Mlb Stats

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It explains that the tool routes across many sources, picks the right tool, fills arguments, and returns a result. However, it does not discuss potential latency, rate limits, or error conditions, leaving some behavioral aspects opaque.

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 moderately concise and front-loaded with the core purpose. Every sentence adds value, including examples and source coverage. It could be slightly shorter, but overall it is 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?

Given the tool has only one parameter and no output schema, the description provides enough context for an AI agent to understand its purpose, usage, and scope. It explains the routing behavior and gives examples, making it sufficiently complete for its 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 input schema has one parameter ('question') with a clear description, and the tool description adds substantial meaning by listing example questions and the range of sources. This goes beyond the schema to clarify what kinds of natural-language queries are 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 it answers natural-language questions by picking the right data source. It provides specific examples and distinguishes itself from sibling tools by indicating it routes across many sources, saving the user from having to choose a specific tool.

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 states 'Use when a user asks... and you don't want to figure out which Pipeworx pack/tool to call,' providing clear when-to-use guidance. It lists example queries but does not explicitly mention when not to use this tool or alternatives, though the context implies it is a general fallback.

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 present, so description must cover behavioral traits. It lists data pulled and return type ('paired data + citation URIs'), but omits potential failure modes, data freshness, or authentication needs. Adequate but not fully 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?

Well-structured: first sentence introduces purpose, followed by usage triggers, then per-type details. A bit lengthy but each sentence earns its place. Could be slightly more concise.

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 moderate complexity (two entity types, multiple fields), the description sufficiently explains inputs, data sources, and return structure. Lacks details on output format but adequate for selection and invocation.

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 covers 100% of parameters. Description adds value by clarifying the enum values ('company' or 'drug') and providing example values for the array (tickers vs drug names), which aids correct invocation.

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 compares 2–5 companies or drugs side by side, with specific data sources and fields for each type. It distinguishes itself from siblings like entity_profile by offering batch comparison.

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?

Provides explicit trigger phrases ('compare X and Y', 'X vs Y', etc.) and states it replaces 8–15 sequential calls, implying efficiency. Lacks explicit when-not-to-use or alternative tools.

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 convey behavioral traits. It discloses that the tool 'Returns the top-N most relevant tools with names + descriptions', indicating a read-only operation. However, it does not elaborate on how relevance is determined, potential rate limits, or any other side effects. The basic behavior is transparent but not deeply detailed.

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 paragraph that packs essential information efficiently, front-loading the purpose and usage. It is concise without being overly terse, though a slightly more structured format (e.g., bullet points for domains) could improve scanability.

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 (2 parameters, no output schema), the description is complete: it explains what the tool does, when to use it, and what it returns. It provides a comprehensive list of example domains, making the tool's scope clear. No major gaps are evident.

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 100% description coverage, so the baseline is 3. The description does not add significant meaning beyond the schema for the two parameters ('query' and 'limit'); it merely echoes their purposes without providing additional examples or constraints.

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 ('Find tools') and identifies the resource ('tools by describing the data or task'). It distinguishes itself from sibling tools by positioning as a discovery/search tool and lists numerous domains, making its purpose unmistakably 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 explicitly states when to use it: 'Use when you need to browse, search, look up, or discover what tools exist' and provides a strategic directive: 'Call this FIRST when you have many tools available and want to see the option set'. This offers clear context and alternatives (e.g., not for specific single-answer tasks).

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 provided, but the description lists exactly what is returned (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. Does not address rate limits, authentication, or data recency, but provides substantial behavioral context for a read-only aggregation 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?

Starts with purpose, then use cases, output summary, and input format. All sentences are informative, but the paragraph is slightly dense. Could be split into bullet points for readability, but it is still concise for the amount of information.

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?

Covers input format, output categories, and use cases. No output schema exists, so description must explain return values, which it does adequately. Missing error scenarios or pagination details, but overall complete for a profile 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?

Schema coverage is 100% with descriptions. Description adds meaning: explains 'type' is currently limited to 'company', and 'value' accepts ticker or zero-padded CIK not names. Also provides format example and redirects to resolve_entity for name inputs.

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?

Clearly states 'Get everything about a company in one call', specifying verb and resource. Lists concrete use cases and output types. Does not explicitly differentiate from sibling tools like 'compare_entities' but implies it is a comprehensive aggregation.

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?

Provides explicit examples of triggering user queries ('tell me about X', 'research Microsoft'). States when not to use it ('Names not supported — use resolve_entity first'). Also warns that only 'company' type is supported. This gives clear when/when-not guidance with an alternative.

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?

Description clearly indicates destructive action ('delete'). No annotations provided, so description carries full burden. It could mention irreversibility or effects on recall, but for a simple delete it is sufficiently 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?

Two sentences, each earning its place: first states purpose, second gives usage guidelines. No fluff.

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 delete tool with no output schema, the description covers purpose, usage, and relationship to siblings. 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?

Schema coverage is 100% with key parameter described. Description does not add additional meaning beyond what schema provides, but restates the purpose of the key.

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?

Clearly states 'delete a previously stored memory by key' with specific verb and resource. Distinguishes from siblings like remember and recall, which store and retrieve memories respectively.

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?

Explicitly describes when to use: 'when context is stale, the task is done, or you want to clear sensitive data.' Also mentions pairing with remember and recall, providing clear usage 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 must cover behavioral traits. It discloses it returns a 'full box score' but does not detail update frequency for in-progress games, caching behavior, error handling, or authentication requirements.

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 concise sentence with no extraneous words. It efficiently conveys the purpose and key parameter.

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 simple input (one parameter) and no output schema, the description covers the essentials. It could mention typical fields returned, but overall it is sufficient for basic 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?

Schema coverage is 100% with a description for 'game_pk' as 'MLB game primary key'. The description adds no further semantic value beyond what the schema provides, so 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 states it provides the full box score for a game (completed or in-progress) identified by gamePk. It uses specific verb 'get' and resource 'box score', and distinguishes from siblings like 'get_game_feed' which likely provides more detailed play-by-play 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?

The description implies when to use: for completed or in-progress games, by supplying gamePk. It does not explicitly state when not to use or name alternatives, but given context, no direct alternative exists for box scores.

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 empty, so description should disclose behavior. 'Live' implies current state but fails to mention polling, data freshness, or whether it returns all events. Lacks behavioral details.

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?

Extremely concise (3 words) but at the expense of informativeness. Not as helpful as it could be.

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 one required parameter and no output schema, the description should explain the parameter and expected return. It does neither, leaving gaps.

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% and description does not explain the only parameter 'game_pk'. No added value 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 clearly states 'Live game feed with play-by-play', which is a specific verb+resource combination. It differentiates from siblings like get_boxscore (summary) and schedule (list of games).

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 guidance on when to use this tool vs alternatives. Does not mention context like real-time usage or prerequisites.

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 should disclose behavioral traits. It only states what is returned but not any side effects, rate limits, or authentication needs. Minimal transparency.

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 just 5 words, the description is too terse. While concise, it sacrifices critical information about the output and usage, being more under-specified than 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 single-parameter tool without output schema or annotations, the description fails to explain what the profile contains, format, or any constraints. Lacks completeness for effective agent decision-making.

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% with player_id described as 'MLB player id'. The description adds no extra meaning beyond the schema, merely restating the purpose. Baseline 3 adjusted down due to lack of added 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 'Player profile by ID.' clearly states the tool retrieves a player profile using an ID. It distinguishes from siblings like player_stats (statistics) and entity_profile (generic entity), though it could be more explicit about the profile 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 guidance on when to use this tool versus alternatives such as entity_profile or player_stats. An agent would need to infer usage from context, which is insufficient.

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 provided; description only lists output types without disclosing behavioral traits such as idempotency, authentication needs, or whether it affects data. Minimal 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?

Single sentence is concise but overly terse. Lacks structure or additional context that could improve usability without increasing length significantly.

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 output schema and one parameter, the description should at least hint at the return format or key fields. 'Team profile + venue info' is vague and insufficient for an agent to know what to expect.

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% with a clear parameter description ('MLB team id'). Description adds no extra meaning beyond what's in the schema, so 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?

Description states the tool returns 'team profile + venue info', which is clear and specific. It distinguishes from siblings like 'team_roster' (roster vs profile) and 'entity_profile' (generic vs team-specific). Verb is implied by name 'get_team'.

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 guidance on when to use this tool vs alternatives like 'entity_profile' or 'team_roster'. No context about prerequisites or limitations.

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?

Even without annotations, the description fully discloses behavioral traits: rate-limited to 5 per identifier per day, free usage, no impact on quota, and that the team reads digests daily. There is no destructive behavior, and the tool is safe. This exceeds the minimum required transparency.

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 concise (two paragraphs) and well-structured. It starts with the core purpose, then provides usage guidelines, and ends with constraints. Every sentence adds value without redundancy. The length is appropriate for the tool's simplicity.

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 straightforward nature (feedback submission), the description covers all necessary aspects: what it does, when to use it, parameters (with added context), constraints (rate limits, free), and behavior (team reads digests). No output schema is needed, and the description is fully adequate for an AI agent to invoke 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?

The input schema has 100% description coverage, so the baseline is 3. However, the description adds valuable semantic context beyond the schema: it explains the 'type' enum values in more detail, advises to describe issues in terms of Pipeworx tools/packs, and clarifies not to paste prompts. This adds meaningful guidance for parameter usage.

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: to tell the Pipeworx team about issues (bug, feature, data_gap, praise). It distinguishes itself from other tools on the server, which are data retrieval or utility tools, so there is no ambiguity. The verb 'tell' and specific resource 'Pipeworx team' make it highly 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 description provides explicit guidance on when to use the tool (bug, feature, data_gap, praise) and what not to do ('don't paste the end-user's prompt'). It also mentions rate limits and that it's free and doesn't count against tool-call quota, setting clear expectations for when this tool should be invoked.

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 bear the full burden of behavioral disclosure. It only states the output type ('stats') without detailing data freshness, required permissions, or what the response contains (e.g., which metrics).

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 short (one sentence), which is concise but potentially under-specified. It earns its place but lacks structure or key details.

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 4 parameters, 1 required, and no output schema, the description should clarify how parameters interact (e.g., season+carrer) or default values. It does not, leaving the agent without essential usage context.

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 schema description coverage is 75% (3 of 4 params described), the description adds no extra meaning beyond the schema—e.g., it doesn't explain the 'group' values or that 'season' must be YYYY. The description is too vague to supplement 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 clearly states the tool retrieves career or season stats for a player, which identifies the verb (get/retrieve) and resource (stats). It implicitly distinguishes from sibling tools like get_player (profile) and get_boxscore (per-game) by focusing on aggregated stats.

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 guidance is provided on when to use this tool versus alternatives like get_boxscore or schedule. There is no mention of prerequisites, limitations, or when not to use it.

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 burden of behavioral disclosure. It indicates the tool is read-only (retrieve/list) and scoped, but lacks explicit statements about error handling, rate limits, or the consequence of missing keys.

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 concise and well-structured, with three sentences that cover purpose, usage context, and pairing with sibling tools. No redundant information is present.

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 simple nature of the tool (retrieve/list) and no output schema, the description adequately explains scope and pairing. It lacks details on return format or error states, but the core functionality is well-covered.

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 sole parameter 'key' is described completely in both schema and description. The description adds valuable context: omitting the key lists all saved memories, which enriches the meaning beyond the schema definition.

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's function: retrieve a previously saved value or list all saved keys. It distinguishes itself from siblings by explicitly referencing 'remember' and 'forget' tools, which are present in the sibling list.

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 explicit use cases (looking up stored context like tickers or addresses) and notes scoping to the agent's identifier, guiding appropriate use. However, it does not explicitly state when not to use this tool or name alternatives beyond the paired tools.

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 that the tool fans out to three sources and returns structured changes with citation URIs. However, it does not mention error handling, rate limits, or what happens if no changes are found. The description is adequate but could be more thorough.

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 concise (about 4 sentences) and front-loads the purpose. It efficiently conveys the tool's functionality and usage examples without unnecessary verbosity. Minor redundancy could be trimmed, but overall it is 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?

Given the tool's complexity (parallel fan-out to multiple sources) and lack of output schema, the description adequately explains what is returned (structured changes, total_changes, citation URIs). It covers the key data sources and input parameter details. Some missing details about error handling or edge cases, but it is sufficiently complete for typical 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 covers 100% of parameters. The description adds value by explaining accepted date formats (ISO or relative shorthand) and clarifying that 'value' can be a ticker or CIK. It also notes that 'type' currently only supports 'company'. This enriches the schema definitions.

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: retrieving recent changes for a company. It provides example queries and specifies the data sources (SEC EDGAR, GDELT, USPTO). The purpose is distinct from sibling tools, which focus on profiles, comparisons, or sports 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?

The description includes explicit example user queries ('what's happening with X?', 'any updates on Y?') and describes the tool's behavior (fans out to multiple sources). It does not explicitly state when not to use the tool or compare it to alternatives, but the usage context is clear enough for an AI agent.

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 provided, but description discloses scoping by identifier, persistence differences between authenticated and anonymous sessions, and 24-hour retention for anonymous users. Does not mention behavior on overwriting existing keys.

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?

Single paragraph that front-loads core purpose and usage. Contains six sentences, each adding unique information without 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 only two parameters, the description fully covers input semantics, use cases, scoping, retention, and relationship to sibling tools, leaving no meaningful gaps.

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% with clear descriptions for both parameters. The description adds value by providing concrete key examples ('subject_property', 'target_ticker') and explaining that 'value' can be any text.

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?

Description uses strong verb-object pair ('Save data') and clearly distinguishes from siblings 'recall' and 'forget' by mentioning them specifically.

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?

Explicitly states when to use ('discover something worth carrying forward'), provides concrete examples, and instructs to pair with 'recall' and 'forget' for complementary actions.

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 provided, so description carries full burden. It states it returns IDs and citation URIs, implying a read-only operation. However, it doesn't explicitly confirm no side effects or disclose limitations like rate limits or required permissions.

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?

Description is a single focused paragraph, front-loaded with main action. Every sentence adds value: purpose, when to use, examples, output format, and relationship to other tools. No unnecessary 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?

Given no output schema and no annotations, the description covers input, output examples, and usage context well. It explains the ID systems returned and provides concrete examples. Lacks details on error handling or edge cases, but adequate for a 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?

Schema coverage is 100% with basic descriptions, but the tool description adds significant meaning: examples mapping company names to tickers/CIKs and drugs to RxCUI, plus explanation of ID systems. This goes beyond the schema's enum and string types.

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?

Description clearly states the tool resolves names to official identifiers (CIK, ticker, RxCUI, LEI). It distinguishes itself from siblings by saying 'Use this BEFORE calling other tools that need official identifiers' and claims it replaces 2–3 lookup calls.

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?

Explicitly tells when to use: when a user mentions a name and you need an official identifier. It gives examples and instructs to use before other tools. Does not explicitly state when not to use or provide alternatives, but context makes it clear.

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 present, and the description does not disclose any behavioral traits such as read-only or destructive nature, permissions required, side effects, or output format. The burden is entirely on the description, which only states the tool's subject.

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 with no filler, front-loaded with 'Game schedule'. It is concise but sacrifices completeness for brevity.

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 6 optional parameters and no output schema, the description is insufficient. It does not explain what the tool returns (e.g., a list of games, dates, scores), nor does it provide context about typical usage or constraints. Sibling tools suggest more detailed outputs, but schedule's output remains unclear.

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% (all 6 parameters have descriptions). The description adds no new meaning beyond summarizing a few parameters; it mentions filtering by date (YYYY-MM-DD), full season, or specific team, which is already present in the schema. Baseline 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 'Game schedule' is a noun phrase without a verb; it does not explicitly state an action like 'Retrieve schedule'. However, it indicates the resource and filtering options, making the purpose understandable but not precise. It does not distinguish from sibling tools like get_game_feed or get_boxscore.

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 filtering options (by date, season, team) but gives no guidance on when to use this tool versus alternatives, no when-not-to-use conditions, and no mention of alternative tools.

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 bears full responsibility for disclosing behavioral traits. It does not state whether the tool is read-only, requires authentication, or has any side effects. The agent is left to infer that it is likely a read operation, which is insufficient.

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 (one sentence) and front-loaded. However, it is a noun phrase rather than a complete sentence, which slightly reduces clarity. It could be restructured with a verb for better readability.

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 lack of output schema and annotations, the description should provide more context about the return format, pagination, or data structure. It does not explain what the standings object contains or how results are organized, leaving the agent with limited information.

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 100% description coverage, so each parameter is already documented. The description adds no additional meaning beyond summarizing that filters are by league/division, season, or date. 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 communicates that the tool provides standings data, filtered by league/division, season, or date. It differentiates from sibling tools like 'schedule' or 'get_boxscore' by specifying the type of data returned. However, it lacks an explicit action verb like 'Get' or 'Retrieve', which would improve clarity.

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 no guidance on when to use this tool versus alternatives, such as when to choose 'standings' over 'schedule' or 'get_game_feed'. There is no mention of prerequisites, typical scenarios, or exclusion 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?

Annotations are empty, so the description should disclose behavioral traits. It only states what the tool returns ('Players on a team') and does not mention that it is read-only, any authorization needs, or other side effects. The description lacks transparency beyond the basic output.

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 with two short sentences, no unnecessary words, and front-loads the core purpose. Every word 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?

Given no output schema and empty annotations, the description is too brief. It does not describe the return format, pagination, or any additional context about the players data (e.g., fields returned, ordering). The agent may need more information to use the tool effectively.

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 67% (season and roster_type have descriptions, team_id does not). The description adds value by explaining the roster_type parameter with examples ('active for current 26-man, =40Man for full org'), which is helpful beyond the schema's enum list. However, it does not add meaning for team_id or season.

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 'Players on a team' which clearly indicates the resource (team roster) but lacks an explicit verb like 'Get' or 'List'. However, it is distinct from sibling tools like get_player or get_team, and the example usage clarifies the purpose.

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 explicit guidance on using roster_type values: 'Use roster_type=active for current 26-man, =40Man for full org.' This tells the agent when to choose each roster type. No mention of when not to use, but given the context, it is sufficient.

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 that the tool returns a verdict, extracted form, actual value with citation, and percent delta. It also notes it replaces multiple sequential calls. It does not cover auth or rate limits, but those are not critical for a read-like fact-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 two paragraphs, each sentence adds value. It opens with the purpose, then usage, then output details. No wasted 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?

For a single-parameter tool with no output schema, the description fully explains input format, output structure, and domain constraints. It is self-contained and complete for an agent to understand when and how to use it.

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 'claim' parameter, which already describes it as a string. The description adds a clarifying example, which is helpful but does not significantly extend beyond schema semantics. 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 states the tool fact-checks a natural-language claim against authoritative sources, specifically for company-financial claims. It uses a specific verb ('fact-check') and resource ('authoritative sources'), and distinguishes from sibling tools by its unique function.

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 explicit use cases ('when an agent needs to check whether something a user said is true') with example phrasing. It limits scope to company-financial claims but does not explicitly state when not to use it; however, sibling tools are different enough that confusion is unlikely.

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