetched, which suggests a read-only operation, but it does not disclose output format, potential absence of results, error behavior, or any side effects. It is minimally informative but lacks behavioral depth.

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, front-loaded sentence with no redundant phrasing. Every word contributes meaning, and it is compact enough 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?

For a simple tool with one required parameter and a high-coverage schema, the description is largely sufficient: it states what the tool fetches and how to identify the target. It leaves minor open questions about the response shape, but the lack of an output schema and low tool complexity keep this from being a serious gap.

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 the id parameter well. The description reinforces the id as 'a search result id' and adds the concept of full prediction document, but it does not significantly expand on parameter semantics 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?

The description names a specific verb ('Fetch'), a concrete resource ('full prediction document'), and a clear input context ('search result id'). It distinguishes the tool from broad search and list-like getters by emphasizing that it retrieves the full document for one ID, though it does not explicitly compare against a sibling.

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 should be used after obtaining a search result id (e.g., pred:123), so the usage context is inferable. However, it does not explicitly say when to prefer this over get_predictions, get_bet, or search, 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?

No annotations are provided, so the description carries the full burden of behavioral transparency. It only states the resource and the API key requirement; it does not disclose whether the operation is read-only, what 'recently detected' means operationally, whether there are rate limits, or what kind of response the caller should expect.

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 very short and front-loads the core concept. However, it is stylistically truncated with 'PRO:' and relies on the tool name to complete the meaning, which slightly reduces clarity.

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?

The tool is small and the schema covers its parameters well, so this is minimally viable. But with no output schema, no annotations, and many sibling tools, the description should clarify the return shape, the meaning of 'live', and when to choose this tool over the alternatives.

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 of 3 applies. The schema already documents api_key and within_seconds with descriptions and a default; the tool description adds nothing meaningful about the parameters beyond repeating the api_key requirement.

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 the resource clearly: 'recently detected live arbitrage opportunities.' However, it is not a full verb statement about what the tool does, and it relies on the tool name 'get_arbs' to convey the retrieval action. It also does not explicitly distinguish it from siblings such as get_value_bets or get_predictions.

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 like get_value_bets, get_predictions, or fetch. The 'PRO:' label and 'Requires api_key' hint at access requirements, but no conditions, alternatives, or exclusions are provided.

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 behavioral burden. It clarifies what the balance represents, but it does not state that this is a read-only lookup, whether any side effects occur, what identity the agent_id relates to, or what the response 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?

The description is a single, front-loaded sentence with no filler. The parenthetical 'not cash' earns its place by preventing a common misunderstanding.

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 one required parameter and no output schema, this description is close but incomplete: the caller must infer whose balance is returned and how to map that to agent_id. The description alone does not guarantee a correct 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%, so the description should compensate by explaining the required agent_id parameter. It never mentions agent_id directly; 'Your' hints that a specific agent's balance is being retrieved, but the connection to the agent_id parameter remains implicit.

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 identifies the resource as the paper-points balance and explicitly distinguishes it from a cash balance. It lacks an explicit verb such as 'returns' or 'fetches', but the get_ name and the resource noun make the tool's purpose clear enough to separate from sibling get_* tools.

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: call this tool when you need a paper-points balance rather than cash. It gives no explicit when-to-use or when-not-to-use guidance, and it does not name any sibling get_* tool as the alternative for related concepts.

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 only says that one paper bet is returned; it does not explain whether this is read-only, how missing IDs are handled, what response shape to expect, or whether any side effects occur.

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?

At four words, the definition is extremely terse, front-loaded, and contains no filler. Every word contributes meaning about the operation's scope.

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 getter this brevity is understandable, but without annotations, an output schema, or usage guidance the description still leaves important gaps: return format, error behavior, and explicit distinction from sibling getters.

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 has 0% description coverage and the description adds almost nothing about bet_id beyond saying 'by id.' The property name is self-explanatory, but the description does not compensate for the missing schema-level semantics or explain how the ID is used.

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 that this tool returns a single paper bet identified by ID, which distinguishes it from list-oriented siblings like get_my_bets and get_arbs. It lacks an explicit verb, but the resource and scope ('one paper bet by id') make the purpose reasonably clear.

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 about when to use this tool versus alternatives such as get_my_bets, fetch, or search. The description implies that you need a known bet_id, but it never explains the conditions or prerequisites for choosing this 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?

No annotations are provided, so the description should carry behavioral meaning, but it only defines the object. It does not state whether this operation is read-only, what the API returns, what CLV represents, or any rate/scope information. The agent has almost no insight into the tool's actual behavior.

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 text is extremely short, but this is under-specification rather than conciseness. It does not provide enough actionable information for an agent to rely on, so the brevity is not effective.

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 this is a simple tool with a single parameter and no output schema, the description omits key context like the meaning of CLV, the return shape, and any operational considerations. Overall, the documentation is minimal and offers little more than the tool name alone.

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. The phrase 'contributor's' implicitly maps to the contributor_id parameter, but it adds no extra semantics such as ID format, constraints, or examples. The schema only stores type string, so the tool remains essentially undocumented.

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 'A contributor's CLV record' identifies a resource but lacks a clear verb. It is not a pure tautology, but CLV is left undefined and the tool's behavior (retrieving/creating/updating/) is ambiguous. It does not clearly distinguish this from sibling tools like get_value_bets or get_track_record.

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 any alternative. No context is given for which scenarios require fetching a contributor's CLV record, and no sibling tools are mentioned or excluded.

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, and it does offer non-obvious context: CLV means reputation points, not cash. However, it does not state ordering direction, whether the leaderboard is global/filtered, pagination/limits, or what data is returned, leaving several operational behaviors to inference.

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 tight sentence with no filler; the primary subject and caveat are both front-loaded. It could earn a higher score if it also gave one line of usage context or return-format detail, but as pure conciseness it wastes no words.

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?

This is a simple zero-parameter tool, so much can be inferred from the name and the word 'leaderboard.' But there is no output schema or annotation fallback, and the description does not describe the returned entries, count, ordering, or any limits, so an agent must guess at the response contract.

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 there is no parameter semantic burden on the description. The parenthetical about what CLV represents gives useful conceptual context for interpreting any future or returned data, but no additional parameter documentation is needed.

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 ('contributors'), the ranking criterion ('CLV'), and adds a useful disambiguation ('reputation points, not cash'). It is obvious that this returns a ranked leaderboard, distinguishing it from per-contributor tools like get_contributor_score, though that sibling is not explicitly named.

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 statement about when to use this tool versus alternatives. The description implies a leaderboard lookup through the phrase 'ranked by CLV,' but it does not say when not to use it, nor does it reference sibling tools like get_contributor_score or get_track_record for individual metrics.

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 disclosure burden. It discloses the scope ('your paper bets') and ordering ('newest first'), but does not mention response shape, pagination, error behavior, or whether it is strictly read-only.

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 at six words and contains no filler. It front-loads the useful ordering behavior, though it may be too minimalist for full completeness.

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 list getter with no output schema, the description covers the core idea and sort order but leaves out agent_id semantics and response structure. It is minimally adequate but not richly 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?

The schema provides only the parameter name 'agent_id' with type string and 0% description coverage. The description gives only an indirect link via 'your', which may help infer that the parameter identifies the betting agent, but it does not explicitly explain how to use agent_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?

The description clearly identifies the resource as the user's paper bets and specifies ordering as newest first. It does not explicitly state the verb 'get', relying on the tool name, and does not fully distinguish itself from get_bet, but the plural 'bets' and 'my' make the purpose reasonably clear.

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 no explicit guidance on when to use this tool versus siblings such as get_bet or get_value_bets. There are no exclusions, alternatives, or contextual conditions provided.

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 are absent, so the description carries the full behavioral burden. It does disclose the output essence (upcoming predictions with probabilities and edge), but it does not mention ordering, pagination, response shape, or any operational caveats. It is adequate but not richly 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 extremely concise at one sentence with no filler, and it front-loads the main value proposition. It loses a point because it is phrased as a fragment rather than a clear action statement, and the mention of 'probabilities and model-vs-market edge' may be a bit compressed.

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?

The tool is simple with two optional parameters, and the schema already documents those. Yet the description leaves some context unclear: it does not explain how predictions relate to 'value bets', how league filtering works, or how results are ordered. It is adequate for a basic listing tool, but not fully 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 coverage is 100% and both parameters have their own descriptions (limit and league filter). The tool description adds no parameter information, but the schema already explains the semantics, so the baseline score of 3 applies.

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 identifies the main content of the tool: upcoming predictions, probabilities, and model-vs-market edge. It is clear what resource the tool is about, though it lacks an explicit verb (Get/List) and does not distinguish itself from sibling tools like get_value_bets.

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 instead of similar siblings such as get_value_bets, get_arbs, or fetch. The description implies a read-only listing of upcoming predictions, but never states exclusions or alternative selection criteria.

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 burden of behavioral disclosure. It only mentions 'honest' and 'CLV' without explaining data source, freshness, access requirements, or what makes this record 'honest'. The 'get' verb implies read-only behavior, but that is not stated and the description adds little beyond the name.

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 short, but it is an ungrammatical fragment using plus signs to join unexplained terms like 'CLV'. It sacrifices clarity for brevity and the opaque wording keeps it from being genuinely concise or effectively 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?

With no output schema, no parameters, and no annotations, the description should clearly explain what is returned and how to interpret the result. 'Honest settled record + high-confidence subset + CLV' is an unsupported high-level listing that leaves key terms ambiguous, so the agent still lacks enough context to invoke it 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?

The input schema has zero parameters, so there is nothing to document and the description cannot be faulted for missing parameter details. This is the baseline case where parameters cannot be clarified in prose.

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 hints at the tool's purpose by listing 'Honest settled record', 'high-confidence subset', and 'CLV', but it lacks an explicit verb and never clearly states what get_track_record does. It is more than a tautology because it mentions specific components, but an agent would have to infer that these are parts of a returned track record.

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 about when to use this tool versus siblings like get_bets, get_my_bets, or get_predictions. The description does not mention any alternative tools, preconditions, or context, leaving the agent to guess the appropriate use case.

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 for behavior. It communicates that the tool returns upcoming value-bet picks and demands a PRO api_key, implying a gated read operation. However, it does not disclose failure behavior, response format, or any rate-limiting or subscription subtleties. The basics are present but not the full picture.

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 compact and front-loaded: the purpose and PRO nature are named in the first clause, and the auth requirement is stated in the second. There is slight redundancy in repeating 'Requires api_key' when the schema already marks it required, but no excess or filler drags the description down.

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 only one required parameter and no output schema, this description provides enough context: it names the domain ('value bets'), the relative timing ('upcoming'), the advantage signal ('model-vs-market edge'), and the required credential. The absence of an explicit 'returns a list of picks' phrase is a minor gap given the name and description.

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 api_key parameter already has a helpful description including where to obtain a key. The tool description merely repeats 'Requires api_key', adding no semantic meaning beyond what the schema already provides. Baseline 3 is appropriate here.

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 the resource clearly: 'PRO: upcoming picks with positive model-vs-market edge.' This goes beyond the generic tool name and distinguishes it from siblings like get_arbs or get_predictions. The verb is implied by the name, but the kind of data returned is explicit and specific.

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 only usage guidance is the prerequisite 'Requires api_key', which is already obvious from the schema. There is no when-to-use guidance, no contrast with sibling tools like get_arbs or get_predictions, and no statement about conditions like PRO access beyond the label. The agent is left to infer when to select this over 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?

No annotations exist, so the description must fully carry behavioral context. It mentions 'paper bet' and 'points only,' which suggests stakes and gamification, but it does not reveal side effects like point deduction, whether existing bets are replaced, or any prerequisites.

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 one sentence, front-loads the action and purpose, and avoids filler. 'Against our prediction' and 'points only' are useful qualifiers that justify the sentence.

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 four required parameters, no output schema, and no annotations, a one-sentence description is insufficient. Important context is missing: the meaning of 'side' relative to the opponent's prediction, prerequisites, return format, restrictions (e.g., staking limits), and what 'points only' is in a practical sense.

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 only 25%; only stake_points has a schema comment. The description adds no detail about agent_id, match_id, or the side specified as home/draw/away—omitting how the enum maps to 'against the prediction.' Since the schema doesn't cover these and the description doesn't compensate, parameter meaning is under-specified.

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 and resource: 'place a paper bet' against the agent's prediction. It clearly distinguishes this from other tools by adding 'points only' and 'against our agent's prediction,' which both state the fundamental scope.

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 given about when this tool should be used instead of siblings. The description implies the action but does not differentiate it from 'submit_prediction' or any other related tool, leaving the agent to infer the right context.

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 burden. It usefully reveals that results are limited to {id,title,url} and that the full prediction lives behind fetch. However, it does not mention behavior for missing/empty queries, result limits, errors, or visibility constraints.

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 sentences with no filler. The core action appears first, and the key output and follow-up behavior appear in the second sentence.

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 search tool with no output schema, the description is nearly complete: it gives the purpose, result shape, and next step. The main gap is that the schema does not require query, and the description doesn't clarify what an omitted query returns.

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%, with the query parameter already described as the team or league to search. The description adds no meaningful parameter semantics beyond what the schema provides.

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 action and resource: 'Search upcoming match predictions' and gives the output shape as {id,title,url}. It also points to fetch for full predictions, which helps differentiate, though it doesn't contrast with similarly named siblings like get_predictions.

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 to 'use fetch(id) for the full prediction,' so an agent knows search returns previews and when to switch tools. It does not mention alternatives like get_predictions or when to avoid search, so some routing guidance is missing.

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 of explaining behavior. It discloses that a prediction is submitted and will be scored, but it says nothing about side effects, duplicate submissions, mutability, required probability constraints, or whether the operation is reversible.

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 one short sentence with no filler and front-loads the core action. It is efficient, though slightly too sparse to fully support agent decision-making.

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 five required parameters, no output schema, no annotations, and 0% schema description coverage, the definition is not complete. An agent can identify the general purpose but cannot determine validation rules, expected behavior, or how to handle 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?

Schema description coverage is 0%, yet the description adds no information about contributor_id, match_id, or the p_home/p_draw/p_away fields. The phrase 'prediction' implies the p_* fields are probabilities, but no ranges, summation rules, or formats are 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?

The description specifies a concrete action ('Submit a prediction') and its purpose ('scored by CLV'), which distinguishes the tool from purely informational siblings like get_predictions. It does not explicitly differentiate from place_bet or mention alternatives, but the core intent is clear.

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 about when to use this tool versus alternatives such as place_bet or get_predictions. The description does not mention prerequisites, restrictions on submission window, or how this prediction relates to CLV scoring.

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