Tmdb

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

Annotations already declare readOnlyHint=true, destructiveHint=false, so safeness is clear. Description adds value by detailing return format (per-model + combined), default model (Workers AI Llama), and key requirement for Anthropic. No contradictions with annotations.

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, no wasted words. Front-loaded with purpose, then parameter behavior, then return format. Highly 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 tool without output schema, description fully explains return structure (per-model fields + combined view). All parameters are documented in schema, description adds use-case context. No 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% so description adds beyond schema: explains default model, that apiKey is for Anthropic, and context helps disambiguate. Adds meaningful context not in 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 probes LLMs for knowledge about entities and scores visibility 0-100 per model. It specifies the resource (LLMs), action (probe and score), and context (business/brand/etc.). Distinguishes itself from sibling tools by its unique functionality.

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 usage contexts: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains when to use optional apiKey for Anthropic probing. Lacks explicit 'when not to use' or alternatives, but context is clear enough.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds context about routing to 3,751 tools and returning structured answers with citation URIs, which is helpful but not crucial beyond annotations.

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 front-loaded with the key recommendation, then systematically covers scope and usage patterns. While long, every sentence adds value and 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?

No output schema, but description explains return format (structured answer with citation URIs). Covers broad range of topics and usage. Lacks error handling details, but overall adequate 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 descriptions for all aliases. Description adds examples but no new semantic information 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?

Description clearly identifies the tool as a question-answering tool for factual data from authoritative sources, with specific examples. It contrasts with web search, making its purpose distinct.

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 'PREFER OVER WEB SEARCH' and provides concrete scenarios and example queries, guiding the agent on when to use this tool versus alternatives.

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

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

Beyond annotations (readOnlyHint, idempotentHint), the description details the tool's mechanism: routing, argument filling, fetching data, extracting answer from result, and returning structured output with refusal reasons. No contradictions with annotations.

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 information-dense but slightly long. However, each sentence contributes essential details (purpose, mechanism, usage guidance, return structure). It is front-loaded with the core value proposition.

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 no output schema, the description thoroughly explains the return format (success with fields, refusal with reasons). It covers when to use, trade-offs, and behavioral details, making it fully self-contained 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 all parameters clearly described as aliases for 'question'. The description adds no additional meaning 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 identifies the tool as a 'Hallucination-resistant answer mode' for high-stakes reads. It specifies that it extracts answers using only tool results and distinguishes from sibling ask_pipeworx by noting the same routing but with added extraction and refusal handling.

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: 'whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts.' Provides concrete examples (financial verdicts, legal claims, medical lookups, public statements) and even advises preferring ask_pipeworx for casual lookups due to extra cost.

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

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

The description adds extensive behavioral context beyond the annotations (readOnlyHint, idempotentHint, destructiveHint). It details resolver contract, market-match confidence, parent-event extraction, news fallback mechanisms, safety short-circuits (low_confidence_match, market_closed_or_inactive), spread warnings, and resolution-rule risk. No contradiction with annotations is present.

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

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

The description is very long (exceeding 1000 words) and highly detailed, which may overwhelm agents. However, it is well-structured with uppercase section headers (CLASSIFIERS, FAN-OUT EXAMPLES, etc.) and front-loads the main purpose. It could be more concise without losing critical detail.

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 (multiple classifiers, fan-out examples, response shapes, safety mechanisms) and no output schema, the description is remarkably complete. It covers all relevant aspects: input types, classifiers, fan-out patterns, response structure, resolver contract, parent-event extraction, news fields, safety short-circuits, spread warnings, and resolution-rule risk. No gaps in 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?

With 100% schema description coverage, baseline is 3. The description adds meaning beyond schema by explaining the market parameter accepts slug, URL, or question text, and provides examples. It also clarifies the 'depth' enum (quick vs thorough) and 'include_raw' boolean with practical guidance on 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the input types (slug, URL, question text) and output components (evidence packet, market-vs-model comparison). This distinguishes it from sibling tools like ask_pipeworx, which is a general Q&A 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 lists when to use the tool: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It provides rich context with examples, but does not contain explicit when-not-to-use or direct comparisons to siblings, leaving some ambiguity.

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

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

Annotations already indicate read-only and non-destructive. Description adds behavioral details: results sorted by primary metric, handles off-calendar fiscal years, and includes citation URIs.

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 user-like queries, then details data sources and behavior. Slightly verbose but justified by complexity. No unnecessary 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?

Covers purpose, usage, data sources, and return structure. Sibling tools include entity_profile, making distinction clear. No output schema, but description mentions paired data and citation URIs.

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%, but description enhances parameter understanding: explains what data each type pulls (SEC XBRL vs FAERS) and value format (tickers vs names).

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 specific verbs like 'side-by-side comparison' and lists example queries. It clearly distinguishes itself from single-entity lookups like entity_profile by emphasizing parallel calls for 2-5 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?

Explicitly states 'ALWAYS PREFER over sequential single-pack lookups' and gives example phrases. Does not provide when-not scenarios, but 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing a clear safety and behavior profile. The description adds little beyond mentioning 'Image config + change keys', which doesn't significantly enhance transparency. A score of 3 reflects that annotations carry the burden.

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 very concise (4 words), but it is front-loaded and to the point. However, clarity is sacrificed for brevity, preventing a higher score.

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?

An output schema exists, so return values are documented, but the description fails to explain what 'Image config' or 'change keys' means. For a tool with no parameters, the description should provide more context about what configuration is available and why one would use it. The description is incomplete and leaves the agent guessing.

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?

There are 0 parameters and schema description coverage is 100%. The description doesn't need to add parameter information, so baseline 4 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 'Image config + change keys' is vague and doesn't clearly state what the tool does. 'Configuration' is too broad, and the description fails to distinguish this tool from siblings like discover_tools or other utilities. The purpose is unclear.

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. No context about usage scenarios or exclusions is 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?

Discloses key behaviors beyond annotations: returns verbatim evidence, confidence, source, and a citation per finding; explicitly states it will never invent facts by including a 'gaps' array. Consistent with readOnlyHint and idempotentHint.

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 relatively long but every sentence serves a purpose: defining the tool, listing capabilities, providing usage guidance, and specifying requirements. Front-loaded with the core action, making it efficient despite length.

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 sufficiently explains the structured return format (evidence, confidence, gaps, etc.) and the parallel processing behavior. Covers input details and prerequisites, making it complete for agent 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 coverage is 100%, but the description adds meaningful context by explaining the practical mapping of depth enum values (e.g., 'quick=3') and emphasizes that the question can be broad/multi-part. This adds 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 the tool performs grounded multi-source research by decomposing questions into sub-questions and routing them in parallel to many tools. It distinctly differentiates from sibling tools like 'ask_pipeworx' and 'bet_research'.

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 instructs to use for broad/multi-part questions and to use 'ask_pipeworx' for single lookups. Also mentions account requirements and paid plan for 'thorough' depth, providing clear guidance on when and how to use.

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

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

Annotations already fully declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds 'passes through query params', hinting at open-world parameter acceptance, which is consistent but adds minimal value beyond the annotations.

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

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

The description is extremely concise at one sentence with no waste. However, it could be slightly expanded without compromising conciseness to include key usage 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 the tool's complexity (open-world parameters, no schema properties, sibling tools), the description is too sparse. It lacks explanation of typical use cases, parameter options (despite examples), and return value context. The output schema exists but is not referenced.

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 schemas have no defined properties (coverage 100% but empty) and only provide examples. The description helps by stating 'passes through query params', indicating that parameters are flexible and not strictly defined, but it does not explain the meaning of common parameters 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 states 'Discover movies' which is a clear verb+resource, but it lacks specificity about scope (e.g., filtering by genre, rating) and does not differentiate from sibling tools like search_movie or trending. The parenthetical 'passes through query params' hints at flexibility but is vague.

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 such as search_movie (for title-based search) or trending (for popular movies). There is no mention of exclusions 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?

Annotations already declare readOnlyHint and idempotentHint. The description adds that results include full input schemas with curated examples and are ready to call directly. Missing details on how relevance is computed, but overall adds value.

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

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

The description is a single dense paragraph with all critical information: purpose, use cases, return content, and call order. It is front-loaded and every sentence adds 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?

Despite no output schema, the description explains exactly what is returned (names, descriptions, schemas, examples). It fully covers the tool's behavior and parameters, making it complete for agent use.

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

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

The input schema is fully described (100% coverage). The description adds that the query parameter accepts multiple aliases (task, q, description, search) and specifies limit defaults and max, enhancing schema documentation.

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: finding tools by describing a data or task. It lists many specific domains and indicates it returns top-N relevant tools with full schemas, distinguishing it from sibling tools that are specific lookups.

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 you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available and want to see the option set (not just one answer)', providing clear context for when to use versus alternatives.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds no behavioral traits beyond what annotations provide (no mention of sorting, filtering, or output behavior).

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

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

Extremely concise but at the expense of clarity. One sentence that adds no value beyond the tool name. Underspecification outweighs 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?

Despite zero parameters, rich annotations, and an output schema, the description fails to explain what 'discover' means (e.g., trending, by date, genre). Incomplete for an agent to decide when to invoke this 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?

Input schema has zero parameters with 100% coverage (trivial). Baseline is 4 per guidelines. The description does not add meaning, but no parameter info is needed.

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

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

Description is 'Discover TV.' which is a tautology of the tool name. It fails to specify what kind of TV discovery (e.g., browse, popular, by genre) and does not distinguish from sibling tools like discover_movie or search_tv.

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 discover_movie, search_tv, or tv. The description provides no context for 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?

Annotations already indicate read-only, open-world, idempotent, non-destructive. Description adds valuable context: fans out across multiple sources (SEC, XBRL, USPTO, news, GLEIF), soft-fails for patents, and details return fields. No contradiction.

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 comprehensive but slightly long; however, it is well-structured with examples first and details following. Every sentence 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?

No output schema, but description thoroughly explains what is returned (cik, company_name, recent_filings, fundamentals, patents, news, LEI) without needing further documentation.

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%. Description adds meaning: type is only 'company' today, value must be ticker or CIK, names not supported—important guidance beyond enum/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 explicitly states the tool provides a 'full cross-source profile of a US public company' and lists numerous example queries. It clearly distinguishes from siblings like resolve_entity by specifying that names are not supported.

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 to 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' Also gives clear when-not-to-use: if only have a name, use resolve_entity first.

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

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

Annotations already declare destructiveHint=true; description adds context about clearing data but doesn't exceed annotation value.

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, front-loaded with action, 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?

Simple tool with one param, clear annotations, description covers purpose and usage sufficiently.

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 param with description; description doesn't add extra semantics beyond what schema provides.

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

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

Description specifies 'Delete a previously stored memory by key' – clear verb and noun, distinct from siblings like 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?

Explicit when-to-use: 'stale context, task done, clear sensitive data', and suggests pairing with remember/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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's addition of 'fetches the page, extracts title/description/key links' adds process context but no behavioral cautions beyond what annotations provide.

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 (four sentences) and front-loaded with core purpose, then details and use cases. No redundant or unnecessary 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 no output schema, the description explains the output format ('standard llms.txt markdown format', 'single text blob'), and covers use cases. Tool is simple with two parameters, so completeness is achieved.

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 does not add meaningful detail beyond the schema's parameter descriptions. The schema already explains 'url' and 'max_links' adequately.

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 generates a production-ready llms.txt file for a given URL, explaining the extraction process and output format. It distinguishes itself from siblings like 'ai_visibility_check' and 'scan_competitor_ai_presence' by focusing on creating the specific file.

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 lists three concrete use cases (client indexing, personal project drafting, competitor auditing), providing clear context for when to use the tool. However, it does not explicitly mention when not to use it or offer 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that the tool returns genre IDs, which provides some behavioral context beyond the schema, but does not elaborate on output format, data freshness, or any potential side effects. Given the rich annotations, a 3 is appropriate.

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

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

The description is a single, front-loaded sentence that conveys purpose and usage without any fluff. Every word is justified.

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

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

For a simple tool with one optional parameter and an output schema, the description covers the core functionality and usage. However, the lack of parameter documentation and the absence of information about default behavior or output structure (even though output schema exists) make it 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 has one parameter 'language' with no description (0% schema coverage). The description does not mention the parameter at all, leaving its purpose and format unexplained. This is a significant gap.

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 it is a movie genre directory with examples and IDs, and specifies that it is used to filter discover_movie by genre. It clearly distinguishes from the sibling tool 'genres_tv' by focusing on movies.

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 to filter discover_movie by genre.' This gives clear context and implies that for TV genres, one should use the sibling tool 'genres_tv'.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read operation. The description adds that it returns genre IDs, which is useful but not extensive. No contradictions.

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 that are front-loaded with the core purpose. Every word serves a purpose. Could be slightly improved by including parameter mention, but overall concise and 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?

For a simple list tool with an output schema, the description covers the main intent and usage. However, the lack of parameter documentation is a notable gap, making it less complete. The output schema likely covers return values, so that's adequate.

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

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

The schema has one parameter 'language' with no description and 0% schema description coverage. The tool description does not mention this parameter at all, failing to add any semantic meaning beyond the bare 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 this is a TMDb TV genre directory, lists examples of genres, and explicitly says it provides genre IDs for filtering discover_tv. It effectively distinguishes from the sibling tool genres_movie by specifying 'TV'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells when to use this tool: 'Use to filter discover_tv by genre.' This provides clear context. However, it does not mention when not to use it or explicitly compare with genres_movie, but given the sibling list, it's implied.

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

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds the specific fields returned and the include_inactive parameter behavior, providing additional 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 two sentences: first states purpose and output, second gives usage guidance. Every sentence is necessary and front-loaded.

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

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

The tool is simple with one optional parameter. The description covers purpose, return fields, and usage context, making it fully informative 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 coverage is 100% and the parameter is well-described in the schema. The description does not add further semantics beyond 'active subscriptions', so baseline score 3.

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

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

The description clearly states 'List the caller's active subscriptions', specifying the verb and resource, and lists returned fields, distinguishing it from sibling tools like subscribe/unsubscribe.

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 specific usage scenarios: 'review what you're monitoring before adding more or to find an id to cancel', giving clear context for when to use the tool.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond this. No mention of response behavior or limitations.

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, but it is underspecified rather than concise. It fails to convey essential information, making it ineffective.

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 having an output schema, the description provides no context about the movie details returned, how to use parameters like append_to_response, or how it relates to sibling movie 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 description coverage is 0% and the description does not explain any parameters (language, movie_id, append_to_response). The description adds no 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?

Description 'Movie detail.' is a tautology; it does not specify what the tool retrieves or how it differs from sibling tools like movie_credits or movie_recommendations.

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 discover_movie, movie_credits, or movie_videos. The description lacks context for selection.

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

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds behavioral context about the type of data returned (cast and crew), which is consistent with annotations. No contradictions.

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 plus a usage hint. Every part adds value, and it is front-loaded with the core purpose.

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 an output schema exists, the description doesn't need to detail return values. It covers the main use case well but omits the language parameter. Overall, it's fairly complete for a simple lookup tool with good annotations.

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% (no parameter descriptions in schema). The description mentions movie_id implicitly ('by ID') but does not explain the language parameter. The example helps but a 0% coverage requires more compensation; missing parameter semantics for language reduces score.

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 'full cast + crew for a movie by ID' and lists return fields (actor names, characters, order, department, job). It also gives example queries, making the purpose specific and distinct from siblings like movie_recommendations or movie_videos.

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 usage contexts: 'use for "who played X in Y", "who directed Y", "behind-the-scenes credits"'. It doesn't explicitly state when not to use, but the presence of sibling tools implies alternatives.

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

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

Annotations already cover read-only, safe, idempotent behavior. The description adds no further behavioral context, which is acceptable but does not improve transparency beyond structured data.

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 too brief to be useful; it under-specifies the tool's purpose and parameters. Conciseness should pack meaning, not omit it.

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 having an output schema, the description lacks purpose clarity, usage guidelines, and parameter semantics, making it insufficient for effective tool 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?

The input schema has 0% description coverage and the description offers no explanation of parameters like movie_id, page, or language. Agents have no semantic guidance.

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 'Recommended movies' is a tautology that restates the name without specifying the tool's function. It fails to convey that the tool returns movie recommendations based on a given movie_id.

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 siblings like discover_movie or search_movie. The description lacks any context for selection.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so safety is clear. However, the description adds no behavioral context beyond that, failing to disclose potential rate limits, data scope, or side effects.

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 (two words) but sacrifices essential clarity. While brief, it omits critical action and parameter details, making it under-specified rather than efficiently informative.

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 lacks key information such as the tool's exact operation and parameter usage. Although an output schema exists, the description's brevity leaves major gaps in understanding the tool's role within the sibling set.

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?

With 0% schema description coverage, the description should explain parameter meaning. It does not mention 'movie_id' (required) or 'language' at all, leaving the agent to guess their purpose and format.

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 'Trailers/clips.' is a noun phrase without a verb. It names the resource type but fails to specify the action (retrieve, list, etc.), making the exact purpose ambiguous. It barely provides more than the tool name.

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 offers no guidance on when to use this tool versus alternatives like 'movie' or 'movie_credits'. There are no context cues, exclusions, or prerequisites 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the tool is safe and non-destructive. However, the description does not add behavioral context, such as that it returns person details or that the response is read-only. It relies solely on structured fields.

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 only two words, which is under-specified rather than concise. It lacks front-loaded critical information and does not earn its place as it provides virtually no value beyond the name.

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 three parameters (one required) and an output schema, the description is completely inadequate. It does not explain what the tool returns, how to use append_to_response, or any behavioral details, leaving a significant information gap.

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

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

With 0% schema description coverage, the description must explain parameters but fails to do so. It does not mention person_id, language, or append_to_response, leaving the agent without semantic understanding of input 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 'Person detail.' is a tautology that restates the name 'person' without specifying the action or resource. It does not clearly indicate that this tool retrieves details for a given person_id, making its purpose ambiguous.

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 siblings like search_person or person_combined_credits. There is no mention of prerequisites or excluded contexts, leaving the agent to infer usage from the name alone.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds no behavioral context beyond the domain, so it does not enhance transparency beyond the structured fields.

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 only 5 words, the description is extremely concise but under-specified. It does not provide enough information to earn its place, sacrificing clarity 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 the tool has an output schema and good annotations, the description still lacks essential context like the scope of credits (all? primary?), sorting, or filtering. It is incomplete for an agent to invoke 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 0%. The description does not explain the required 'person_id' or optional 'language' parameter, leaving the agent without meaning beyond the schema 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?

The description 'Film + TV credits' clearly indicates the resource is combined credits, and distinguishes from sibling tools like 'movie_credits' which are likely movie-only. However, it lacks a strong verb like 'retrieves' or 'lists'.

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 'person' or 'movie_credits'. The description offers no context for appropriate selection.

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?

Beyond annotations (all false), the description discloses rate limits (5 per identifier per day), free usage, no quota deduction, and that feedback is read daily and affects roadmap.

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 front-loads purpose, then usage, then constraints. Every sentence adds unique value; no fluff or 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 feedback tool with 3 parameters and no output schema, the description covers what, when, how, and limits. No missing information given the tool's simplicity.

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 3. The description adds value by explaining the 'type' enum options, suggesting concise messages, and encouraging structured context, going beyond schema 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 specifies the action ('tell the Pipeworx team something is broken, missing, or needs to exist') and the resource (feedback to Pipeworx). It clearly distinguishes from sibling tools by framing feedback, not data retrieval.

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 for bugs, features, data gaps, or praise. Provides exclusions ('don't paste the end-user's prompt') and mentions rate limits and quota impact.

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

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

Annotations already indicate read-only, idempotent, open world, non-destructive. The description adds significant transparency: data source (CF analytics-engine), no PII, cached 5min-1h, and the data shape. No contradiction.

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 compact sentences with a parenthetical list. Front-loaded with the main action, every sentence adds value. 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?

Given a single optional parameter and no output schema, the description covers the return shape (top tools, top packs, call volume) and caching. Adequate for usage, though output format is not fully specified.

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 one parameter. The description adds practical semantics beyond the schema's enum description, explaining how shorter vs longer windows affect results. Could provide more detail on default behavior.

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 returns top tools, top packs, and total call volume over a recent window, using specific verbs and resource. It distinguishes from sibling tools by specifying 'Pipeworx' context and focusing on aggregated trending 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 provides three explicit use cases (discovering hot data sources, confirming canonical tool, aligning use case) but does not include explicit when-not-to-use or alternatives. 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?

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral details: it analyzes arbitrage opportunities and suggests trades but includes a fill check to avoid trading on non-realizable edges. It covers placeholders, low similarity, and partition rejection, providing full 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 long but every sentence is informative. It is well-structured: required condition first, then mode explanations, then technical details. Front-loading the requirement ensures the agent immediately knows the constraint. A slightly tighter structure could improve conciseness, but it is well-organized.

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 lacking an output schema, the description thoroughly explains the response structure (opportunities array, partition_check, fill check). It covers edge cases, flags, and interactions with sibling tools. For a complex tool, the description is remarkably complete and leaves no ambiguity about behavior or output.

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% (both parameters described in schema). The description adds extensive meaning beyond the schema: it explains the difference between event and topic modes with examples, the required format for event slugs, and the semantics of cross-event topic search. This greatly aids correct 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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes two modes (event and topic) and explicitly differentiates from sibling tools like polymarket_fill_risk, ensuring the agent knows exactly what this tool does.

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 opens with a required condition ('REQUIRES one of `event` ... or `topic` ... call with no args fails'), provides recommendations for each mode, warns about Jaccard similarity, partition filter, and fill check, and even advises when not to trade. It explicitly references sibling tools for further 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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint true, destructiveHint false. The description adds rich behavioral context: caching (1h), response structure (by_segment, _diagnostics), model family details (crypto_price, news_momentum, partition_overround, concentrated_longshot), and edge computation methodology. It goes far beyond annotations to explain underlying mechanisms and safety (no destructive actions).

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 core purpose in the first sentence, then organizes details into segments (model families, response, knobs). However, it is quite long (over 300 words) and includes some implementation details (e.g., specific numbers like 'Run 8 from prior 85%/5%/50:1') that may not be essential for tool selection. Still, the structure is logical and well-paragraphed.

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 9 parameters (none required), no output schema, and high complexity (multiple model families, segments, diagnostics), the description is remarkably complete. It explains the full response structure (by_segment, fed_candidates, _diagnostics), the content of each opportunity (edge_pp_net, kelly_fraction, etc.), caching behavior, and even tradeable-edge filters. No critical gaps remain.

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 value by explaining the purpose of knobs (tradeable-edge filters) in context, e.g., min_liquidity and max_spread_pp, and how they relate to realizing edges. It also explains the min_partition_leg_kelly parameter's special role for partition arbs. This exceeds simple schema 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 clearly states it scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, specifically targeting 'what should I bet on today'. It distinguishes from sibling tools by focusing on edge computation from Pipeworx data, and explicitly contrasts with similar tools like polymarket_arbitrage and bet_research through context.

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 extensive usage guidance, including tradeable-edge knobs (min_liquidity, max_spread_pp) and filtering options (min_kelly, min_edge_pp). It explains when to use the tool (discovery) but does not explicitly state when NOT to use it or compare directly with siblings like polymarket_arbitrage. However, the detailed parameter descriptions compensate.

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 indicate read-only, idempotent, non-destructive operations. The description adds substantial behavioral detail: data sources, response structure (tracked, expired, snapshot_dates), trend computation, limitations (60-day TTL, daily closes, not intraday), and decay/trend mechanics. This goes well beyond the annotations.

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 detailed but well-structured: purpose, usage example, parameter overview, response format (with bullet-like enumeration), and limitations. It is not overly verbose for the complexity, though some sentences could be tightened. Earns its length through high information density.

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

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

With no output schema, the description fully explains the response structure (tracked[], expired[], snapshot_dates[]) and details within each. It also covers limits (60-day TTL, data gaps, decay calculation). All essential information for correct use is present, making it contextually complete.

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

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

Schema coverage is 100% (both parameters described). The description adds value by explaining 'window' as 'snapshot family' and providing defaults and bounds for 'days' (default 14, max 30). While schema already covers basics, the description enriches understanding with contextual 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: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question about edge duration and shrinkage, distinguishing it from related tools like polymarket_edges (current edges) and polymarket_arbitrage.

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 implicitly guides usage by contrasting fresh vs. old edges ('a fresh wide edge and a 3-week-old wide edge are different trades'). It does not explicitly name alternatives but the context is clear. A more explicit 'when to use versus other tools' would raise this to 5.

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 indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description aligns and adds significant behavioral detail: it walks the order book, returns verdicts (clean|degraded|cannot_fill), and explains risks of partial basket fills causing unhedged positions. No contradiction.

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 longer than necessary (~200 words), but front-loads the purpose and requirement. It includes detailed return field lists which would be better in an output schema, but since none exists, it is acceptable. Some redundancy in explaining modes and defaults.

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

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

Given the high complexity (two modes, multiple return fields, edge cases), the description covers all necessary aspects: both modes, parameter defaults, return values, and critical warnings about thin books and partial fills. Without an output schema, this description is complete enough for an agent to understand and use the tool 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?

Schema coverage is 100% with descriptions for all 4 parameters. The tool description reinforces parameter meaning and adds context (e.g., side defaults, size interpretation for basket mode). It adds value beyond the schema by explaining return fields that depend on parameters.

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: a realizable-vs-theoretical edge check against CLOB order-book depth. It distinguishes between single-market and basket modes and explicitly differentiates itself from sibling tools like polymarket_arbitrage and polymarket_edges, stating when it should be used before those.

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 this tool: before acting on polymarket_arbitrage signals or polymarket_edges trades above $500. It explains the rationale (theoretical overround not capturable, partial fills cause directional risk). It does not explicitly state when not to use it, 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?

Annotations already mark the tool as read-only and idempotent. The description adds significant behavioral context: safety fields (compatibility_warning, temporal_alignment, skipped counters) and conditions when spreads are meaningless. No contradictions with annotations.

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 with clear sections (modes, response, safety fields) and front-loaded purpose. It is slightly verbose due to detailed safety explanations, but every sentence adds necessary context for this complex tool.

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 (cross-venue matching, compatibility checks) and lack of output schema, the description thoroughly explains the response fields (leg prices, spreads, compatibility_warning, temporal_alignment, skipped counters). It leaves minimal ambiguity for an AI 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 coverage is 100% with descriptions for all three parameters. The description adds value by explaining the topic enum values and overriding behavior, going beyond the schema's brief descriptions. This helps the agent understand parameter relationships.

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 computes the spread between Kalshi and Polymarket for the same resolving question, specifying verb, resources, and scope. It distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on cross-venue 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?

The description provides explicit usage guidance, including two modes (topic shortcuts and explicit tickers) and warnings about compatibility and temporal alignment. It warns that pre-mapped topics often return warnings, advising caution. However, it does not explicitly compare to sibling tools for when to use this instead.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds further value by noting scoping (anonymous IP, BYO key hash, account ID) and the effect of omitting the key argument (lists all keys). No contradictions.

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, front-loaded with the primary action, no unnecessary words. All information is pertinent and efficiently presented.

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

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

For a simple single-parameter retrieval tool, the description covers purpose, usage, behavioral traits, and parameter semantics fully. Even without an output schema, the behavior is well explained.

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 description for the 'key' parameter. The description adds context about the parameter's role (omit to list all keys) and its relationship to the 'remember' tool, going 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 tool retrieves values saved via 'remember' or lists all keys, with specific verb-resource pairs (retrieve/list, value/keys). It distinguishes from sibling tools like 'remember' and 'forget' by naming them explicitly.

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 clear context for when to use: to look up previously stored context without re-deriving. It mentions pairing with 'remember' and 'forget', but does not explicitly state when to avoid this tool (e.g., when data is not memory-based).

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, etc. The description adds useful behavioral details: returns most recent alerts with source, citation_uri, raw payload; explains mark_read:true flags events as read so next call shows only newer ones. No contradictions with annotations.

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 sized and front-loaded with the main purpose. Every sentence adds value: purpose, return fields, filter options, mark_read behavior, poll compatibility, and alternative access. 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 tool with 5 parameters, no required, 100% schema coverage, and no output schema, the description is thorough. It explains return fields, filtering, mark_read, poll suitability, and alternative API. The annotations cover safety and idempotency, making the description sufficient for an AI 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 coverage is 100% with descriptions for all 5 parameters. The description adds context beyond schema: e.g., type filter example 'sec_8k', mark_read behavior detail, limit range 1-200. This adds meaningful value, so score above baseline of 3.

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

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

The description clearly states the tool's purpose: 'Pull fired events from your subscription feed.' It specifies the verb 'Pull' and the resource 'fired events from your subscription feed'. It distinguishes from sibling tools like list_subscriptions by focusing on retrieving alerts, not managing subscriptions.

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 mentions 'Polls work fine' and provides an alternative access method (GET registry.pipeworx.io/alerts.json), implying when to use this tool vs. scripts/dashboards. It also explains the mark_read parameter's effect on subsequent calls. However, it does not explicitly state when not to use this tool.

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

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

Annotations already indicate read-only, idempotent, open-world. Description adds: specific sources and their status (USPTO soft-fail), fallback logic (GDELT→GNews), output structure (grouped changes, citations). No contradiction with annotations.

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?

Every sentence adds value, but the description is dense and could benefit from better structuring (e.g., bullet points). It is front-loaded with natural language examples.

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 complexity (3 parameters, no output schema), the description is thorough: covers sources, fallback, output structure, and usage. No missing critical information for an agent to use the tool 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?

All parameters are described in schema (100% coverage). Description enriches: example values for `since`, recommendation for typical monitoring, and explanation that `value` accepts ticker or CIK. Only 'company' supported for type.

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 explains the tool returns a change feed for a company from multiple sources, with natural language examples. It distinguishes from sibling 'entity_profile' by specifying that this is for dynamic changes.

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 use cases and example queries. Directly contrasts with entity_profile for static profiles, and explains fallback behavior for news sources. Advises on `since` parameter values.

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 by identifier, persistence differences for authenticated vs anonymous sessions (24 hours), and idempotent nature. Annotations confirm non-destructive and idempotent; no contradiction.

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?

Four sentences, each earning its place: core purpose, when to use, scoping/persistence, and related tools. Front-loaded with action verb 'Save'.

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?

Fully explains behavior for a simple key-value store: scoping, persistence, pairing with recall/forget. No output schema needed as return value is implicit. Given parameters and annotations, description is 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 provides full descriptions for 'key' and 'value' (100% coverage). Description adds value with concrete examples and context for key naming conventions, but does not add entirely new semantic information 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?

Description clearly states the tool saves data for reuse across conversations/sessions, gives specific examples (resolved ticker, target address, user preference, research subject), and distinguishes from siblings 'recall' and 'forget'.

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 you discover something worth carrying forward'), why ('so you don't have to look it up again'), and how to pair with 'recall' and 'forget'.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context: it reveals that each call cascades through multiple lookup endpoints, replaces 2-3 manual lookups, and details the exact return structure for each entity type (ticker, CIK, URI for companies; RxCUI, ingredient, brand for drugs). No contradiction with annotations.

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 dense but well-structured: beginning with example queries, then a usage directive, followed by detailed type breakdowns. Every sentence adds value, though it is slightly lengthy. It front-loads the purpose and usage, making it easy for an agent to parse quickly.

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

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

Given the tool's moderate complexity (two entity types, multiple input formats, rich returns) and no output schema, the description is remarkably complete. It covers all parameter semantics, return values, and behavioral nuances (cascading lookups). The 100% schema coverage and detailed description leave no significant 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 both parameters described. The description enriches the schema by explaining that for company, the value can be a ticker, CIK, or company name with auto-disambiguation, and for drug, it accepts brand or generic name. It also clarifies what each type returns, adding meaning beyond the enum and string 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 explicitly states that the tool resolves user-spoken names to canonical identifiers, with concrete examples like 'What's the ticker for…'. It distinguishes itself from sibling tools by specifying it is used when an ID is needed from a name, and details supported types (company and drug) and their returns.

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 advises 'Use FIRST whenever you have a name but need an ID', providing clear usage context. While it does not explicitly list alternative tools, the sibling set includes entity_profile and compare_entities, implying the scope. The instruction is clear enough for an AI to decide when to invoke.

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

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

The description discloses internal behavior: probes each entity with 'ai_visibility_check', ranks by score, and treats the first entity as the subject. This adds context beyond annotations (readOnlyHint, etc.), which are already non-destructive and idempotent.

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, front-loaded with core purpose, followed by mechanism and use case. No redundant words; every sentence contributes uniquely.

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 no output schema, the description specifies return fields: 'ranked list with score, confidence, signal density per entity'. Combined with internal mechanism, this fully informs the agent of expected output. No missing elements.

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?

With 100% schema coverage, the description still adds significant meaning: 'entities' specifies 2-8 range and first-as-subject; 'models' distinguishes free vs. paid; '_apiKey' conditional on model; 'context' adds disambiguation purpose. These details are absent from 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 starts with 'Compare AI visibility across multiple entities side-by-side,' clearly stating the verb and resource. It contrasts with sibling 'ai_visibility_check' by explaining it batch-probes multiple entities and ranks them, making the tool's purpose unique and unambiguous.

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 a concrete use case: 'competitive AI-marketing audits' with an example query. It implicitly excludes single-entity checks by referencing 'ai_visibility_check' for individual probes, but lacks an explicit 'when not to use' statement.

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 important behavioral traits beyond annotations: bundlephobia's first measurement can take 5-30 seconds, partial failures degrade gracefully with sources_failed reporting. Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds timing and error-handling context without contradiction.

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 core purpose in the first sentence. While relatively long, every sentence adds value (ecosystem scope, return fields, timing caveat). Minor redundancy could be trimmed, but overall structure is effective.

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

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

Despite lacking an output schema, the description thoroughly explains return values: summary block fields (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and recent versions. Covers edge cases like partial failures and timeout handling, making it complete 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 coverage is 100% with both parameters described. The description adds extra meaning: notes that scoped packages (e.g., '@types/node') are accepted for 'package', and explains that 'version' defaults to the latest published version when omitted, enhancing understanding 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 it performs a composite check for npm packages across deps.dev and bundlephobia, answering safety, popularity, and size questions. It uniquely identifies itself among siblings, which include unrelated tools like movies and AI visibility checks.

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 provides usage context: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me".' Also clarifies NPM-only scope in v1 and directs to deps.dev:version for other ecosystems, effectively guiding when to use alternatives.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. However, the description adds no behavioral details (e.g., results pagination, sorting, or quotas). Minimal value beyond annotations.

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 (two words) but this is under-specification rather than efficiency. The description is too short to be informative, failing to earn its place with any useful 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?

Given the complexity (7 parameters, no param descriptions, output schema exists), the description is drastically incomplete. It does not explain what the tool returns or how to use it 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 0%. The description provides no information about any of the 7 parameters, including the required 'query'. The input schema has examples, but the description fails to add semantic meaning or usage details.

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 'Movie search.' adds no specificity beyond the tool name. It is essentially a tautology, as 'search_movie' already implies movie search. No distinction from siblings like search_person or search_tv.

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. The description lacks context for usage, such as when to prefer search_movie over search_multi or discover_movie.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds no extra behavioral context, which is acceptable but not improved upon.

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 brief (2 words) but lacks substance; it is under-specified rather than efficiently informative.

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 having an output schema and multiple sibling tools, the description provides no details on search scope, result format, or parameter effects, making it 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?

Schema description coverage is 0% and description fails to explain any parameters (page, query, language, include_adult), leaving the agent to infer from 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?

Description is 'Multi-type search.' which is vague; it doesn't specify what types are searched or how it differs from siblings like search_movie or search_person.

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 the many sibling search tools; description does not provide any context for selection.

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

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the description adds little behavioral context. It does not mention pagination, result limits, or other behaviors beyond annotations, but does not contradict them.

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 (3 words), but this represents under-specification rather than efficient conciseness. Important information about the tool's purpose and parameters is omitted, and there is no front-loading of 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?

Despite having an output schema, the description does not explain what the search returns (e.g., list of people, IDs, popularity scores). For a tool with 4 parameters and potential pagination, the description is insufficiently 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 description coverage is 0%, yet the description provides no explanations for parameters like 'language', 'include_adult', or 'page'. The description fails to compensate for the lack of schema descriptions, leaving agents without guidance on 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 'Person search.' is a tautology that restates the tool name without providing specifics. It does not clarify the scope (e.g., search by name, partial match) or distinguish from sibling tools like search_movie or search_multi.

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 compared to alternatives such as person, person_combined_credits, or compare_entities. Lacks context on prerequisites, limitations, or preferred use cases.

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

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

Annotations already indicate a safe, idempotent read operation (readOnlyHint=true, destructiveHint=false). The description adds value by detailing the return fields (show name, first-air date, popularity, overview, poster path, tv_id) and connecting it to TMDb, exceeding what annotations alone provide.

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

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

The description is two sentences, each serving a distinct purpose: first explains the tool and its outputs, second gives usage guidance. No fluff, well-structured, and front-loaded with essential 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?

The description adequately covers the tool's high-level purpose and return values, aided by an existing output schema. However, the lack of parameter documentation beyond 'query' leaves the agent underinformed about optional inputs, making it less complete for a 6-parameter 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?

With 0% schema description coverage, the description heavily relies on itself to explain parameters. It only mentions 'by title', mapping to the required 'query' parameter, but fails to describe 'page', 'year', 'language', 'include_adult', or 'first_air_date_year'. This leaves significant ambiguity for the agent.

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 searches TV shows by title, lists return fields, and explains its purpose as resolving a TV title into tv_id for use with tv, tv_season, and tv_episode tools. It effectively distinguishes from sibling tools like search_movie or search_person.

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 this tool: to resolve a TV title into tv_id needed by specific sibling tools. It does not provide explicit when-not or alternative guidance, but the context is clear enough for an agent to decide.

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 technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, and a 200K char cap with truncation and flagging. Annotations only indicate read-only and idempotent, so description adds significant 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?

Single paragraph is dense but well-structured: purpose first, then usage, then technical details. Every sentence adds value. Front-loaded with key 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 no output schema, description explains returns: top-N passages with offsets and scores. Also covers limitations (200K cap), pairing with another tool, and underlying algorithm. Complete for intended use case.

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 detailed descriptions for each parameter. The description adds minimal extra meaning: examples for query and states default for limit. Baseline 3 is appropriate as description does not significantly enhance parameter understanding 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 it performs semantic search inside a fetched record, specifying the inputs and outputs. It distinguishes itself from siblings by mentioning pairing with ask_pipeworx_grounded and explicitly stating its use case when the record is too large for the prompt.

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 guidance on when to use: when the record is too big to cram into the prompt. Also explains how it pairs with ask_pipeworx_grounded for a complete workflow. No need to list exclusions as the purpose is narrow.

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

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

The description discloses rich behavioral traits beyond the annotations: it returns a new subscription id, specifies authentication requirements, lists subscription types with examples, details delivery channels with constraints (SMS cap, webhook auto-disable), and notes that the signing secret is returned once. It does not contradict the annotations.

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 the core purpose. However, it is somewhat lengthy, including detailed technical explanations (e.g., webhook signing) that could be succinct. Still, every sentence adds necessary context, and the structure supports 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 absence of an output schema, the description adequately explains that it returns a subscription id. It covers key aspects like required auth, type constraints, and delivery details. However, it does not explicitly mention the structure of the created subscription or error handling, leaving minor gaps for a complex 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?

With 100% schema coverage, the baseline is 3. The description adds significant value by providing concrete examples for the 'params' parameter (e.g., 'sec_8k: {ticker:"AAPL", items?:["5.02"]}') and detailing the 'delivery' parameter's behavior (webhook HMAC signing, SMS verification). This clarifies usage beyond the schema's property 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 clearly states the tool's purpose: 'Create a proactive monitoring subscription to a live-data event stream.' It uses a specific verb 'create' and identifies the resource 'subscription', distinguishing it from sibling tools like 'unsubscribe' and 'list_subscriptions'.

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 clear context on when to use the tool, including the requirement for a Pipeworx OAuth account and the inability for anonymous users. It implicitly differentiates from siblings like 'recent_alerts' for pulling events, but does not explicitly state alternatives 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?

Annotations already indicate readOnly, openWorld, and idempotent. The description adds beyond: explains the output structure (category-bucketed examples), that it draws from a live catalog of tools, and that it teaches how to call meta-tools. Good added 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?

Front-loaded with common user queries, then states purpose and details. Every sentence adds value. Slightly lengthy but well-organized, earning a 4.

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 explains the return type sufficiently. It covers use case, parameters, and expected output. Minor lack of exact JSON structure but adequate for agent understanding.

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 the topic parameter with a description listing values. The description adds meaning by explaining the effect: omission gives full spread, passing a topic focuses the response. This enriches the agent's understanding 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 tool returns category-bucketed example questions with tool+argument shapes, serving as an onboarding entry point. It distinguishes from siblings like ask_pipeworx and discover_tools by being for initial exploration when the agent doesn't know Pipeworx's capabilities.

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 this FIRST when you do not yet know what Pipeworx can do for you' and explains how to call with or without a topic. Lacks explicit when-not-to-use, but the strong recommendation to use first provides clear 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?

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. Description adds data source (TMDb) and return fields (ranked list with name, popularity, etc.), providing useful behavioral context beyond annotations.

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, no filler. The first sentence states purpose and scope, the second gives concrete use cases. 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?

Covers core functionality and return data, but fails to mention the pagination parameter (page) or language filtering. With an output schema present, the lack of return structure details is acceptable, but missing optional parameter context makes it 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?

Schema coverage is 50% with only media_type and time_window described. The description reiterates these but adds no detail about page or language parameters. Usage examples show valid values but do not compensate for the missing documentation on optional parameters.

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 returns trending movies, TV shows, or people for a time window, and lists returned fields. However, it does not explicitly differentiate from the sibling tool 'pipeworx_trending', which might require additional context.

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 usage scenarios like 'what is popular this week' and 'trending celebrities', giving clear context. Does not include when-not-to-use or alternatives, but the provided use cases are sufficient for most agents.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety traits. The description adds no behavioral context beyond annotations, such as rate limits, response size, or data freshness. It does not contradict annotations.

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 (three words) but lacks structure and completeness. It is not front-loaded with key information; it is a fragment that fails to communicate the tool's action clearly. Brevity here comes at the cost of utility.

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 three parameters and an output schema, the description is severely lacking. It does not explain what 'TV show detail' entails, how to specify the TV show, or what to expect in the response. The existence of an output schema does not excuse the lack of 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?

Schema description coverage is 0%, so the description must compensate. It does not mention any parameters (tv_id, language, append_to_response) or explain their purpose, format, or effects. The examples in the schema are not referenced or expanded upon.

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 'TV show detail' vaguely indicates the tool returns details about a TV show, but lacks a specific verb (e.g., 'get', 'retrieve') and does not distinguish the tool from siblings like search_tv, discover_tv, tv_episode, or tv_season. The name and title are nearly identical, making it close to a tautology.

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 like search_tv (for finding TV shows) or tv_episode (for episode details). There are no examples, prerequisites, 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 already declare readOnlyHint, openWorldHint, idempotentHint as true and destructiveHint as false. The description adds no behavioral context but does not contradict annotations.

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 brief but lacks substance. It is under-specified rather than concise, failing to provide 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 4 parameters with no schema descriptions and no usage guidance, the description is incomplete despite the presence of an output schema.

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 the description does not explain the parameters. While parameter names are self-explanatory, the description fails to compensate for the lack of schema 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 'Episode detail.' is a tautology of the tool's name and title. It does not specify what kind of detail is provided, making it vague and minimally informative.

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 given on when to use this tool versus alternatives like 'tv_season' or 'search_tv'. The description provides no context for selection.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint (safe, read-only operation). The description 'Season detail.' adds no behavioral context beyond what annotations provide, such as data freshness, error behavior, 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?

The description is extremely short (one phrase), but this is under-specification rather than effective conciseness. It does not provide enough information to be helpful, and the sentence does not earn its place by adding 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?

Despite having an output schema, the tool has 3 parameters with no description, and the description does not address input expectations or usage context. The minimal description leaves significant gaps for correct invocation, even for a simple 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 description coverage is 0%, meaning the schema itself provides no parameter descriptions. The description also fails to explain any parameter (tv_id, season_number, language). With a high schema coverage gap, the description must compensate, but it does not.

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 'Season detail.' which is a tautology—it restates the name and title. It lacks a specific verb (e.g., 'get' or 'retrieve') and does not clearly indicate the resource beyond the name. This provides minimal useful differentiation from siblings like 'tv' or 'tv_episode'.

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. Sibling tools include 'tv' for series details and 'tv_episode' for episode details, but the description offers no context for selecting this tool.

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

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

Adds value beyond annotations: ownership enforcement, soft-delete behavior, and historical event preservation. Annotations indicate non-destructive, idempotent, and non-read-only, which is consistent.

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, each providing essential information: action, constraint, and side effects. 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?

Covers all necessary aspects for a simple one-parameter tool: what it does, who can use it, what happens to the data, and where to find historical events.

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 parameter description; description adds context that 'id' comes from 'subscribe', linking tools.

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 'Cancel a subscription by id.' Distinguishes from sibling tools 'subscribe' and 'list_subscriptions' by specifying the action and ownership constraints.

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 ownership enforcement ('only cancel your own subscriptions') and explains deactivation vs deletion, linking to 'recent_alerts' for historical data. Does not explicitly list when not to use, but provides sufficient 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?

Annotations already indicate readOnly, idempotent, non-destructive. Description adds behavioral context: returns verdict type, structured form, actual value with citation and percent delta, and replaces multiple calls. Does not contradict annotations.

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 informative but slightly lengthy; front-loaded with example queries. Every sentence adds value, though 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?

Fully explains what claims are supported, sources (SEC EDGAR+XBRL), return structure (verdict, structured form, actual value with citation, percent delta). No output schema exists, so description adequately covers completeness.

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 schema coverage 100%. Description adds value by providing example inputs and clarifying supported claim types (company-financial), beyond the schema 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?

Description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It specifies the domain (company-financial claims via SEC EDGAR+XBRL) and distinguishes from siblings by noting it replaces multiple sequential 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 says 'Use whenever the agent needs to check whether something a user said is factually correct.' Provides context for when to use, but no explicit when-not or exclusion criteria, though domain limitation is implied.

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