Trakt

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

With no annotations, the description carries full burden. It discloses the routing behavior, argument filling, and result return. However, it omits details like rate limits, error cases, or timeout behaviors, which would improve 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 front-loaded with the main purpose and includes helpful examples and a source list. While slightly verbose, every sentence adds value and it remains 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?

Given the tool's complexity (many data sources) and absence of output schema, the description provides sufficient context: purpose, when to use, examples, and source diversity. It could mention return format or limitations, but is complete enough for an agent.

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 only parameter 'question' is fully described in the schema with 'Your question or request in natural language'. The description adds no additional meaning beyond the schema, so a 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 states the tool's purpose: answering natural-language questions by automatically selecting the right data source. It distinguishes itself from sibling tools like 'search' by specifying its routing capability across 300+ sources and giving concrete examples.

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 advises use when a user asks certain types of questions and you don't want to pick the specific tool. It provides examples and lists covered domains, but lacks explicit guidance on 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?

No annotations provided, so the description carries full burden. It discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), return type (paired data + citation URIs), and implies read-only operation. No mention of rate limits or auth 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 well-structured and front-loaded with purpose and usage. Every sentence adds value, no 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, the description adequately explains return values (paired data + URIs) and covers key aspects for each type. Could be more explicit about exact output fields.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds significant meaning beyond schema: it explains what each type fetches (e.g., revenue, net income for company; adverse events for drug) and provides examples for the values parameter.

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 verbs and resources. It implicitly distinguishes from sibling entity_profile which handles single entities.

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 when to use (e.g., 'compare X and Y', 'X vs Y'), and notes it replaces multiple sequential calls. Lacks explicit when-not-to-use instructions, but the context is 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 provided, so description carries full burden. It states it returns 'top-N most relevant tools with names + descriptions,' but does not disclose rate limits, data freshness, or side effects. Since it's a read-only discovery tool, the lack of major behavioral issues keeps it at 3.

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?

Front-loaded with purpose and usage advice. The list of domains (SEC, FDA, etc.) is lengthy but informative. Could be slightly more concise, but every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should compensate. It mentions return format vaguely ('names + descriptions') but not ordering or pagination. For a discovery tool, this is adequate but not fully comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds context for query ('natural language description') and limit ('default 20, max 50'), which is helpful but not essential beyond 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 starts with 'Find tools by describing the data or task,' clearly stating the verb and resource. It distinguishes itself from siblings (which are specific data functions like get_movie, search) by positioning itself as a discovery tool for browsing available options.

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 advises 'Call this FIRST when you have many tools available and want to see the option set' and provides examples of when to use (browse, search, look up). Does not explicitly state when not to use, but context implies it's for exploration, not direct answers.

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 description carries full burden. It details the output: recent filings, revenue/net income/cash, patents, news, LEI with citation URIs. Does not explicitly state side effects, but as a read-oriented tool, it is transparent about what the call returns.

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 relatively long but well-structured: purpose first, then usage examples, then data details. Every sentence adds value; could be slightly tighter but no waste.

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?

No output schema, but description enumerates return elements. Addresses aggregation across multiple sources (SEC, USPTO, GLEIF). Might benefit from mentioning result size limits or pagination, but for an 'everything in one call' tool, it is sufficiently 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?

Input schema covers 100% of parameters with descriptions. The description adds context: value accepts ticker or zero-padded CIK, clarifies that names are unsupported, and notes type enum currently limited to 'company' with future expansion. This adds meaningful guidance beyond schema alone.

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 retrieves comprehensive company data in one call, listing specific use cases and data types (SEC filings, fundamentals, patents, news, LEI). It distinguishes itself from sibling tools by noting it replaces 10+ individual pack 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?

Provides explicit example queries ('tell me about X', 'research Microsoft') and states when not to use it (names not supported, requires ticker/CIK). Directs users to resolve_entity for name resolution, giving clear when-to and when-not-to guidance.

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

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

No annotations provided, so description carries burden. It indicates destructive action ('Delete'), but lacks details on error handling (e.g., key not found). Still, the behavior is clear for a simple deletion.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences: purpose, usage scenario, and sibling tools. No superfluous 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?

Given simplicity (single param, no output schema), description is fully complete: explains operation, when to use, and related tools.

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?

Input schema has 100% coverage with description for 'key'. Description adds 'by key' but does not significantly enhance meaning beyond the schema. Baseline score of 3 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 clearly states 'Delete a previously stored memory by key', with specific verb and resource. It distinguishes from siblings by mentioning pairing with remember and recall.

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: context stale, task done, or clearing sensitive data. Also suggests alternatives by naming remember and recall.

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 does not disclose side effects, authentication needs, rate limits, or behavior with invalid inputs. The optional extended parameter is not mentioned.

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 with no wasted words. It is front-loaded and gets the point across quickly, though it could benefit from a brief additional sentence on usage.

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 retrieval tool with 4 parameters and no output schema, the description is too sparse. It does not mention return format, pagination, or error behavior, leaving agents poorly informed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description adds minimal value beyond the schema. It repeats the required combination but does not explain parameter formats or the extended parameter's effect.

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 a single episode using show ID, season, and episode number. This verb+resource combination differentiates it from siblings like get_movie, get_show, and list_seasons.

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. For example, it does not mention that to get all seasons use list_seasons or to get a show use get_show.

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 describes a read operation (retrieving a record) but does not disclose error handling, authentication needs, rate limits, or whether the tool is idempotent. It is minimally transparent beyond the obvious fetch action.

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 clear front-loading of the main action and a specific usage tip. There is no extraneous text; every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description only hints at what extended=full returns. It does not describe the base record fields, error states, or pagination. For a simple lookup tool, it is adequate but not comprehensive.

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 covers both parameters with descriptions (100% coverage). The description adds value by explaining how to use the 'extended' parameter to get additional fields like plot, runtime, genres, and cast, which goes beyond the schema's generic 'full | metadata | images' hint.

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 a movie record by ID/slug/IMDB ID, and differentiates from sibling tools like get_show and get_episode. It uses a specific verb ('get') and resource ('movie 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?

The description implies use for movie-specific lookups but does not explicitly state when to use this tool versus alternatives (e.g., for shows use get_show). There is no exclusion guidance or when-not-to-use advice.

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 carries full burden. It states the tool retrieves a record, which implies read-only behavior, but lacks details on error handling, required permissions, or rate limits. Adequate for a simple get 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?

Two sentences with no wasted words. The purpose is front-loaded in the first sentence, and the second sentence adds a key usage hint. Perfectly 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?

For a simple tool with two parameters and no output schema, the description covers the core functionality and provides essential guidance for the extended parameter. It lacks details on error behavior but is largely sufficient.

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 for both parameters. The description adds specific value by explaining that extended=full provides runtime, genres, cast, etc., which goes beyond the schema's generic 'full | metadata | images' description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Show record', specifying a verb and resource. It distinguishes itself from sibling tools like get_episode and get_movie by focusing on shows.

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 usage when show details are needed, but does not explicitly mention when not to use or compare with alternatives like search or resolve_entity. No 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 provided, so description must cover behavior. It only states output (seasons + episode counts) but omits whether results are sorted, paginated, or any restrictions (e.g., requires existing show). Minimal behavioral insight.

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 (5 words). Could fit more context (e.g., 'list all seasons for a show') without losing conciseness. Still, 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?

For a simple list tool with 2 parameters and no output schema, the description is adequate but minimal. Missing details like return format, order, or any special behavior (e.g., full vs episodes parameter). Could be slightly more 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 already describes all parameters fully (100% coverage). Description adds no additional meaning about parameters, just restates the overall purpose. 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?

Description clearly states it lists seasons with episode counts for a show. Verb 'list' is implied. Distinct from siblings like get_episode or get_show.

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 (e.g., get_show for details, search to find shows). No context for 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?

With no annotations, the description fully handles transparency. It discloses that feedback is read daily by the team (digests), affects roadmap, has a rate limit, and does not count against tool-call quota. It doesn't specify confirmation or anonymity, but those are minor omissions for a feedback 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 somewhat long but every sentence serves a purpose—defining use cases, setting expectations (rate limit, free), offering formatting advice. It's front-loaded with the primary action and could be slightly tighter, but remains 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?

Given that there is no output schema and the tool is simple (feedback submission), the description covers the essential context: purpose, constraints, and expected content format. It could mention what happens after submission (e.g., a confirmation), but the description is largely 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 input schema has 100% description coverage, so the baseline is 3. The description adds value by elaborating on the 'type' enum and providing guidance on structuring the message (e.g., reference tool/pack slugs, avoid pasting prompts). This pushes it to a 4.

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 explicitly states the tool is for reporting bugs, feature requests, data gaps, or praise, with a clear verb ('tell') and resource ('Pipeworx team'). It distinguishes itself from sibling tools, none of which serve a feedback 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 when-to-use scenarios (bug, feature, data_gap, praise) and what not to do (don't paste end-user prompt). It also mentions a rate limit of 5 per identifier per day and that the tool is free, guiding appropriate usage.

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 disclose behavior. It only states the resource type but omits details like read-only nature, pagination behavior, default values, or whether authentication is needed.

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 at one sentence, no wasted words. However, it could be slightly more structured by explicitly mentioning pagination or defaults, but efficiency is high.

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 list tool with 3 parameters and no output schema, the description is too minimal. It does not mention that 'page' and 'limit' are optional, their defaults, or what the return format looks like, leaving gaps for an agent.

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%; each parameter already has a description. The tool description adds no new meaning beyond restating the resource type, so it meets the baseline without exceeding it.

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 is specific: 'All-time popular movies or shows.' It uses a clear verb (list) and resource (movies/shows) with a scope qualifier ('all-time'), distinguishing it from siblings like 'trending' (time-sensitive) and 'search' (query-based).

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 on when to use this tool versus alternatives such as 'trending' or 'search'. The description lacks context for when to choose 'popular' over other options for entity listing.

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 carry full behavioral disclosure. It explains that the tool retrieves values or lists keys and is scoped to an identifier, but does not mention side effects, authentication, rate limits, or persistence semantics. The information is adequate but not exhaustive.

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 concise four-sentence paragraph, front-loaded with the main action, and every sentence provides distinct information without redundancy or 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?

Given the tool's simplicity and no output schema, the description covers the two modes and scoping. However, it does not describe the return format (e.g., string, JSON), which would enhance completeness. The sibling set is coherent.

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 covers the single parameter 'key' with 100% coverage. The description adds value by stating 'omit the key argument' to list all keys and provides examples of what keys might contain (ticker, address, notes), adding meaning 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 the verb 'Retrieve' and the resource 'value previously saved via remember' and distinguishes from sibling tools 'remember' and 'forget'. It also covers the two use cases: retrieving a specific key or listing all keys.

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 concrete examples of when to use (e.g., to look up a ticker, address, research notes) and mentions scoping by identifier. It suggests pairing with remember and forget, but does not explicitly state when not to use or mention alternatives among the siblings.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool fans out to three external sources in parallel and returns structured changes with a count and citation URIs. However, it does not explicitly state that it is read-only (though implied) or mention any rate limits or authentication requirements. The parallel fan-out behavior is well explained.

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—a few sentences—and front-loaded with the core purpose. Every sentence adds value: purpose, usage context, data sources, parameter hints, and return format. No redundant or filler content.

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 description covers purpose, parameters, data sources, and return structure (structured changes, count, URIs). Since there is no output schema, the return description is essential and adequately provided. The tool is moderately complex with three external sources, and the description gives sufficient context for an agent to use it correctly. A perfect score would require explicit mention of the read-only nature or any potential delays.

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% parameter description coverage, but the description adds significant semantic value: it clarifies that 'type' is restricted to 'company' only, explains the two valid formats for 'since' (ISO date or relative shorthand) with common examples, and specifies that 'value' can be a ticker or zero-padded CIK. This goes beyond the schema's basic descriptions.

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 starts with a clear question summarizing the tool's function: 'What's new with a company in the last N days/months?' It then lists several example user queries that match this purpose. The tool distinguishes itself from siblings like 'entity_profile' (static) and 'trending' (general) by focusing on recent changes from specific data sources (SEC, GDELT, USPTO).

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 the tool: 'Use when a user asks...' followed by a list of concrete query patterns. This makes it easy for an agent to recognize the appropriate context. It also implicitly excludes static queries, which are better served by 'entity_profile', and general trend queries, which go to 'trending'.

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?

Discloses scoping, persistence (24hr for anonymous, persistent for authenticated), and key-value nature. However, does not explicitly state that storing a new value with an existing key overwrites the old value, which is an important behavioral detail.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each adding value: purpose, usage context, and behavioral details. Front-loaded with the main action. 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?

Given simple schema (2 required string params, no output schema), the description covers all needed aspects: purpose, when to use, storage mechanics, and pairing. No missing 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?

Input schema fully documents both parameters with examples. Description adds usage context (e.g., 'resolved ticker') but does not add meaning beyond schema. Baseline 3 for 100% schema coverage.

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 saves data for reuse, with a specific verb ('save') and resource ('data'). It distinguishes from siblings 'recall' and 'forget' by mentioning pairing, establishing its unique role.

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?

Explicit guidance: 'Use when you discover something worth carrying forward' and instructs to pair with 'recall' and 'forget' for complementary operations. Also details scoping by identifier and persistence rules by auth state.

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, but description discloses that it returns IDs plus pipeworx:// citation URIs. Does not mention side effects, but as a read-only lookup, that's acceptable. Minor gap: no error handling info.

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?

Concise and front-loaded: purpose first, then examples, then usage. Every sentence contributes value 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?

For a lookup tool with 2 params and no output schema, description covers input types, usage context, and output content. It also explains how it fits into workflow (use before other tools).

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 100% but description adds examples of valid values (e.g., ticker, CIK, name for company; brand/generic for drug), enhancing meaning beyond the enum and string descriptions.

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 it looks up canonical IDs for companies or drugs, with specific examples (CIK, ticker, RxCUI, LEI). Distinguishes from sibling tools like compare_entities or search.

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 user mentions a name and needs an official identifier. Advises to use BEFORE other tools and notes it replaces 2–3 lookup calls.

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 the return format (score + matched object) but does not mention whether the operation is read-only, any rate limits, or potential side effects. The description lacks important behavioral context.

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 sentences. It front-loads the core purpose 'Search Trakt' and efficiently conveys the type parameter usage and return format. 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?

Despite high schema coverage, the description fails to explain how to use many optional parameters (page, limit, years, fields, genres, countries, languages) effectively. It does not mention defaults or pagination behavior. For a tool with 9 parameters, the description is incomplete.

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, so baseline is 3. The description adds minimal extra meaning by repeating the type values and mentioning the return format, which is not schema-provided. However, it does not enhance understanding of other parameters like years, genres, or fields.

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 searches Trakt and specifies the type parameter with allowed values. It also mentions the return format (score + matched object). However, it does not differentiate from sibling tools like 'popular' or 'trending', so it lacks explicit distinction.

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 basic usage for the type parameter but provides no guidance on when to use this tool versus alternatives (e.g., 'popular', 'trending'). There are no prerequisite or exclusion criteria mentioned.

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 full burden but only says 'currently-trending'. It does not disclose pagination behavior, data freshness, ordering, or any other behavioral traits beyond the implied trending status.

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 short phrase, which is concise but lacks sufficient structure and detail. It is not wasteful but does not earn its place fully given the available space for more useful 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?

Given the tool has 3 parameters, no output schema, and no annotations, the description is too brief. It does not cover what the return data looks like, edge cases, or any context beyond the basic function.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning to parameters like 'page', 'type', or 'limit' beyond what the schema already 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?

The description clearly states the tool retrieves currently-trending movies or shows, which is a specific verb+resource. However, it does not differentiate from the sibling tool 'popular', which might serve a similar 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?

No guidance is provided on when to use this tool versus alternatives like 'popular' or 'search'. There are no exclusions or usage contexts mentioned.

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 discloses scope (company-financial claims via SEC EDGAR + XBRL), return values (verdict, extracted form, actual value with citation, percent delta), and that it replaces multiple sequential calls. Does not mention rate limits or auth, but those may be assumed.

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 paragraph that efficiently covers purpose, usage, scope, output, and benefits. No wasted words; front-loaded with verbs. Highly concise for the information provided.

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 parameter set and lack of output schema, the description provides sufficient context: scope limitation, output structure, and use case. Minor missing details like error handling or prerequisites, but overall 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?

Only one parameter 'claim' with a schema description that already explains it. The description adds example claims and context about what kind of claims are supported, but does not significantly extend beyond the schema's description. Schema coverage is 100%, so baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool's action (validate, fact-check) and resource (natural-language factual claim). It distinguishes from sibling tools like ask_pipeworx by specifying it's for verifying claims against authoritative sources, and gives example queries.

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 says 'Use when an agent needs to check whether something a user said is true' and provides example usage. It also notes the scope limitation (v1 supports company-financial claims), implying when not to use. Lacks explicit mention of alternatives.

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